Project3_Part2_Web APIs & Classification-EDA Modelling.ipynb

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    "<img src=\"http://imgur.com/1ZcRyrc.png\" style=\"float: left; margin: 20px; height: 55px\">\n",
    "\n",
    "# Project 3: Web APIs & Classification - Part 2\n",
    "\n",
    "EDA Modelling and Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Problem-Statement\" data-toc-modified-id=\"Problem-Statement-1\">Problem Statement</a></span></li><li><span><a href=\"#Part-2:-EDA,-Modelling-&amp;-Conclusion\" data-toc-modified-id=\"Part-2:-EDA,-Modelling-&amp;-Conclusion-2\">Part 2: EDA, Modelling &amp; Conclusion</a></span></li><li><span><a href=\"#NLP\" data-toc-modified-id=\"NLP-3\">NLP</a></span><ul class=\"toc-item\"><li><span><a href=\"#Cleaning-Data-(removing-irrelavant-characters,-cleaning-data,-removing-stopwords)\" data-toc-modified-id=\"Cleaning-Data-(removing-irrelavant-characters,-cleaning-data,-removing-stopwords)-3.1\">Cleaning Data (removing irrelavant characters, cleaning data, removing stopwords)</a></span></li><li><span><a href=\"#EDA\" data-toc-modified-id=\"EDA-3.2\">EDA</a></span><ul class=\"toc-item\"><li><span><a href=\"#WordCloud\" data-toc-modified-id=\"WordCloud-3.2.1\">WordCloud</a></span></li><li><span><a href=\"#Average-Length-of-each-post\" data-toc-modified-id=\"Average-Length-of-each-post-3.2.2\">Average Length of each post</a></span></li><li><span><a href=\"#Common-words-for-each-category\" data-toc-modified-id=\"Common-words-for-each-category-3.2.3\">Common words for each category</a></span></li></ul></li></ul></li><li><span><a href=\"#Model-Prep\" data-toc-modified-id=\"Model-Prep-4\">Model Prep</a></span><ul class=\"toc-item\"><li><span><a href=\"#Labelling\" data-toc-modified-id=\"Labelling-4.1\">Labelling</a></span></li><li><span><a href=\"#Train-Test-Split\" data-toc-modified-id=\"Train-Test-Split-4.2\">Train Test Split</a></span></li></ul></li><li><span><a href=\"#Predictive-Models\" data-toc-modified-id=\"Predictive-Models-5\">Predictive Models</a></span><ul class=\"toc-item\"><li><span><a href=\"#Models-with-Hyperparameter-tuning-and-Vectorizers\" data-toc-modified-id=\"Models-with-Hyperparameter-tuning-and-Vectorizers-5.1\">Models with Hyperparameter tuning and Vectorizers</a></span></li><li><span><a href=\"#Hyperparameters\" data-toc-modified-id=\"Hyperparameters-5.2\">Hyperparameters</a></span></li><li><span><a href=\"#Performance-Metrics\" data-toc-modified-id=\"Performance-Metrics-5.3\">Performance Metrics</a></span></li><li><span><a href=\"#User-Defined-Functions\" data-toc-modified-id=\"User-Defined-Functions-5.4\">User Defined Functions</a></span></li><li><span><a href=\"#Global-inputs-for-GridSearchCV\" data-toc-modified-id=\"Global-inputs-for-GridSearchCV-5.5\">Global inputs for GridSearchCV</a></span></li></ul></li><li><span><a href=\"#Model-Selection\" data-toc-modified-id=\"Model-Selection-6\">Model Selection</a></span><ul class=\"toc-item\"><li><span><a href=\"#Baseline-accuracy\" data-toc-modified-id=\"Baseline-accuracy-6.1\">Baseline accuracy</a></span></li><li><span><a href=\"#Logistic-Regression\" data-toc-modified-id=\"Logistic-Regression-6.2\">Logistic Regression</a></span></li><li><span><a href=\"#Multinomial-Naive-Bayes\" data-toc-modified-id=\"Multinomial-Naive-Bayes-6.3\">Multinomial Naive Bayes</a></span></li><li><span><a href=\"#KNN\" data-toc-modified-id=\"KNN-6.4\">KNN</a></span></li><li><span><a href=\"#Support-Vector-Machines\" data-toc-modified-id=\"Support-Vector-Machines-6.5\">Support Vector Machines</a></span></li></ul></li><li><span><a href=\"#Summary-of-Performance-of-Models:\" data-toc-modified-id=\"Summary-of-Performance-of-Models:-7\">Summary of Performance of Models:</a></span></li><li><span><a href=\"#Observations\" data-toc-modified-id=\"Observations-8\">Observations</a></span><ul class=\"toc-item\"><li><span><a href=\"#Top-Predictors\" data-toc-modified-id=\"Top-Predictors-8.1\">Top Predictors</a></span></li><li><span><a href=\"#Misclassified-data\" data-toc-modified-id=\"Misclassified-data-8.2\">Misclassified data</a></span></li></ul></li><li><span><a href=\"#Conclusion\" data-toc-modified-id=\"Conclusion-9\">Conclusion</a></span></li></ul></div>"
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   "source": [
    "## Problem Statement\n",
    "\n",
    "As a member of the Data Science Team in All Wellness online platform, we are tasked to use NLP to reduce the time and efforts required to classify members' online queries into fitness related or diet related, which will then be  channeled to the panel of certified fitness coach or nutritionist. \n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Part 2: EDA, Modelling & Conclusion\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 472,
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   "source": [
    "#import libraries\n",
    "\n",
    "#standard imports\n",
    "import pandas as pd\n",
    "#so that pandas do not truncate the rows\n",
    "pd.set_option('max_columns', 100) \n",
    "#Set datafrome display format\n",
    "pd.options.display.float_format = \"{:,.3f}\".format\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "#graph imports\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "#define the style of sns/plt\n",
    "\n",
    "#API imports\n",
    "import requests\n",
    "import time\n",
    "import random\n",
    "\n",
    "from bs4 import BeautifulSoup\n",
    "\n",
    "#Regex\n",
    "import regex as re\n",
    "\n",
    "#Lemmatizing and Stopwords\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "from nltk.tokenize import RegexpTokenizer\n",
    "from nltk.stem.porter import PorterStemmer\n",
    "\n",
    "#Modelling\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "#==KNN\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "#==== Classification matrix\n",
    "from sklearn.metrics import f1_score, classification_report, accuracy_score, matthews_corrcoef\n",
    "from sklearn.metrics import plot_confusion_matrix, confusion_matrix, ConfusionMatrixDisplay\n",
    "from sklearn.metrics import plot_roc_curve, auc, roc_auc_score, RocCurveDisplay\n",
    "\n",
    "#=== Naive Bayes\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "#=== CountVectorizer and TFIDFVectorizer from feature_extraction.text.\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "#=== SVC\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "#word cloud\n",
    "from os import path\n",
    "from PIL import Image\n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator"
   ]
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   "cell_type": "markdown",
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   "source": [
    "----"
   ]
  },
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   "source": [
    "## NLP\n",
    "\n",
    "<font color = \"blue\"> \n",
    "\n",
    "**Part 2** of the project focuses on **Natural Language Processing** and converting standard text data (like Titles and Comments) into a format that allows us to analyze it and use it in modeling. </font>"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "df_combine = pd.read_csv('datasets/combine.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning Data (removing irrelavant characters, cleaning data, removing stopwords)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 261,
   "metadata": {},
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   "source": [
    "def cleanwords(data, do_all=True):\n",
    "    \"\"\"\n",
    "    Function will remove all irrevant characters(or digits), HTML tags, stopwords), \n",
    "    then perform stemming to get the root word. \n",
    "    Takes in arguement:\n",
    "    data- str the raw text to be converted\n",
    "    \n",
    "    Return the list of cleaned and stemmed words\n",
    "    \"\"\"    \n",
    "    # 1. Remove HTML.\n",
    "    review_text = BeautifulSoup(data).get_text()\n",
    "    \n",
    "    # 2. Remove non-letters.\n",
    "    letters_only = re.sub(\"[^a-zA-Z]\", \" \", review_text)\n",
    "    \n",
    "    # 3. Convert to lower case, split into individual words.\n",
    "    words = letters_only.lower().split()\n",
    "    \n",
    "    # 4.remove stop words, including title of the subreddits\n",
    "    stops = set(stopwords.words('english'))\n",
    "    stops.update(['diet', 'diets', 'dieting', 'dieter', 'dieters', 'dietary', 'dieted', 'workout', 'workouts', 'working', 'working out'])\n",
    "    meaningful_words = [w for w in words if w not in stops]\n",
    "    \n",
    "    #5 Stemming\n",
    "    p_stemmer = PorterStemmer()\n",
    "    meaningful_words_stemmed = [p_stemmer.stem(w) for w in meaningful_words]\n",
    "    \n",
    "    #6 return the list of stemmed and cleaned words\n",
    "    return(meaningful_words_stemmed)"
   ]
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   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [],
   "source": [
    "#clean the posts\n",
    "df_combine['wordslist'] = df_combine['post'].apply(cleanwords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [],
   "source": [
    "#have a column of all the stemmed words\n",
    "df_combine['stemmed_post'] = df_combine['wordslist'].str.join(\" \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Separated the original post into list of actual words and stemmed words\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### EDA\n",
    "<font color = \"blue\"> \n",
    "\n",
    "\n",
    "\n",
    "- Analyse the features of the data\n",
    "- Create a Wordcloud to see the frequently occuring word. This will indicate how distinct the 2 subreddits are\n",
    "- Look at the average length of each post\n",
    "- Look at the common words to both documents</font>"
   ]
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      "C:\\Users\\Rachel\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>subreddit</th>\n",
       "      <th>diet</th>\n",
       "      <th>workout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">post</th>\n",
       "      <th>count</th>\n",
       "      <td>919</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>919</td>\n",
       "      <td>914</td>\n",
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       "      <th>top</th>\n",
       "      <td>Turmeric and how to use it Does putting a pinc...</td>\n",
       "      <td>Dont feel the burn Ive been working out at the...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">wordslist</th>\n",
       "      <th>count</th>\n",
       "      <td>919</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>914</td>\n",
       "      <td>914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>[keto]</td>\n",
       "      <td>[need, good, plan, boy, realli, need, good, pl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>3</td>\n",
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       "subreddit                                                      diet  \\\n",
       "post      count                                                 919   \n",
       "          unique                                                919   \n",
       "          top     Turmeric and how to use it Does putting a pinc...   \n",
       "          freq                                                    1   \n",
       "wordslist count                                                 919   \n",
       "          unique                                                914   \n",
       "          top                                                [keto]   \n",
       "          freq                                                    3   \n",
       "\n",
       "subreddit                                                   workout  \n",
       "post      count                                                 914  \n",
       "          unique                                                914  \n",
       "          top     Dont feel the burn Ive been working out at the...  \n",
       "          freq                                                    1  \n",
       "wordslist count                                                 914  \n",
       "          unique                                                914  \n",
       "          top     [need, good, plan, boy, realli, need, good, pl...  \n",
       "          freq                                                    1  "
      ]
     },
     "execution_count": 264,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df_combine.groupby('subreddit')['post', 'wordslist'].describe().T"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparing the text for word cloud\n",
    "set_diet = \" \".join(r for r in df_combine[df_combine['subreddit']=='diet']['stemmed_post'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {},
   "outputs": [],
   "source": [
    "#preparing the text for word cloud for wholoe of \n",
    "set_workout = \" \".join(r for r in df_combine[df_combine['subreddit']=='workout']['stemmed_post'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting the word cloud\n",
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(20,20)) \n",
    "listwords = [{'Workout Set': set_workout}, \n",
    "             {'Diet Set': set_diet}]\n",
    "ax = ax.ravel()\n",
    "\n",
    "for i in range(len(listwords)):\n",
    "    key, val = next(iter(listwords[i].items())) \n",
    "    wordcloud = WordCloud( min_font_size=10, \n",
    "                          normalize_plurals=True,\n",
    "                         repeat=False).generate(str(val))\n",
    "    ax[i].imshow(wordcloud, interpolation='bilinear')\n",
    "    ax[i].axis(\"off\")\n",
    "    ax[i].set_title(str(key))\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "The 2 sets are pretty distinct from list of words they have in each subreddit\n",
    "There are some overlaps of words like 'day' and 'look' which could affect the accuracy of the prediction, but the distinct words should allow us to classify the classes with good accuracy\n",
    "</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Average Length of each post"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "metadata": {},
   "outputs": [],
   "source": [
    "#find the average length of the post. \n",
    "df_combine['lenpost'] =  df_combine['stemmed_post'].apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "metadata": {
    "scrolled": false
   },
   "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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subreddit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diet</th>\n",
       "      <td>0</td>\n",
       "      <td>2478</td>\n",
       "      <td>223.535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workout</th>\n",
       "      <td>0</td>\n",
       "      <td>8450</td>\n",
       "      <td>238.155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           min   max  average\n",
       "subreddit                    \n",
       "diet         0  2478  223.535\n",
       "workout      0  8450  238.155"
      ]
     },
     "execution_count": 269,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine.groupby('subreddit')['lenpost'].agg([min, max, np.average])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "metadata": {
    "scrolled": true
   },
   "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>subreddit</th>\n",
       "      <th>diet</th>\n",
       "      <th>workout</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>919.000</td>\n",
       "      <td>914.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>223.535</td>\n",
       "      <td>238.155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>255.035</td>\n",
       "      <td>441.945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>41.500</td>\n",
       "      <td>79.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>156.000</td>\n",
       "      <td>154.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>300.000</td>\n",
       "      <td>278.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2,478.000</td>\n",
       "      <td>8,450.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "subreddit      diet   workout\n",
       "count       919.000   914.000\n",
       "mean        223.535   238.155\n",
       "std         255.035   441.945\n",
       "min           0.000     0.000\n",
       "25%          41.500    79.000\n",
       "50%         156.000   154.000\n",
       "75%         300.000   278.000\n",
       "max       2,478.000 8,450.000"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine.groupby('subreddit')['lenpost'].describe().T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\">There are empty records after stemming and cleaning. Investigate further </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "scrolled": true
   },
   "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>subreddit</th>\n",
       "      <th>post</th>\n",
       "      <th>wordslist</th>\n",
       "      <th>stemmed_post</th>\n",
       "      <th>lenpost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>workout</td>\n",
       "      <td>Workout</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1098</th>\n",
       "      <td>diet</td>\n",
       "      <td>بدائل الارز في الكيو دايت</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1165</th>\n",
       "      <td>diet</td>\n",
       "      <td>😂</td>\n",
       "      <td>[]</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     subreddit                        post wordslist stemmed_post  lenpost\n",
       "430    workout                    Workout         []                     0\n",
       "1098      diet  بدائل الارز في الكيو دايت         []                     0\n",
       "1165      diet                          😂         []                     0"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine[df_combine['lenpost']==0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> Removing the empty posts before doing the modelling</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop rows for the empty rows\n",
    "droprows = df_combine[df_combine['lenpost']==0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_combine = df_combine.drop(droprows, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "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>min</th>\n",
       "      <th>max</th>\n",
       "      <th>average</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>subreddit</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>diet</th>\n",
       "      <td>3</td>\n",
       "      <td>2478</td>\n",
       "      <td>224.023</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>workout</th>\n",
       "      <td>3</td>\n",
       "      <td>8450</td>\n",
       "      <td>238.416</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           min   max  average\n",
       "subreddit                    \n",
       "diet         3  2478  224.023\n",
       "workout      3  8450  238.416"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine.groupby('subreddit')['lenpost'].agg([ min, max, np.average])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> Each subreddit had average of 225 to 241 words in each posts. Data seemed balance from both categoies </font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 275,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<AxesSubplot:title={'center':'diet'}>,\n",
       "       <AxesSubplot:title={'center':'workout'}>], dtype=object)"
      ]
     },
     "execution_count": 275,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5qVaSJEkaI4Oe2f4A8C7gp31tZ1XVIYDu8cyufTmwv6/fga7tGEkuT7Irya7p6ekTrVuStAi5NkhabOYM20leB9xXVTcPeMzM0laPaajaUlVrqmrN1NTUgIeWJC1mrg2SFpulA/R5JfCGJL8JPAn4uSQfBw4nWVZVh5IsA+7r+h8AVva9fgVwcD6LliRJksbBnGe2q+qqqlpRVavo/eHjn1fVW4AdwMau20bg2m57B7AhyalJzgFWAzfNe+WSJEnSiBvkzPbxXA1sT3IZcA9wKUBV7U6yHbgVOAJcUVVHT7pSSZIkacycUNiuqhuAG7rt+4ELjtNvM7D5JGuTJEmSxpp3kJQkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKmROcN2kicluSnJN5PsTvIvu/bTk1yfZG/3eFrfa65Ksi/JniQXthyAJEmSNKoGObP9EPDrVfVi4HxgXZKXA5uAnVW1GtjZ7ZPkXGADcB6wDrgmyZIGtUuSJEkjbc6wXT0PdrtP6P4VsB7Y2rVvBS7pttcD26rqoaq6E9gHrJ3PoiVJkqRxMNA120mWJLkFuA+4vqq+CpxVVYcAusczu+7Lgf19Lz/Qtc085uVJdiXZNT09fRJDkCQtFq4NkhabgcJ2VR2tqvOBFcDaJC/6Gd0z2yFmOeaWqlpTVWumpqYGKlaStLi5NkhabE7o20iq6gHgBnrXYh9Osgyge7yv63YAWNn3shXAwZMtVJIkSRo3g3wbyVSSZ3bbTwZeC9wO7AA2dt02Atd22zuADUlOTXIOsBq4aZ7rliRJkkbe0gH6LAO2dt8ocgqwvaq+kOQrwPYklwH3AJcCVNXuJNuBW4EjwBVVdbRN+ZIkSdLomjNsV9VfAS+Zpf1+4ILjvGYzsPmkq5MkSZLGmHeQlCRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDVi2JYkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpkaXDLmC+rdp03aztd1198QJXIkmSpEnnmW1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRG5gzbSVYm+R9JbkuyO8k7u/bTk1yfZG/3eFrfa65Ksi/JniQXthyAJEmSNKoGObN9BPi9qnoh8HLgiiTnApuAnVW1GtjZ7dM9twE4D1gHXJNkSYviJUmSpFE2Z9iuqkNV9fVu+4fAbcByYD2wteu2Fbik214PbKuqh6rqTmAfsHae65YkSZJG3glds51kFfAS4KvAWVV1CHqBHDiz67Yc2N/3sgNdmyRJkjRRBg7bSZ4GfAa4sqp+8LO6ztJWsxzv8iS7kuyanp4etAxJ0iLm2iBpsRkobCd5Ar2g/Ymq+mzXfDjJsu75ZcB9XfsBYGXfy1cAB2ces6q2VNWaqlozNTX1eOuXJC0irg2SFptBvo0kwIeB26rq/X1P7QA2dtsbgWv72jckOTXJOcBq4Kb5K1mSJEkaD0sH6PNK4K3AXye5pWt7D3A1sD3JZcA9wKUAVbU7yXbgVnrfZHJFVR2d78IlSZKkUTdn2K6qv2T267ABLjjOazYDm0+iLkmSJGnseQdJSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNWLYliRJkhoxbEuSJEmNGLYlSZKkRgzbkiRJUiOGbUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDVi2JYkSZIaMWxLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpEcO2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY3MGbaTfCTJfUm+1dd2epLrk+ztHk/re+6qJPuS7ElyYavCJUmSpFE3yJntjwLrZrRtAnZW1WpgZ7dPknOBDcB53WuuSbJk3qqVJEmSxsicYbuq/ifwNzOa1wNbu+2twCV97duq6qGquhPYB6ydn1IlSZKk8fJ4r9k+q6oOAXSPZ3bty4H9ff0OdG2PkeTyJLuS7Jqenn6cZUiSFhPXBkmLzdJ5Pl5maavZOlbVFmALwJo1a2btM59WbbruMW13XX1x67eVJJ2AhV4bJKm1x3tm+3CSZQDd431d+wFgZV+/FcDBx1+eJEmSNL4eb9jeAWzstjcC1/a1b0hyapJzgNXATSdXoiRJkjSe5ryMJMkngVcDz0pyAHgvcDWwPcllwD3ApQBVtTvJduBW4AhwRVUdbVS7JEmSNNLmDNtV9ebjPHXBcfpvBjafTFGSJEnSYuAdJCVJkqRGDNuSJElSI4ZtSZIkqRHDtiRJktSIYVuSJElqxLAtSZIkNTLft2uXJGlRWLXpunk5zl1XXzwvx5E0njyzLUmSJDVi2JYkSZIamejLSGb7FaG/7pMkSdJ88cy2JEmS1IhhW5IkSWrEsC1JkiQ1YtiWJEmSGjFsS5IkSY0YtiVJkqRGJvqr/2ZzvDuG+ZWAkiRJOlGe2ZYkSZIaMWxLkiRJjRi2JUmSpEa8ZntA3tpdkiRJJ6rZme0k65LsSbIvyaZW7yNJkiSNqiZhO8kS4D8BFwHnAm9Ocm6L95IkSZJGVavLSNYC+6rqDoAk24D1wK2N3m8oTuTSEi9DkaTJdLyvlD1RrhnSeGoVtpcD+/v2DwC/3N8hyeXA5d3ug0n2PI73eRbw3cdVYSP5N236/gwjNwcLbNLHD84BLPwcPHsB32uiLNa1YT6cwJqxKMd/gpwD52AY4591bWgVtjNLWx2zU7UF2HJSb5Lsqqo1J3OMcTfpczDp4wfnAJyDxcS14eRN+vjBOQDnYJTG3+oPJA8AK/v2VwAHG72XJEmSNJJahe2vAauTnJPkicAGYEej95IkSZJGUpPLSKrqSJLfBf4MWAJ8pKp2N3irk/pV4yIx6XMw6eMH5wCcAx1r0v97mPTxg3MAzsHIjD9VNXcvSZIkSSfM27VLkiRJjRi2JUmSpEYM25IkSVIjhm1JkiSpkVY3tWkiyQvo3fZ9Ob2b5BwEdlTVbUMtTJI0FK4Lkkbd2HwbSZJ3A28GttG7aQ70bpazAdhWVVcPq7aFkuRC4BKOXVSuraovDrOuheQcOAeTPn49ynWhZ9I/E5M+fnAOYLTnYJzC9reB86rq4RntTwR2V9Xq4VS2MJJ8AHge8F85dlH5B8DeqnrnkEpbMM6BczDp49exJn1dAD8Tkz5+cA5g9OdgnML27cCFVXX3jPZnA1+qqucPp7KFkeTbVfW8WdoDfHtCFhXnYMLnYNLHr2NN+roAfiYmffzgHMDoz8E4XbN9JbAzyV5gf9f2C8Bzgd8dVlEL6G+TrK2qm2a0vwz422EUNATOgXMw6ePXsa5kstcF8DMx6eMH5wBGfA7G5sw2QJJTgLX0rscJvV8VfK2qjg61sAWQ5KXAHwJP59FfkawEfgD8TlXdPKzaFopz4BxM+vj1WJO8LoCfiUkfPzgHMPpzMFZhW5Dk5+lbVKrqO0MuacE5B87BpI9fmmnSPxOTPn5wDmB052CcLiOZeN21R8/m0b+0XZLkcE3Q/zE5B87BpI9fmmnSPxOTPn5wDmC058Az22MiyW8A1wB7gXu75hX0rk38nar60rBqWyjOgXMw6eOXZpr0z8Skjx+cAxj9OTBsj4kktwEXVdVdM9rPAf60ql44lMIWkHPgHEz6+KWZJv0zMenjB+cARn8OvF37+FjKoxf997sXeMIC1zIszoFzMOnjl2aa9M/EpI8fnAMY8Tnwmu3x8RHga0m28ehXXK2kd6e0Dw+tqoXlHDgHkz5+aaZJ/0xM+vjBOYARnwMvIxkjSc4F3sCxX3G1o6puHWphC8g5cA4mffzSTJP+mZj08YNzAKM9B4ZtSZIkqRGv2R4TSZ6R5Ooktye5v/t3W9f2zGHXtxCcA+dg0scvzTTpn4lJHz84BzD6c2DYHh/bge8Br66qM6rqDOA1wAPAp4ZZ2AJyDpyDSR+/NNOkfyYmffzgHMCIz4GXkYyJJHuq6vkn+txi4hw4B5M+fmmmSf9MTPr4wTmA0Z8Dz2yPj7uTvCvJWY80JDkrybt59C9vFzvnwDmY9PFLM036Z2LSxw/OAYz4HBi2x8ebgDOAG5N8L8nfADcApwN/f5iFLaCZc/A9enNwBpM7B5P238Gkj1+aadI/E64L/jcAIz4HXkYyRpK8gN7tR/93VT3Y176uqr44vMqGJ8nHquqtw65joST5ZeD2qvp+kqcAm4CXAruBP6iq7w+1wMaSPBF4M3BvVf33JL8NvAK4FdhSVQ8PtUBpCFwbjuW6MFnrAoz+2mDYHhNJ3gFcAdwGnA+8s6qu7Z77elW9dIjlLYgkO2Zp/nXgzwGq6g0LW9HCS7IbeHFVHUmyBfgR8Bnggq79jUMtsLEkn6B3M64nA98Hngp8jt74U1Ubh1ietOAmfW1wXXBdgNFfG7yD5Pj4R8AvVdWDSVYBn06yqqo+SO/L2yfBCnr/l/ohoOiN+2XAvx1mUQvslKo60m2v6VtI/zLJLUOqaSH9YlX9nSRL6d2G9+yqOprk48A3h1ybNAyTvja4LrguwIivDV6zPT6WPPLrwaq6C3g1cFGS9zMZP1AB1gA3A/8c+H5V3QD836q6sapuHGplC+dbSd7WbX8zyRqAJM8DJuESilO6Xxc+HXgK8Iyu/VTgCUOrShqeSV8bXBdcF2DE1wbPbI+P7yQ5v6puAejOYrwO+Ajwi0OtbIFU1U+Bf5fkU93jYSbvv+G3Ax9M8i+A7wJfSbKf3l9bv32olS2MDwO3A0voLa6fSnIH8HJg2zALk4ZkotcG1wXAdQFGfG3wmu0xkWQFcKSqvjPLc6+sqi8PoayhSnIx8Mqqes+wa1loSZ4OPIfeonKgqg4PuaQFk+RsgKo6mN6dwV4L3FNVNw21MGkIXBuO5bowmesCjPbaYNiWJEmSGvGabUmSJKkRw7YkSZLUiGFbkiRJasSwLUmSJDXy/wAiWv4bET04ewAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#checking the average length of each post\n",
    "df_combine.hist(column='lenpost', by='subreddit', bins=15, figsize=(12,5), sharex=True,\n",
    "    sharey = True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> Diet group generally have a bigger variance in the length of posts  </font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Common words for each category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "metadata": {},
   "outputs": [],
   "source": [
    "def findcommonwords(data, isstr=True, num_words=20, plot=True, color=None):\n",
    "    \"\"\"\n",
    "    Function to count the top number of wrords and display in a graph\n",
    "    Takes in arguements:\n",
    "    - data - list or string to count words\n",
    "    - isstr - if True, data parsed in is a string, and needs to perform split into list \n",
    "    - num_words - count of the most common words\n",
    "    - plot - if a graph is required to be plotted. \n",
    "    \n",
    "    Returns Dataframe of the common words and their count\n",
    "    \"\"\"\n",
    "    if isstr:\n",
    "        #splitting into individual words\n",
    "        indiv_words = data.split(\" \")\n",
    "    else:\n",
    "        indiv_words = data\n",
    "        \n",
    "    common = pd.DataFrame(Counter(indiv_words).most_common(num_words), columns=('words', 'count')).sort_values(by='count', ascending=False)\n",
    "    \n",
    "    if plot:\n",
    "        \n",
    "        sns.barplot(x='count', y='words', data=common, color=color)\n",
    "        \n",
    "    return common"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 369,
   "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": [
    "#plot out the top 20 common words for workout document\n",
    "common_diet = findcommonwords(set_diet, isstr=True, num_words=20, plot=True, color='maroon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 370,
   "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": [
    "#plot out the top 20 common words for workout document\n",
    "common_workout = findcommonwords(set_workout, isstr=True, num_words=20, plot=True, color='green')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {},
   "outputs": [],
   "source": [
    "#comparing the 2 dataframes on the 20 top common words:\n",
    "def dataframe_difference(df1, df2, which=None):\n",
    "    \"\"\"Find rows which are different between two DataFrames.\"\"\"\n",
    "    comparison_df = df1.merge(df2, on='words',\n",
    "                              indicator=True,\n",
    "                              how='outer')\n",
    "    if which is None:\n",
    "        diff_df = comparison_df[comparison_df['_merge'] != 'both']\n",
    "    else:\n",
    "        diff_df = comparison_df[comparison_df['_merge'] == which]\n",
    "    return diff_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "scrolled": false
   },
   "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>words</th>\n",
       "      <th>count_x</th>\n",
       "      <th>count_y</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>eat</td>\n",
       "      <td>890.000</td>\n",
       "      <td>232.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>day</td>\n",
       "      <td>552.000</td>\n",
       "      <td>442.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>weight</td>\n",
       "      <td>515.000</td>\n",
       "      <td>502.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>like</td>\n",
       "      <td>341.000</td>\n",
       "      <td>353.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>get</td>\n",
       "      <td>295.000</td>\n",
       "      <td>420.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>help</td>\n",
       "      <td>252.000</td>\n",
       "      <td>316.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>want</td>\n",
       "      <td>245.000</td>\n",
       "      <td>344.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>would</td>\n",
       "      <td>245.000</td>\n",
       "      <td>236.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>week</td>\n",
       "      <td>241.000</td>\n",
       "      <td>267.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>know</td>\n",
       "      <td>238.000</td>\n",
       "      <td>275.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tri</td>\n",
       "      <td>233.000</td>\n",
       "      <td>262.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>fat</td>\n",
       "      <td>233.000</td>\n",
       "      <td>273.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>go</td>\n",
       "      <td>229.000</td>\n",
       "      <td>249.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>start</td>\n",
       "      <td>217.000</td>\n",
       "      <td>393.000</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     words  count_x  count_y _merge\n",
       "0      eat  890.000  232.000   both\n",
       "1      day  552.000  442.000   both\n",
       "2   weight  515.000  502.000   both\n",
       "5     like  341.000  353.000   both\n",
       "6      get  295.000  420.000   both\n",
       "7     help  252.000  316.000   both\n",
       "9     want  245.000  344.000   both\n",
       "10   would  245.000  236.000   both\n",
       "11    week  241.000  267.000   both\n",
       "12    know  238.000  275.000   both\n",
       "13     tri  233.000  262.000   both\n",
       "14     fat  233.000  273.000   both\n",
       "15      go  229.000  249.000   both\n",
       "17   start  217.000  393.000   both"
      ]
     },
     "execution_count": 280,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataframe_difference(common_diet, common_workout, which='both')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Existence of these words in both classes may cause error. I will remove them from the stop words as well</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [],
   "source": [
    "common_words =  dataframe_difference(common_diet, common_workout, which='both')['words'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['eat',\n",
       " 'day',\n",
       " 'weight',\n",
       " 'like',\n",
       " 'get',\n",
       " 'help',\n",
       " 'want',\n",
       " 'would',\n",
       " 'week',\n",
       " 'know',\n",
       " 'tri',\n",
       " 'fat',\n",
       " 'go',\n",
       " 'start']"
      ]
     },
     "execution_count": 282,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "common_words"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Prep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "### Labelling\n",
    " \n",
    "Convert workout/diet sets into binary labels:\n",
    "- 0 for workout\n",
    "- 1 for diet\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 283,
   "metadata": {
    "scrolled": true
   },
   "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>subreddit</th>\n",
       "      <th>post</th>\n",
       "      <th>wordslist</th>\n",
       "      <th>stemmed_post</th>\n",
       "      <th>lenpost</th>\n",
       "      <th>is_diet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1828</th>\n",
       "      <td>diet</td>\n",
       "      <td>Am I Actually \"Intermittent Fasting\"? I have b...</td>\n",
       "      <td>[actual, intermitt, fast, lot, success, keto, ...</td>\n",
       "      <td>actual intermitt fast lot success keto plateau...</td>\n",
       "      <td>502</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1829</th>\n",
       "      <td>diet</td>\n",
       "      <td>50 calories over calorie allowance I went over...</td>\n",
       "      <td>[calori, calori, allow, went, calori, allow, c...</td>\n",
       "      <td>calori calori allow went calori allow calori e...</td>\n",
       "      <td>88</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1830</th>\n",
       "      <td>diet</td>\n",
       "      <td>BEST WHEY PROTEIN FOR WEIGHT LOSS (lean) Which...</td>\n",
       "      <td>[best, whey, protein, weight, loss, lean, prot...</td>\n",
       "      <td>best whey protein weight loss lean protein bes...</td>\n",
       "      <td>121</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>diet</td>\n",
       "      <td>Keep getting cravings/hungry in the evening He...</td>\n",
       "      <td>[keep, get, crave, hungri, even, hey, new, cut...</td>\n",
       "      <td>keep get crave hungri even hey new cut calori ...</td>\n",
       "      <td>311</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1832</th>\n",
       "      <td>diet</td>\n",
       "      <td>Why is my diet so expensive to maintain? So I ...</td>\n",
       "      <td>[expens, maintain, got, back, shop, spent, alm...</td>\n",
       "      <td>expens maintain got back shop spent almost alm...</td>\n",
       "      <td>694</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     subreddit                                               post  \\\n",
       "1828      diet  Am I Actually \"Intermittent Fasting\"? I have b...   \n",
       "1829      diet  50 calories over calorie allowance I went over...   \n",
       "1830      diet  BEST WHEY PROTEIN FOR WEIGHT LOSS (lean) Which...   \n",
       "1831      diet  Keep getting cravings/hungry in the evening He...   \n",
       "1832      diet  Why is my diet so expensive to maintain? So I ...   \n",
       "\n",
       "                                              wordslist  \\\n",
       "1828  [actual, intermitt, fast, lot, success, keto, ...   \n",
       "1829  [calori, calori, allow, went, calori, allow, c...   \n",
       "1830  [best, whey, protein, weight, loss, lean, prot...   \n",
       "1831  [keep, get, crave, hungri, even, hey, new, cut...   \n",
       "1832  [expens, maintain, got, back, shop, spent, alm...   \n",
       "\n",
       "                                           stemmed_post  lenpost  is_diet  \n",
       "1828  actual intermitt fast lot success keto plateau...      502        1  \n",
       "1829  calori calori allow went calori allow calori e...       88        1  \n",
       "1830  best whey protein weight loss lean protein bes...      121        1  \n",
       "1831  keep get crave hungri even hey new cut calori ...      311        1  \n",
       "1832  expens maintain got back shop spent almost alm...      694        1  "
      ]
     },
     "execution_count": 283,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine['is_diet'] = df_combine['subreddit'].map({'workout': 0, 'diet': 1})\n",
    "df_combine.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    917\n",
       "0    913\n",
       "Name: is_diet, dtype: int64"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_combine['is_diet'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Train Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_combine['stemmed_post']\n",
    "y = df_combine['is_diet']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Train test split with stratify\n",
    "X_train, X_val, y_train, y_val = train_test_split(X,y,random_state=42, stratify=y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1226,)"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(604,)"
      ]
     },
     "execution_count": 289,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_val.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predictive Models\n",
    "**Part 3** of the project focuses on **Classification Modeling.** \n",
    "\n",
    "### Models with Hyperparameter tuning and Vectorizers\n",
    "\n",
    "We will use both ```CountVectorizer()``` and ```TfidfVectorizer()``` to transform the data before fitting into the following models:\n",
    "\n",
    "1. Logistic Regression \n",
    "2. Naive Bayes \n",
    "3. KNearestNeighbours with (KNN) \n",
    "4. Support Vector Machines (SVM) \n",
    "\n",
    "The models will be run with **hyperparameters tuning** for the various model using GridSearch on 5-folds Cross Validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyperparameters\n",
    "\n",
    "**CountVectorizer Parameters:**\n",
    "- max_features': [1000, 3000, 5000]\n",
    "- max_df': [1, 3, 5, 7]\n",
    "\n",
    "**TF-IDF Parameters:**\n",
    "- max_features': [1000, 3000, 5000]\n",
    "- max_df': [0.3, 0.4, 0.5, 0.7]\n",
    "- ngram_range':[(1,2), (2,2)]\n",
    "\n",
    "**Linear Regression:**\n",
    "- solver': ['newton-cg', 'lbfgs', 'liblinear']\n",
    "- C':[100,10,1.0, 0.1, 0.01]\n",
    "- penalty':['l1', 'l2']\n",
    "\n",
    "**KNeighbours:**\n",
    "- n_neighbors': [3,5,7,9, 11, 13]\n",
    "\n",
    "**Support Vector Machine:** \n",
    "- svm__C': [1.0, 2.0, 3.0],\n",
    "- svm__kernel': ['rbf', 'linear', 'poly'],\n",
    "- svm__degree': [3,4,5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Performance Metrics\n",
    "\n",
    "The models will be gauged on the performance using the following metrics:\n",
    "\n",
    "**1) Accuracy - TP+TN/total records**\n",
    "   - How accurate is the Model in predicting both classes? \n",
    "   - The higher score the more accurate the model is at predicting the classes\n",
    "    \n",
    "**2) Matthews Correlation Coefficient (MCC)** \n",
    "   - What is the correlation coefficient between the observed and predicted binary classifications --  \n",
    "   - the  higher the score indicates that the model was able to correctly predict the majority of positive data instances and the majority of negative data instances\n",
    "   \n",
    "Not considering other metrics like Recall or Specificity because the 2 classes are equally important, and not just looking at the positive class, and in this case, accuracy and MCC will be a better gauge on model performanace.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### User Defined Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 465,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifiermodel(X_train, y_train, X_test, y_test, model, gs=False, title=None):\n",
    "    \n",
    "    \"\"\"\"\n",
    "    Function to fit and display scores of the model, the confusion matrix and AUC ROC\n",
    "    \n",
    "    Take in Arguements:\n",
    "     - X_train, y_train, X_test, y_test - the train test split data\n",
    "     - model - the type of model to fit data\n",
    "     - gs - if the model parsed in is a grid search\n",
    "     - est_trans - the type of estimator and transformer being used\n",
    "     - labels - list that model is classification\n",
    "     \n",
    "     Returns the dataframe of the predicted values and actual values\n",
    "    \"\"\"    \n",
    "    model.fit(X_train, y_train)\n",
    "    trgscore = model.score(X_train, y_train)\n",
    "    y_pred = model.predict(X_test)\n",
    "    valscore = model.score(X_test, y_test)\n",
    "    \n",
    "    #getting the probabilities of test data\n",
    "    \n",
    "    if gs:\n",
    "        best_model = model.best_estimator_\n",
    "        best_score = model.best_score_\n",
    "        best_params =  model.best_params_\n",
    "    else:\n",
    "        best_model = model\n",
    "        best_score = trgscore\n",
    "        best_params = model.get_params()\n",
    "        \n",
    "    proba = model.predict_proba(X_test)\n",
    "    proba = proba[:, 1]\n",
    "\n",
    "    print(f'======== {title} =================\\n')\n",
    "    \n",
    "    \n",
    "    print('Best parameters  : ', best_params)\n",
    "    print('Best CV  score   : ', best_score)\n",
    "    print('Training score   :', trgscore)\n",
    "    print('Validation score :', valscore)\n",
    "    print()    \n",
    "        \n",
    "    return y_pred, proba"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_metrics(true, pred, prob, cm=False, show_df=False, title = 'Model_Metrics', labels=['0', '1']):\n",
    "    \"\"\"\n",
    "    Function that calculates and returns a dataframe with classification metrics.\n",
    "    Input\n",
    "    true: true values of target variable\n",
    "    pred: predicted values of target variable\n",
    "    prob: probability of positive class \n",
    "    cm: confusion matrix will be plot if == True\n",
    "    show_df: dataframe with metrics will be displayed if == True\n",
    "    title: the column name for the dataframe that is displayed/returned\n",
    "    labels: the negative and positive class labels \n",
    "    \n",
    "    Output\n",
    "    df: dataframe with classification metrics\n",
    "    \n",
    "    Metric Descriptions\n",
    "    True Positive: total number of positive class correctly predicted\n",
    "    True Negative: total number of negative class correctly predicted\n",
    "    False Positives (Type I Error): total number of negatives class predicted as positive\n",
    "    False Negative (Type II Error): total number of positives class predicted as negative\n",
    "    \n",
    "    Accuracy: Percentage of observations correctly classified.\n",
    "    Misclassification Rate: Percentage of observations incorrectly classified.\n",
    "    Sensitivity(Recall): Percentage of observations with positive correctly classified.\n",
    "    Specificity: Percentage of observations with negative correctly classified.\n",
    "    Precision: Percentage of positive class prediction vs total positive predictions\n",
    "    F1 Score: Weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0\n",
    "    AUC ROC: AUC ROC Score\n",
    "    \"\"\"\n",
    "    tn, fp, fn, tp = confusion_matrix(true, pred).ravel()\n",
    "    fpr, tpr, threhold = roc_curve(true, prob)\n",
    "    \n",
    "    if cm:\n",
    "        fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15,5))\n",
    "        ax = ax.ravel()\n",
    " \n",
    "        #plot confusion matrix\n",
    "        ax[0].set_title('Confusion Matrix')\n",
    "        sns.heatmap([[tn,fp],[fn,tp]], \n",
    "                         annot=True, \n",
    "                         cmap=\"YlGnBu\", \n",
    "                         cbar_kws={'label': 'Scale'}, \n",
    "                         fmt='g', ax=ax[0])\n",
    "        ax[0].set_xticklabels(labels)\n",
    "        ax[0].set_yticklabels(labels)\n",
    "        ax[0].set(ylabel=\"True Label\", xlabel=\"Predicted Label\")\n",
    "        \n",
    "        #plot roc_curve\n",
    "        ax[1].set_title('ROC Curve')\n",
    "        ax[1].plot(fpr, tpr, lw = 2, label='ROC curve (area = %0.2f)' % roc_auc_score(true,prob))\n",
    "        ax[1].plot([0, 1], [0,1], lw = 2, linestyle = '--')\n",
    "        ax[1].set(ylabel=\"True Positive Rate\", xlabel=\"False Positive Rate\")\n",
    "        ax[1].legend(loc='lower right')\n",
    "        \n",
    "        \n",
    "    df = pd.DataFrame(data = [(tp+tn)/(tp+fp+tn+fn),\n",
    "                              1- ((tp+tn)/(tp+fp+tn+fn)),\n",
    "                              tp/(tp+fn),\n",
    "                              tn/(tn+fp),\n",
    "                              tp/(tp+fp),\n",
    "                              tp,\n",
    "                              fp,\n",
    "                              fn,\n",
    "                              tn,\n",
    "                              f1_score(true, pred),\n",
    "                              roc_auc_score(true,prob),\n",
    "                              matthews_corrcoef(true, pred)],\n",
    "                         index = ['Accuracy',\n",
    "                                  'Misclassification Rate',\n",
    "                                  'Sensitivity (Recall)',\n",
    "                                  'Specificity',\n",
    "                                  'Precision', \n",
    "                                  'True Positive',\n",
    "                                  'False Positive',\n",
    "                                  'False Negative',\n",
    "                                  'True Negative', \n",
    "                                  'F1-Score',\n",
    "                                  'AUC Score',\n",
    "                                  'Matthews Correlation'],\n",
    "                        columns = [title])\n",
    "    if show_df:\n",
    "        display(df)\n",
    "    return(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 467,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_impt_features(features_df, title, top_feat=20):\n",
    "    plt.figure(figsize=(12,6))\n",
    "    sns.barplot(x = features_df['feature_importance'][:top_feat], y = features_df['feature'][:top_feat], color='b')\n",
    "    plt.title(title, fontsize=18)\n",
    "    plt.xlabel('Feature importance', fontsize=14)\n",
    "    plt.xticks(size=13)\n",
    "    plt.ylabel(None)\n",
    "    plt.yticks(size=13);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Global inputs for GridSearchCV"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 478,
   "metadata": {},
   "outputs": [],
   "source": [
    "cv = 5\n",
    "n_jobs = -1\n",
    "labels = ['workout', 'diet']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1   0.502\n",
       "0   0.498\n",
       "Name: is_diet, dtype: float64"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_val.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> Baseline model: 0.50 of the majority class (diet class)</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Logistic Regression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 469,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 360 candidates, totalling 1800 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    6.3s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:    9.1s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   15.7s\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:   25.0s\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:   37.4s\n",
      "[Parallel(n_jobs=-1)]: Done 1784 tasks      | elapsed:   53.4s\n",
      "[Parallel(n_jobs=-1)]: Done 1800 out of 1800 | elapsed:   53.8s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== LogisticRegression + CountVectorizer with Hyperparameters Tuning =================\n",
      "\n",
      "Best parameters  :  {'cvec__max_df': 7, 'cvec__max_features': 5000, 'lr__C': 0.01, 'lr__penalty': 'l2', 'lr__solver': 'liblinear'}\n",
      "Best CV  score   :  0.694119794259167\n",
      "Training score   : 0.8499184339314845\n",
      "Validation score : 0.6937086092715232\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Tuning Hyperparameters to find the best params using GridSearch\n",
    "cveclr_pipe = Pipeline([('cvec', CountVectorizer(stop_words=common_words)), ('lr', LogisticRegression())])\n",
    "cveclr_param_grid = {'cvec__max_features': [1000, 3000, 5000],\n",
    "                     'cvec__max_df': [1, 3, 5, 7],\n",
    "                     'lr__solver': ['newton-cg', 'lbfgs', 'liblinear'],\n",
    "                     'lr__C':[100,10,1.0, 0.1, 0.01],\n",
    "                     'lr__penalty':['l1', 'l2']}\n",
    "    \n",
    "cveclr_gs = GridSearchCV(cveclr_pipe, param_grid=cveclr_param_grid, cv=cv, n_jobs=n_jobs, verbose=1)\n",
    "\n",
    "cveclr_pred, cveclr_proba = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=cveclr_gs, gs=True, \n",
    "                            title='LogisticRegression + CountVectorizer with Hyperparameters Tuning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "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>CountVec + LogReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.694</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.608</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>236.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>118.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>67.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>183.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.393</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CountVec + LogReg\n",
       "Accuracy                            0.694\n",
       "Misclassification Rate              0.306\n",
       "Sensitivity (Recall)                0.779\n",
       "Specificity                         0.608\n",
       "Precision                           0.667\n",
       "True Positive                     236.000\n",
       "False Positive                    118.000\n",
       "False Negative                     67.000\n",
       "True Negative                     183.000\n",
       "F1-Score                            0.718\n",
       "AUC Score                           0.772\n",
       "Matthews Correlation                0.393"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cveclr_m = model_metrics(y_val, cveclr_pred, cveclr_proba, title='CountVec + LogReg', labels=labels)\n",
    "cveclr_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 480,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 720 candidates, totalling 3600 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    5.5s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   13.1s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   29.9s\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:   53.5s\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:  1.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1784 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 2434 tasks      | elapsed:  2.7min\n",
      "[Parallel(n_jobs=-1)]: Done 3184 tasks      | elapsed:  3.4min\n",
      "[Parallel(n_jobs=-1)]: Done 3600 out of 3600 | elapsed:  3.9min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== LogisticRegression +  TF-IDF with Hyperparameters Tuning =================\n",
      "\n",
      "Best parameters  :  {'lr__C': 1.0, 'lr__penalty': 'l2', 'lr__solver': 'liblinear', 'tvec__max_df': 0.3, 'tvec__max_features': 3000, 'tvec__ngram_range': (1, 2)}\n",
      "Best CV  score   :  0.8825286212045794\n",
      "Training score   : 0.9543230016313213\n",
      "Validation score : 0.8509933774834437\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Tuning Hyperparameters to find the best params using GridSearch\n",
    "tveclr_pipe = Pipeline([('tvec', TfidfVectorizer(stop_words=common_words)), ('lr', LogisticRegression())])\n",
    "\n",
    "tveclr_param_grid = {'tvec__ngram_range':[(1,2), (2,2)],\n",
    "                     'tvec__max_features': [1000, 3000, 5000],\n",
    "                     'tvec__max_df': [0.3, 0.4, 0.5, 0.7],\n",
    "                     'lr__solver': ['newton-cg', 'lbfgs', 'liblinear'],\n",
    "                     'lr__C':[100,10,1.0, 0.1, 0.01],\n",
    "                     'lr__penalty':['l1', 'l2']}\n",
    "    \n",
    "tveclr_gs = GridSearchCV(tveclr_pipe, param_grid=tveclr_param_grid, cv=cv, n_jobs=n_jobs, verbose=1)\n",
    "\n",
    "\n",
    "tveclr_pred, tveclr_proba = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=tveclr_gs, gs=True, \n",
    "                            title='LogisticRegression +  TF-IDF with Hyperparameters Tuning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "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>TFIDF + LogReg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.825</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>250.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>37.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>53.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>264.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.847</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        TFIDF + LogReg\n",
       "Accuracy                         0.851\n",
       "Misclassification Rate           0.149\n",
       "Sensitivity (Recall)             0.825\n",
       "Specificity                      0.877\n",
       "Precision                        0.871\n",
       "True Positive                  250.000\n",
       "False Positive                  37.000\n",
       "False Negative                  53.000\n",
       "True Negative                  264.000\n",
       "F1-Score                         0.847\n",
       "AUC Score                        0.929\n",
       "Matthews Correlation             0.703"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tveclr_m = model_metrics(y_val, tveclr_pred, tveclr_proba, cm=True, title='TFIDF + LogReg', labels=labels)\n",
    "tveclr_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Multinomial Naive Bayes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 482,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 12 candidates, totalling 60 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== Naive Bayes + CountVectorizer with Hyper parameters =================\n",
      "\n",
      "Best parameters  :  {'cvec__max_df': 7, 'cvec__max_features': 5000}\n",
      "Best CV  score   :  0.7063680106188818\n",
      "Training score   : 0.863784665579119\n",
      "Validation score : 0.6903973509933775\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Done  60 out of  60 | elapsed:    0.9s finished\n"
     ]
    }
   ],
   "source": [
    "#Naive Bayes, using Multinomial with CountVectorizer and hyper parameters tuning\n",
    "cvecnb_pipe = Pipeline([('cvec', CountVectorizer(stop_words=common_words)), ('nb' , MultinomialNB())])\n",
    "cvecnb_param_grid = {'cvec__max_features': [1000, 3000, 5000],\n",
    "                     'cvec__max_df': [1, 3, 5, 7]}\n",
    "    \n",
    "cvecnb_gs = GridSearchCV(cvecnb_pipe, param_grid=cvecnb_param_grid, cv=cv, n_jobs=n_jobs,  verbose=1)\n",
    "\n",
    "cvecnb_pred, cvecnb_proba = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=cvecnb_gs, gs=True, \n",
    "                            title='Naive Bayes + CountVectorizer with Hyper parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "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>CountVec + NaiveBayes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.690</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.628</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>228.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>112.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>75.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>189.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.383</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CountVec + NaiveBayes\n",
       "Accuracy                                0.690\n",
       "Misclassification Rate                  0.310\n",
       "Sensitivity (Recall)                    0.752\n",
       "Specificity                             0.628\n",
       "Precision                               0.671\n",
       "True Positive                         228.000\n",
       "False Positive                        112.000\n",
       "False Negative                         75.000\n",
       "True Negative                         189.000\n",
       "F1-Score                                0.709\n",
       "AUC Score                               0.762\n",
       "Matthews Correlation                    0.383"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvecnb_m = model_metrics(y_val, cvecnb_pred, cvecnb_proba, cm=True, title='CountVec + NaiveBayes', labels=labels)\n",
    "cvecnb_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 24 candidates, totalling 120 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    6.1s\n",
      "[Parallel(n_jobs=-1)]: Done 120 out of 120 | elapsed:   11.2s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== Naive Bayes+  TF-IDF with Hyperparameters Tuning =================\n",
      "\n",
      "Best parameters  :  {'tvec__max_df': 0.3, 'tvec__max_features': 3000, 'tvec__ngram_range': (1, 2)}\n",
      "Best CV  score   :  0.8849776007964161\n",
      "Training score   : 0.9518760195758564\n",
      "Validation score : 0.8443708609271523\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#NB with TF-IDF with hyper parameters\n",
    "tvecnb_pipe = Pipeline([('tvec', TfidfVectorizer(stop_words=common_words)), ('nb' , MultinomialNB())])\n",
    "\n",
    "tvecnb_param_grid = {'tvec__ngram_range':[(1,2), (2,2)],\n",
    "                     'tvec__max_features': [1000, 3000, 5000],\n",
    "                     'tvec__max_df': [0.3, 0.4, 0.5, 0.7]}\n",
    "    \n",
    "tvecnb_gs = GridSearchCV(tvecnb_pipe, param_grid=tvecnb_param_grid, n_jobs=-1, verbose=1)\n",
    "\n",
    "tvecnb_pred, tvecnb_proba  = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=tvecnb_gs, gs=True, \n",
    "                            title='Naive Bayes+  TF-IDF with Hyperparameters Tuning')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "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>TFIDF + NaiveBayes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.887</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>243.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>34.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>60.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>267.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        TFIDF + NaiveBayes\n",
       "Accuracy                             0.844\n",
       "Misclassification Rate               0.156\n",
       "Sensitivity (Recall)                 0.802\n",
       "Specificity                          0.887\n",
       "Precision                            0.877\n",
       "True Positive                      243.000\n",
       "False Positive                      34.000\n",
       "False Negative                      60.000\n",
       "True Negative                      267.000\n",
       "F1-Score                             0.838\n",
       "AUC Score                            0.933\n",
       "Matthews Correlation                 0.691"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tvecnb_m = model_metrics(y_val, tvecnb_pred, tvecnb_proba, cm=True, title='TFIDF + NaiveBayes', labels=labels)\n",
    "tvecnb_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KNN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 502,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 72 candidates, totalling 360 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    1.0s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:    4.6s\n",
      "[Parallel(n_jobs=-1)]: Done 360 out of 360 | elapsed:    8.9s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== KNeighborsClassifier + CountVectorizer with Hyper parameters =================\n",
      "\n",
      "Best parameters  :  {'cvec__max_df': 7, 'cvec__max_features': 1000, 'knn__n_neighbors': 7}\n",
      "Best CV  score   :  0.5587323709971794\n",
      "Training score   : 0.6566068515497553\n",
      "Validation score : 0.5794701986754967\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#KNN with CountVectorizer and default parameters\n",
    "cvecknn_pipe = Pipeline([('cvec', CountVectorizer(stop_words=common_words)), ('knn' , KNeighborsClassifier())])\n",
    "cvecknn_param_grid = {'cvec__max_features': [1000, 3000, 5000],\n",
    "                     'cvec__max_df': [1, 3, 5, 7],\n",
    "                     'knn__n_neighbors': [3,5,7,9, 11, 13]\n",
    "                     }\n",
    "    \n",
    "cvecknn_gs = GridSearchCV(cvecknn_pipe, param_grid=cvecknn_param_grid, n_jobs=-1, verbose=1)\n",
    "\n",
    "cvecknn_pred, cvecknn_proba = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=cvecknn_gs, gs=True, \n",
    "                            title='KNeighborsClassifier + CountVectorizer with Hyper parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 507,
   "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>CountVec + KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.579</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>238.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>189.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>65.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>112.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.173</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CountVec + KNN\n",
       "Accuracy                         0.579\n",
       "Misclassification Rate           0.421\n",
       "Sensitivity (Recall)             0.785\n",
       "Specificity                      0.372\n",
       "Precision                        0.557\n",
       "True Positive                  238.000\n",
       "False Positive                 189.000\n",
       "False Negative                  65.000\n",
       "True Negative                  112.000\n",
       "F1-Score                         0.652\n",
       "AUC Score                        0.602\n",
       "Matthews Correlation             0.173"
      ]
     },
     "execution_count": 507,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvecknn_m = model_metrics(y_val, cvecknn_pred, cvecknn_proba, cm=True, title='CountVec + KNN', labels=labels)\n",
    "cvecknn_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 504,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 144 candidates, totalling 720 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    2.0s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   11.2s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   27.7s\n",
      "[Parallel(n_jobs=-1)]: Done 720 out of 720 | elapsed:   46.3s finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== KNeighborsClassifier + TF-IDF with Hyper parameters =================\n",
      "\n",
      "Best parameters  :  {'knn__n_neighbors': 13, 'tvec__max_df': 0.3, 'tvec__max_features': 5000, 'tvec__ngram_range': (1, 2)}\n",
      "Best CV  score   :  0.8132171893147502\n",
      "Training score   : 0.8433931484502447\n",
      "Validation score : 0.7996688741721855\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#KNN with TF-IDF with hyper parameters\n",
    "tvecknn_pipe = Pipeline([('tvec', TfidfVectorizer(stop_words=common_words)), ('knn' , KNeighborsClassifier())])\n",
    "tvecknn_param_grid = {'tvec__ngram_range':[(1,2), (2,2)],\n",
    "                     'tvec__max_features': [1000, 3000, 5000],\n",
    "                     'tvec__max_df': [0.3, 0.4, 0.5, 0.7],\n",
    "                     'knn__n_neighbors': [3,5,7,9, 11, 13]}\n",
    "\n",
    "tvecknn_gs = GridSearchCV(tvecknn_pipe, param_grid=tvecknn_param_grid, n_jobs=-1, verbose=1)\n",
    "\n",
    "tvecknn_pred, tvecknn_proba  = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=tvecknn_gs, gs=True, \n",
    "                            title='KNeighborsClassifier + TF-IDF with Hyper parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 508,
   "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>TFIDF + KNN</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>278.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>96.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>25.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>205.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        TFIDF + KNN\n",
       "Accuracy                      0.800\n",
       "Misclassification Rate        0.200\n",
       "Sensitivity (Recall)          0.917\n",
       "Specificity                   0.681\n",
       "Precision                     0.743\n",
       "True Positive               278.000\n",
       "False Positive               96.000\n",
       "False Negative               25.000\n",
       "True Negative               205.000\n",
       "F1-Score                      0.821\n",
       "AUC Score                     0.903\n",
       "Matthews Correlation          0.616"
      ]
     },
     "execution_count": 508,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tvecknn_m = model_metrics(y_val, tvecknn_pred, tvecknn_proba, cm=True, title='TFIDF + KNN', labels=labels)\n",
    "tvecknn_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Support Vector Machines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 505,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 324 candidates, totalling 1620 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:    2.4s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   12.9s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:   33.2s\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:  1.1min\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:  1.8min\n",
      "[Parallel(n_jobs=-1)]: Done 1620 out of 1620 | elapsed:  2.5min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== SVC + CountVectorizer with Hyper parameters =================\n",
      "\n",
      "Best parameters  :  {'cvec__max_df': 7, 'cvec__max_features': 5000, 'svm__C': 3.0, 'svm__degree': 3, 'svm__kernel': 'rbf'}\n",
      "Best CV  score   :  0.6745345943255351\n",
      "Training score   : 0.9061990212071778\n",
      "Validation score : 0.6539735099337748\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Svm with CountVectorizer and hyper parameters\n",
    "cvecsvm_pipe = Pipeline([('cvec', CountVectorizer(stop_words=common_words)), ('svm' , SVC(probability=True))])\n",
    "cvecsvm_param_grid = {'cvec__max_features': [1000, 3000, 5000],\n",
    "                      'cvec__max_df': [1, 3, 5, 7],\n",
    "                      'svm__C': [1.0, 2.0, 3.0],\n",
    "                      'svm__kernel': ['rbf', 'linear', 'poly'],\n",
    "                      'svm__degree': [3,4,5]}\n",
    "    \n",
    "cvecsvm_gs = GridSearchCV(cvecsvm_pipe, param_grid=cvecsvm_param_grid, n_jobs=-1, verbose=1)\n",
    "\n",
    "cvecsvm_pred, cvecsvm_proba = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=cvecsvm_gs, gs=True, \n",
    "                            title='SVC + CountVectorizer with Hyper parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 509,
   "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>CountVec + SVC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.761</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>166.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>72.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>137.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>229.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.729</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.316</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CountVec + SVC\n",
       "Accuracy                         0.654\n",
       "Misclassification Rate           0.346\n",
       "Sensitivity (Recall)             0.548\n",
       "Specificity                      0.761\n",
       "Precision                        0.697\n",
       "True Positive                  166.000\n",
       "False Positive                  72.000\n",
       "False Negative                 137.000\n",
       "True Negative                  229.000\n",
       "F1-Score                         0.614\n",
       "AUC Score                        0.729\n",
       "Matthews Correlation             0.316"
      ]
     },
     "execution_count": 509,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cvecsvm_m = model_metrics(y_val, cvecsvm_pred, cvecsvm_proba, cm=True, title='CountVec + SVC', labels=labels)\n",
    "cvecsvm_m"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 506,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 648 candidates, totalling 3240 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 8 concurrent workers.\n",
      "[Parallel(n_jobs=-1)]: Done  34 tasks      | elapsed:   10.4s\n",
      "[Parallel(n_jobs=-1)]: Done 184 tasks      | elapsed:   48.7s\n",
      "[Parallel(n_jobs=-1)]: Done 434 tasks      | elapsed:  2.0min\n",
      "[Parallel(n_jobs=-1)]: Done 784 tasks      | elapsed:  3.5min\n",
      "[Parallel(n_jobs=-1)]: Done 1234 tasks      | elapsed:  5.6min\n",
      "[Parallel(n_jobs=-1)]: Done 1784 tasks      | elapsed:  8.2min\n",
      "[Parallel(n_jobs=-1)]: Done 2434 tasks      | elapsed: 11.2min\n",
      "[Parallel(n_jobs=-1)]: Done 3184 tasks      | elapsed: 14.9min\n",
      "[Parallel(n_jobs=-1)]: Done 3240 out of 3240 | elapsed: 15.2min finished\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======== SVC + TF-IDF with Hyper parameters =================\n",
      "\n",
      "Best parameters  :  {'svm__C': 3.0, 'svm__degree': 3, 'svm__kernel': 'rbf', 'tvec__max_df': 0.3, 'tvec__max_features': 5000, 'tvec__ngram_range': (1, 2)}\n",
      "Best CV  score   :  0.8768176538908247\n",
      "Training score   : 0.99836867862969\n",
      "Validation score : 0.8443708609271523\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#SVM with TF-IDF with Hyper Parameters\n",
    "tvecsvm_pipe = Pipeline([('tvec', TfidfVectorizer(stop_words=common_words)), ('svm' , SVC(probability=True))])\n",
    "\n",
    "tvecsvm_param_grid = {'tvec__ngram_range':[(1,2), (2,2)],\n",
    "                      'tvec__max_features': [1000, 3000, 5000],\n",
    "                      'tvec__max_df': [0.3, 0.4, 0.5, 0.7],\n",
    "                      'svm__C': [1.0, 2.0, 3.0],\n",
    "                      'svm__kernel': ['rbf', 'linear', 'poly'],\n",
    "                      'svm__degree': [3,4,5]}\n",
    "\n",
    "tvecsvm_gs = GridSearchCV(tvecsvm_pipe, param_grid=tvecsvm_param_grid, n_jobs=-1, verbose=1)\n",
    "\n",
    "tvecsvm_pred, tvecsvm_proba  = classifiermodel(X_train, y_train, X_val, y_val, \n",
    "                            model=tvecsvm_gs, gs=True, \n",
    "                            title='SVC + TF-IDF with Hyper parameters')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 512,
   "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>TFIDF + SVC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>244.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>35.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>59.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>266.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        TFIDF + SVC\n",
       "Accuracy                      0.844\n",
       "Misclassification Rate        0.156\n",
       "Sensitivity (Recall)          0.805\n",
       "Specificity                   0.884\n",
       "Precision                     0.875\n",
       "True Positive               244.000\n",
       "False Positive               35.000\n",
       "False Negative               59.000\n",
       "True Negative               266.000\n",
       "F1-Score                      0.838\n",
       "AUC Score                     0.924\n",
       "Matthews Correlation          0.691"
      ]
     },
     "execution_count": 512,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "tvecsvm_m = model_metrics(y_val, tvecsvm_pred, tvecsvm_proba, cm=True, title='TFIDF + SVC', labels=labels)\n",
    "tvecsvm_m"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of Performance of Models:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 511,
   "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>CountVec + LogReg</th>\n",
       "      <th>TFIDF + LogReg</th>\n",
       "      <th>CountVec + NaiveBayes</th>\n",
       "      <th>TFIDF + NaiveBayes</th>\n",
       "      <th>CountVec + KNN</th>\n",
       "      <th>TFIDF + KNN</th>\n",
       "      <th>CountVec + SVC</th>\n",
       "      <th>CountVec + SVC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Accuracy</th>\n",
       "      <td>0.694</td>\n",
       "      <td>0.851</td>\n",
       "      <td>0.690</td>\n",
       "      <td>0.844</td>\n",
       "      <td>0.579</td>\n",
       "      <td>0.800</td>\n",
       "      <td>0.654</td>\n",
       "      <td>0.844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Misclassification Rate</th>\n",
       "      <td>0.306</td>\n",
       "      <td>0.149</td>\n",
       "      <td>0.310</td>\n",
       "      <td>0.156</td>\n",
       "      <td>0.421</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.346</td>\n",
       "      <td>0.156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sensitivity (Recall)</th>\n",
       "      <td>0.779</td>\n",
       "      <td>0.825</td>\n",
       "      <td>0.752</td>\n",
       "      <td>0.802</td>\n",
       "      <td>0.785</td>\n",
       "      <td>0.917</td>\n",
       "      <td>0.548</td>\n",
       "      <td>0.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Specificity</th>\n",
       "      <td>0.608</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.628</td>\n",
       "      <td>0.887</td>\n",
       "      <td>0.372</td>\n",
       "      <td>0.681</td>\n",
       "      <td>0.761</td>\n",
       "      <td>0.884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Precision</th>\n",
       "      <td>0.667</td>\n",
       "      <td>0.871</td>\n",
       "      <td>0.671</td>\n",
       "      <td>0.877</td>\n",
       "      <td>0.557</td>\n",
       "      <td>0.743</td>\n",
       "      <td>0.697</td>\n",
       "      <td>0.875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Positive</th>\n",
       "      <td>236.000</td>\n",
       "      <td>250.000</td>\n",
       "      <td>228.000</td>\n",
       "      <td>243.000</td>\n",
       "      <td>238.000</td>\n",
       "      <td>278.000</td>\n",
       "      <td>166.000</td>\n",
       "      <td>244.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Positive</th>\n",
       "      <td>118.000</td>\n",
       "      <td>37.000</td>\n",
       "      <td>112.000</td>\n",
       "      <td>34.000</td>\n",
       "      <td>189.000</td>\n",
       "      <td>96.000</td>\n",
       "      <td>72.000</td>\n",
       "      <td>35.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>False Negative</th>\n",
       "      <td>67.000</td>\n",
       "      <td>53.000</td>\n",
       "      <td>75.000</td>\n",
       "      <td>60.000</td>\n",
       "      <td>65.000</td>\n",
       "      <td>25.000</td>\n",
       "      <td>137.000</td>\n",
       "      <td>59.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>True Negative</th>\n",
       "      <td>183.000</td>\n",
       "      <td>264.000</td>\n",
       "      <td>189.000</td>\n",
       "      <td>267.000</td>\n",
       "      <td>112.000</td>\n",
       "      <td>205.000</td>\n",
       "      <td>229.000</td>\n",
       "      <td>266.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F1-Score</th>\n",
       "      <td>0.718</td>\n",
       "      <td>0.847</td>\n",
       "      <td>0.709</td>\n",
       "      <td>0.838</td>\n",
       "      <td>0.652</td>\n",
       "      <td>0.821</td>\n",
       "      <td>0.614</td>\n",
       "      <td>0.838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AUC Score</th>\n",
       "      <td>0.772</td>\n",
       "      <td>0.929</td>\n",
       "      <td>0.762</td>\n",
       "      <td>0.933</td>\n",
       "      <td>0.602</td>\n",
       "      <td>0.903</td>\n",
       "      <td>0.729</td>\n",
       "      <td>0.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Matthews Correlation</th>\n",
       "      <td>0.393</td>\n",
       "      <td>0.703</td>\n",
       "      <td>0.383</td>\n",
       "      <td>0.691</td>\n",
       "      <td>0.173</td>\n",
       "      <td>0.616</td>\n",
       "      <td>0.316</td>\n",
       "      <td>0.691</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        CountVec + LogReg  TFIDF + LogReg  \\\n",
       "Accuracy                            0.694           0.851   \n",
       "Misclassification Rate              0.306           0.149   \n",
       "Sensitivity (Recall)                0.779           0.825   \n",
       "Specificity                         0.608           0.877   \n",
       "Precision                           0.667           0.871   \n",
       "True Positive                     236.000         250.000   \n",
       "False Positive                    118.000          37.000   \n",
       "False Negative                     67.000          53.000   \n",
       "True Negative                     183.000         264.000   \n",
       "F1-Score                            0.718           0.847   \n",
       "AUC Score                           0.772           0.929   \n",
       "Matthews Correlation                0.393           0.703   \n",
       "\n",
       "                        CountVec + NaiveBayes  TFIDF + NaiveBayes  \\\n",
       "Accuracy                                0.690               0.844   \n",
       "Misclassification Rate                  0.310               0.156   \n",
       "Sensitivity (Recall)                    0.752               0.802   \n",
       "Specificity                             0.628               0.887   \n",
       "Precision                               0.671               0.877   \n",
       "True Positive                         228.000             243.000   \n",
       "False Positive                        112.000              34.000   \n",
       "False Negative                         75.000              60.000   \n",
       "True Negative                         189.000             267.000   \n",
       "F1-Score                                0.709               0.838   \n",
       "AUC Score                               0.762               0.933   \n",
       "Matthews Correlation                    0.383               0.691   \n",
       "\n",
       "                        CountVec + KNN  TFIDF + KNN  CountVec + SVC  \\\n",
       "Accuracy                         0.579        0.800           0.654   \n",
       "Misclassification Rate           0.421        0.200           0.346   \n",
       "Sensitivity (Recall)             0.785        0.917           0.548   \n",
       "Specificity                      0.372        0.681           0.761   \n",
       "Precision                        0.557        0.743           0.697   \n",
       "True Positive                  238.000      278.000         166.000   \n",
       "False Positive                 189.000       96.000          72.000   \n",
       "False Negative                  65.000       25.000         137.000   \n",
       "True Negative                  112.000      205.000         229.000   \n",
       "F1-Score                         0.652        0.821           0.614   \n",
       "AUC Score                        0.602        0.903           0.729   \n",
       "Matthews Correlation             0.173        0.616           0.316   \n",
       "\n",
       "                        CountVec + SVC  \n",
       "Accuracy                         0.844  \n",
       "Misclassification Rate           0.156  \n",
       "Sensitivity (Recall)             0.805  \n",
       "Specificity                      0.884  \n",
       "Precision                        0.875  \n",
       "True Positive                  244.000  \n",
       "False Positive                  35.000  \n",
       "False Negative                  59.000  \n",
       "True Negative                  266.000  \n",
       "F1-Score                         0.838  \n",
       "AUC Score                        0.924  \n",
       "Matthews Correlation             0.691  "
      ]
     },
     "execution_count": 511,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst_metrics = [cveclr_m, tveclr_m, cvecnb_m, tvecnb_m, cvecknn_m, tvecknn_m, cvecsvm_m, tvecsvm_m]\n",
    "combine_metrics = pd.concat((items for items in lst_metrics), axis=1, join='inner')\n",
    "combine_metrics.reset_index().rename(columns={combine_metrics.index.name:'metrics'})  \n",
    "\n",
    "combine_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|Model|Best Score|Acurracy on Training Data|Accuracy on Validation|MCC|Best Param|\n",
    "|:------|:-----|:-----|:-------|:-------|:---|\n",
    "|Logistic Regression w CountVectorizer|0.694|0.850|0.694|0.393|'cvec__max_df': 7, 'cvec__max_features': 5000, 'lr__C': 0.01, 'lr__penalty': 'l2', 'lr__solver': 'liblinear'|\n",
    "|Logistic Regression w TF-IDF|0.883|0.954|0.851|0.703|'lr__C': 1.0, 'lr__penalty': 'l2', 'lr__solver': 'liblinear', 'tvec__max_df': 0.3, 'tvec__max_features': 3000, 'tvec__ngram_range': (1, 2)| \n",
    "|Naive Bayes w CountVectorizer|0.706|0.864|0.690|0.383|'cvec__max_df': 7, 'cvec__max_features': 5000|\n",
    "|Naive Bayes w TF-IDF|0.885|0.952|0.844|0.691|'tvec__max_df': 0.3, 'tvec__max_features': 3000, 'tvec__ngram_range': (1, 2)|\n",
    "|KNN w CountVectorizer|0.559|0.657|0.579|0.196|'cvec__max_df': 7, 'cvec__max_features': 1000, 'knn__n_neighbors': 7|\n",
    "|KNN w TF-IDF|0.813|0.843|0.800|0.616|'knn__n_neighbors': 13, 'tvec__max_df': 0.3, 'tvec__max_features': 5000, 'tvec__ngram_range': (1, 2)|\n",
    "|SVM w CountVectorizer|0.675|0.906|0.654|0.316|'cvec__max_df': 7, 'cvec__max_features': 5000, 'svm__C': 3.0, 'svm__degree': 3, 'svm__kernel': 'rbf'|\n",
    "|SVM w TF-IDF|0.877|0.998|0.844|0.691|'svm__C': 3.0, 'svm__degree': 3, 'svm__kernel': 'rbf', 'tvec__max_df': 0.3, 'tvec__max_features': 5000, 'tvec__ngram_range': (1, 2)|"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Observations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "The model that performs best here is the **Logistic Regression w TF-IDF** with the following parameters\n",
    "    \n",
    "- 'lr__C': 1.0, \n",
    "- 'lr__penalty': 'l2', \n",
    "- 'lr__solver': 'liblinear'\n",
    "- 'tvec__max_df': 0.3\n",
    "- 'tvec__max_features': 3000, \n",
    "- 'tvec__ngram_range': (1, 2)\n",
    "        \n",
    "which gives us:\n",
    "- **0.851** accuracy score on Validation data \n",
    "- **0.703** on MCC score onn Validation data\n",
    "</font>\t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 341,
   "metadata": {},
   "outputs": [],
   "source": [
    "#running the model with the best parameters and find the coefficients \n",
    "tvec = TfidfVectorizer(stop_words=common_words, max_df=0.3, max_features=3000,ngram_range=(1, 2))\n",
    "\n",
    "# Fit TF-IDF on the training data and transform training data.\n",
    "X_train_tvec = tvec.fit_transform(X_train)\n",
    "# Transform our testing data with the already-fit TFIDF\n",
    "X_val_tvec = tvec.transform(X_val)\n",
    "\n",
    "final = LogisticRegression(C=1, solver='liblinear', penalty='l2')\n",
    "\n",
    "final.fit(X_train_tvec, y_train)\n",
    "predictions = final.predict(X_val_tvec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Top Predictors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 343,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_coefficients(model, feature_names, top_features=20):\n",
    "    \"\"\"\n",
    "    Function to plot the top coefficient for the linear model\n",
    "    \n",
    "    Takes in arguements:\n",
    "    - model - the linear model which has the coef_ arguement\n",
    "    - feature_names - features that goes into the model\n",
    "    - top_features - number of features that has the most influence of the outcome\n",
    "    \"\"\"\n",
    "    \n",
    "    coef = model.coef_.ravel()\n",
    "\n",
    "    top_positive_coefficients = np.argsort(coef)[-top_features:]\n",
    "    \n",
    "    top_negative_coefficients = np.argsort(coef)[:top_features]\n",
    "    \n",
    "    top_coefficients = np.hstack([top_negative_coefficients, top_positive_coefficients])\n",
    "\n",
    "    # create plot\n",
    "    plt.figure(figsize=(15, 7))\n",
    "    colors = ['green' if c < 0 else 'maroon' for c in coef[top_coefficients]]\n",
    "    plt.bar(np.arange(2 * top_features), coef[top_coefficients], color=colors)\n",
    "    feature_names = np.array(feature_names)\n",
    "    plt.xticks(np.arange(0, 2 * top_features), feature_names[top_coefficients], rotation=45, ha='right')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSZIkdYhBmiRJkiR1iEGaJEmSJHWIQZokSZIkdYhBmiRJkiR1iEGaJEmSJHWIQZokSZIkdYhBmiRJkiR1iEGaJEmSJHWIQZokSZIkdYhBmiRJkiR1iEGaJEmSJHWIQZokSZIkdYhBmiRJkiR1yIpznQHNrnJ+messSJIkSZqEM2mSJEmS1CEGaZIkSZLUIQZpkiRJktQh7klbBriPTJIkSZo/DNJmiIGVJEmSpOlwuaMkSZIkdchQQVpEXBIR90XEtyPioxGxzojyJUmSJEnz0rAzabcDDy2lPBz4IfCK4bMkSZIkSfPXUEFaKeXvSil/rg/vBLYYPkuSJEmSNH+Nck/accCtI0xPkiRJkuadKe/uGBF3AJuM86tzSykfq39zLvBn4P2TpLMEWAKw1VZbTSuzkiRJkrS8mzJIK6XsNdnvI+IYYH/gqaWUCe87X0q5CrgKYPHixd6fXpIkSZLGMdT3pEXEPsDLgCeWUv57NFmSJEmSpPlr2D1pVwBrArdHxLci4h0jyJMkSZIkzVtDzaSVUrYdVUYkSZIkSaO9u6MkSZIkaUgGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhK851BrqknF/mOguSJEmS5jln0iRJkiSpQwzSJEmSJKlDDNIkSZIkqUMM0iRJkiSpQwzSJEmSJKlDDNIkSZIkqUMM0iRJkiSpQwzSJEmSJKlDDNIkSZIkqUMM0iRJkiSpQwzSJEmSJKlDDNIkSZIkqUMM0iRJkiSpQwzSJEmSJKlDDNIkSZIkqUMM0iRJkiSpQ6KUMvtvGvEb4Gez/sbD2wD4V9MyrRlKa9TpmZZpzXR6pmVaM5nWqNMzLdOa6fRMa/lIazZtXUrZcLxfzEmQtqyKiLtKKYtNy7RmIq1Rp2dapjXT6ZmWac1kWqNOz7RMa6bTM63lI62ucLmjJEmSJHWIQZokSZIkdYhB2mCuMi3TmsG0Rp2eaZnWTKdnWqY1k2mNOj3TMq2ZTs+0lo+0OsE9aZIkSZLUIc6kSZIkSVKHGKRJkiRJUocYpM2AiFh1rvMgSZo91vuSNLMiIuY6D7PJIG3EIuJBwPsjYru5zkvTslKwh81nRHS2TPeObQTHOJJ0Rm3U+RnlcUbE1sPnaMK0O/s5dCVvEbFBRCxX31/TFBHrABdHxNpznZexulonRsQmEfHQEaY3krIeESuPIp1Ri4gVZjj9TtQVPRGxY0QsHDKNUbdJj4qIbUeQzgPatq6c/4jYfVTtZUQ8ZFR94Yh4dESsW0opXTlXs6GTlfdsmoEP+4/A94ALI+LBo0gwIlaLiBXrzwN/ZhERpd4hJiIeP6p8jXmPocpSrzKuF+C00yql3F/TO2CUHaZhR8nH5GXaHZPmZwmMZOR+FI1/RKwE7FV/PjUi9h4yvbUax7nFkGltBBwdEWsNk8446a4E0yuz0TDO74atlzfqpTudBm3Unfqa3hLgmIh4zBDpdK5D03A/8EZg3YjYc5iEJigT0zreiFgXOLn+fFhEPH+IfK0fEcfWn58dEQcPkdbKwNOBP0fEatNNp5Fes43bebqBVkSsDjwrIjaKiLMi4qwh8rRq4+djI+LEIdJaGzim/nxARBwx3bRqGhtFxCb158dFxKqlQ3eRi4hDgVcCQ7VNjTLxsGHLWS1TOwN/iIg1h0in2YZvNEw7Mk7ae0TErsOkAewD3BIRWw2ZlzWA3YHfjagvdjJwe0SsM58CtXkdpI2p2I+LiNdFxF61YRs0rQUApZR/Bt5GBmpvGDYgioiXAtcAN0TEJqWU+wftVDeO8UzgEuAvY95juh2Ah9QGcdOar+mmcwBwVURcGxErTCetiHhkTafXeX4JQ1bwvTxExA7AmRGx5TTT2RE4HrgsIk4F3hERa03nfDU+yyXA30bESyJi/2nma42a5l8i4rERcVREbBp1QGBAKwDPjIgvkR3y+6aTp5qvFYCjIuKUepxXR8TC6ZyviNislPJrsgO9bUS8bLr5GpPu6cAVEXEl5ODAgA3sNqWKiCURcXHtFG49jbSa+ToJeDfw2oh4ec1b685XRCxsDHQcUTvjT5tOXmoaGwFbA+8BfgccHBG7TyetRtl/AfC6iLgoIrYcQcfmabVzs/N00yil/Ecp5ZfAAcBrIuJx08zLio3jfFzkyP3q0+3AlVJ+B2wVEf8OnAV8cDr5qmn9G/D0iPg5cBrw90Ok9b/A+4FfAW+a7vlqpNds484DNho0jVpX/BewKfAl4Gjgo9PJT2333xgRO9WnNgZ+Os20FpRS/h1YLyJ+AbwauGk6aTUsAt4bEZcAL2aaA34RsXHj5/0j4oURsUMMMSBWA9BHA5fVz2M6aWzb+PkxwEXAUEFoo8yuAXw8InaZZjq9snoieT3+TURcXn83TD9qO+Ac4E/TeX0jf68GbgA+FEPMqJVS/lBKuZo8X2+dbvDY6++WUp4P/By4LubRjNq8DtIaF8uBZMdyNeA5wAsjYsO26dRg768zOMD/Au8EvsUQgVpEbAHsC7wW+AlwZw2I/tImUIuI9Rs/7wM8F3hiKeWnNcDaAwbrxDXSeypwK/Cymq/HTnNGYSOy0XkfsJAcKRnoAqznYmfgxRGxfynlTzWtFYfpwNU87AW8ngyyjo5cztpaff2byfKwJ9lYHF1K+Q+mef1FjmYfDVxKfqa7TSONNYF3RsTetfJ8N/C8mua+MeAyk1LK/5AV+8bA14Ff1GB54JmZUspfgGvJkdTXkOfrf4BBA/cNyED29NrAPgjYoQbK0xYRzwCOIhvsnSLijprvKYOrSKsCX4uIl0fEI8nP8pdk5/AjEbFtrz4ZMF+HAkcAJwK7ANsM8NqIHIz4YkSsW+uxC4FHkAMUp0wjPysBR5L112rAleQA0UGDBmq98xoRRwFnAJ8hZ6RPJzt00xIRLyE7NoeSdfVjp5nObrVuvhz4EPCKiHjCgGksJoOfXrD9AfL4bo0cPR4oeG/Un+8F/hlYr5Ty3/V3K47zdxOm0/ibK8j2bUGtw5ZKa8B8PQY4HPgn4IgYYpa1pvts4DDghFLKLyJni1rNntQO7sH1/N4E/BD4NfC/EbHKNLKzFvAH4ISI2IasuwYOXCLiEcBb6sOvkyt1/lxK+WP9/QqNv52yfuz9TSnla8Bvybri3aWU3/bq6wHytgU5WLJfDaxeAzwRuJg87k0GTK+X/92AFwCbjHm+VRqRM14fjQxAIcvXb8jPsrciqfUAbvP9a73872T9c15EPKxtOmPS3A84BTgV+FtgzYj4QH2Pgfpj9Zh3Ar4GfKGU8t1By+zYc1xKuRC4HfhgDBGoRcQzyRnznwAviIiHD5pG7Q8QEUcD/xfYCfjsoP3EZVYpZd79AzZp/HwgWbi3aDy+jJyJ2WjAdE8mZ9AeVB9vDryKHC3ZfsC0ngWcAJzTeO6NZGHffIrXRn3vjwML63OPAq4mA6LXA/8A/B1wwDTO385kh2uP+vh44B+BXQZMZ3fyAn5l47l3k5XDei3T6H3X39pkwPJJ4OB6jCsBq9TfrzmN43wEOSP0cDKIfxvwil5ZmeK1C+r7X1HzsmN97R3kzOiq0yy7K5GB8U5k5/52YMX6u00HSGdNsiG8iQy2d6rPn1XzfOAgeQQ2IIOMDetn+NZefoAtB/ks688bkp3ne4GXT/NcLQSeRo6GP7c+tz/ZKJ4+zTSfRnaej2089xng0y1fv6D+vxPwf4C7gcc3fv8y4LpBywcZHB9BDgQcW6/tlervHjJAOlcBd1FnHutzTwRuA06exvnaBXg58A5g25rPC8mO3GNavH4vYKv684rA24Ej6uPVaz6vnuZnuQPw8frz64Gb63W7cJDyWj/LT5PtSK/Mn1rTe9IA+XkEcCc5E3QFtZ4nB3m+BqzTLENT5av+vAqwQv35b8n2qdcmPKztMdafn0wGxKvV4/37xu+2G/DcP7ReN9uQszpn1zKy2wBpxJjHLwLeRHbyX13T/wJT1P3A3uRAwgbAQ8jO88J6Ld5MbdeAXQc8X7uQgctl5IDOK4EtgQcDewArtzzOTYDFwDpkvf3aelwb1t+3ur7H5G094PnAueT1vlvjdysOkK9TySDyDmD9+vzT6+ew93if0yTp7dQoqy+rZX6zAdPo1a9bkkHta8nl8pc1z3fvfVqkt2Lj58eT1+iqZD3xCuBjwENbpLPKmMf7Aef18gKsSw7u7D7IdTQmzevImelevd/2GJvlYldg58bj1wJfpdbBA+bnaWR/bHWyrj2X7DdOWe+Mk9YTyL7YRvXcv6d+vusOUj6WxX9znoFZPdgMXjboFZz63OOA/wAubPzdAWSDdgZTNIiN1+xcK5Utxzy/BdkBuKZ38bRI6yDg22QDcQewX+N3VwLfqRf2pAWTrNCfTs7GrU6Omt1cj3ljsjNw8IDncHtymcud5NrlXqV6bq0k2lamjyEDzo/X43l243c3AF9kisZiTOWyFdmIPZcMQO8HbgQ+RwbJ19O+Uex1vp4F3Nh4fu963OcyRaBGv0O1JfB74J8bv/s08MH685HAs9ocY+O5k8gO/h2N504lA8lW5bW+ZrV6vn5Jjj73rpEzyUDrmW0+T7JT8ykyiD2plrUba7m/gAy01hrgszwFOKt3HoF7gIvr48OAxQMc42Hk4MRd9AO1A8jO/ssGLPtrkAH7J8jrcJvG7+4Cbm57jPXxZsC/AO9qPLcj2QC1amDra44jB4MOB/6VHE3t/e5EMiiasOzXz3xB4/GrgF8AT6mPVyIDtTuB4we8hnp17nksHai9hgw+HjtFOi8gO/Fr1Mdn1df1gqGFZH20cYs8rdD4eT1gfbJevgy4hf6Azn7UDmeLNPcn65uzgI8AX6E/4Pdi8lpfd7LrqB5Db6BlMRlg30ENkuvzbyJnd9Ye4Do6nWzH3ky/vXsnWd8uIZf0tT3O0+rrHtR47lM1n0fV/yfNW+N1DwHeRS5na5b7s8hZvyk7qmOO8+lkO7sDOeD09zVPO9Qyt+Mk6WwJfIMc5NuUrNv/BjiocS306rJf0hjgnSJPvfL/YHJA4rvkjM4ltUx8mUkGW3ngNXkrOSDXK6OXkv2N08j+TKtBzfral5Jt9dr18Rnkqp9FwFNqmpOV1+ZxHkIOCv0f4EWN588Frh0gT6eQQfUlwJvrc28mr60pB0XHydcCcqDvXrIP8Dlytc7l9fN9JVPUseS1eGb9+aT6+b2vprVWfY+zgc/SCGzGSWdtsq+0dT23h5EBzC9pBP71M3lq23NWX7Mt8OjG42tZeiCmVcBd//Z0sv66Cri18fyrgR/QcrC1vuYp5GBmc5LhYWRgew1TDCyMLX/ktfxeGsFuvYa+S+1rLa//5jwDs3qw/VGWNciK/Xn18eOBbwJnNP52XyaZSZugEH288bjX6C4kO5obtMzj8fVC2wBYmRxRupSlA7VB8nUM2eF6wphzcDhZMbee4SMbsfPIDsOt5ExHb5T7IFqOaJMdvkuAx9aK7niyM/Gsxt88fIB8vYhsmNet/w6v+Tu1VpDrAVu3SKfXsPYawq3IWZj9G39zJdnROWCC8x1kZXwDGaxsRVbu3wWOavzdbWTAfN9UFVb9+/3JDvcaZKD8vnr+F5IzKN+izoa1PMankqNmK5Md4U/RCNjJhrxNvg4nG60tyEbmhvr8OvRHzgb5LJfQmNmuz61fn7ue7AxM2Oka51r6Bnktv7qe72Pq7w4lOwHrtkzrkeRI59bkiO91ZMdiUeNvJhxtZOkOxGlk5/E0cjb5D2SnYWUyaP7mAPl6BtmBXFQfX0JtBMng7ZtMMtI7Jl9HAofUn99IBmW9keyVyXqy9Ygq2YH4FDmrvwHZ4X0H2XHdhOy87jDBa3ejP9CxI7lMctv62qtrmd2ODLi/xNSzJUEGAccCzyYHIdYkr+VvUDvftczcRfv6+l3AM+vPq5LB5xcb6W0zxetXI2f+H0J2+J5DdmZuJztNazf+9uKp0mv87cnA58mR538mA43t6+9eSXbE2s7APLGWo7Xr4z16ZYqcRXn/ZGVsTFprkp3na8jBjoc3frczOevaKq3GtfTt8colGUB8l0lWGJBt2o9qubyiltMl9edn1b85tOZrso548zo6k2zDLyfrwY2B88m6sDc72na26vH0BySuI6+nXgf8FeRKiEFmyo+v10svzTXJGeoXkKthvkn7+vXI+v7bkkHMu+jPch9B1tertEhnn1pW1yQ79tc3fvd2cgBgkEGrk+kHepvUtL9KDk4/sx7r1i3SmWhm+y31+V6gdiaTBJJk3Xl2fc336a+0OpEcqD4EeCE5M7RogOM8oJb9D5Izeovq8+8g+xxTnvtGWs+q5WLVerx/AL7Z+P05k+WNB/aDHkrWC9c3yxPZ5ziL9oMdC8k6cq36Oe49pix/frJzvzz8m/MMzNqBNqJtskP5eHIU+7D63GPrRXROi7SahWhvcnR9ATnq84rG75bUC6btbNzDyUb+fmCfRl5fVtOZdPnAmHwdTZ2dIkcVf0x/ZPwp9YIcaNqZnL17fr1g30h2zm8gG6AvUkcfp0hjJbIS/U/6o5Wb10rqOhozai3z9EzGzGCSDeNz6uex34DpPZ2cETqFXN5zFvAGMhDcvR7zRcAHJnh9Lzhfv75+x/p4F3JG6OTG3z6OCToQYz7L48jK+CPkaOMjyOWIb6r5uX2Qz5KcKfgh8PT6eHWywb15kPNPBoyHkEtpl5AzAL3j36Z5PlqktYCskG8kG58NySD7GjJwWZUMSAYJEs6jH0yvQzZEXyD3uEGdnWmZ1kPJIONqcvR9l5q3l9KiwW+k0+s8b0HuCbmonsN/reXjfNoF273z9Z36ui3r8zuSHYJbyca7bef5ZDLQbzaobyHridYjqGPS3ID+MsJN6+NzyRHR7Zh8du89ZLC0Tn18Adnx2JSchX8L/RmnXQbIzx/Jer9XTnvL2/+OXNrzHdoNTvTy9V6WXrb0CHI0+pNMsVy+8ZkdUz/D79NfdvzIemxn0DJgb5SLNcj2YvP6+lvJDvQXGulPuKqDB3a4HkwOFF5ODgLcRdZFvfZo0qW59AeGdiCDl4eSA2dX1fLeXF61+gDHOjZ43J0MINeun+v3J/ssG/l6C7nPrjd7vxoZdLyVbEcHCRCeRLY7e9fXf5UcONy8vs8byE77VKtgFtS/u5EMVDauz3+glq1eoNZqaW4j3ZeRbeOTyZneO8kANMjBgc1apvNYMkDrBetbkG3Ad8nr/dNM0CaNU772IgOnF5LXYW+QdNf6f+ttJ/QHubZqPLcRGQC9pmUabWa230wOsE64QoSl2/CHk/Xre8k6uvf5HU1em+9lsMGJx5DX4cb18/w9GRD1AsD3AHu2yVvj2tyU3GJzS33uG8A9LfIytj+8JznotFbNxzk0JgNov6Lp9FqWPkv2WZ5az+H5ZP/zdgbY4rGs/pvzDMzKQWYF9ML64Z4EfLE+vy85Mt8LZvasBWK9sYV4gnTPIPeU9EYoH0VWqjeRHaW7J6qoxknrKcDnGun+jjrKSM7GnNm2sqoX2r1jKpXn1UplLzJQarXMpb52D+DI+vMK5CjZK8nZiVvJUek2S1S2IYOKIDu8d9Ofidui5nvQwPHZ1GUWNDrdZIfsMFp0MOlXyE8iG/bHk43NBTXPzyRnUW4hO2FPIoPTVcekswF5B6/16uOX13O+e+Mz/jp1CcVk5bXx8zrUTdT18UVk53vX+ngtWnZs6nnfiOw4PKo+94haJh5MNhifJiv+qfa9nFzL5EnkcuHm0svjyc7IpCN5Y46zt2z22HqObiGX6Z1Eduam6tSMtyz05eSSiF6DuGlN90baL81q7hfbiezkvLeex8U1b21nvdYiG+RNyQboNrLT9S76o/5t9+/1lq+tTc4iv3nM71eiRYBcy8TGZMevF1g3l5RcWdMfpKParHfWJ+vdT9ZztjFZb4zbIWHpJYkfreWxt+/gleQSoV6dsQmDLfNavx7PL2nsSSSXnR5KdvC2neT1zT1obySXh21P1hm95cKPrb97N3WGbYK0Nqpl50Qy6L+VrFMeRT/o2IXsiJ00Wfln/OtoBbKj9LnG737FFNflmLS2JmdJ1iIH564k96StAbwOOHGAc78vWW/dSw7GPaKeg78hZwjb7OtpEzzeQLYJD2aCwZNx0tmPXBHwe+peU7Kj/uJ6vibriG9PfyDuAHJ28NjG7y8l66D1anndsG1Zrf+vR3Z030o/ULsFuGm8YxlzXT+gDifb7g+T7fBRZNtyNVP0BxplfwE58/YiauBD7XST19eLyGB03HqMrJd6y86XkNfcTuRd++5q/N2JTGPvdj3fz2+811/3a9dj3niic1b/bpCZ7b+uYJjiOtqCrGNWJ2c/r6C/n3+9ms+B9lTV41lMLpv8Gnmtfrx+JjuNl48J8rYJS9+j4e30V1OcTQZqkw6MNspGb6DvUnJ2+hhygOJd5ADYhHXrOGkeQvZTtifrjp+Qq4m2reXmEiaZ2V6e/s15Bmb8AJcukD8iR6+bsy77krMKvaWPbTeNP45sFFatjx9NLvlbtxbuk2kxIl5fe3S9uJ7ceO5MslF9ZH08YSdpzDEuJDs2u9THzc2yx9WLbtCKb796kRxeH69EBgu3ko3YJ+pFM25F37iIF5INfS+ovaxeiA/oHE51nI3njiE7t831+0eRgdRUHftt6HeIViFHfJ5MdpSaAeSa9f9V67n4FhOM3pMN9X30O5ankKPkj6mP9yZHtNeZ4Hian+UZ5Mj+D4DXNZ5/LTmY0HoZYbNsk52kXrBxcz1/x5Adu0lvSlNffwLZMPSWf/TKwFa13N/DYEtwjiYDsoPJhuchjfN3eD1fEwaiY87ZM+t524/s/PZu3LN2/d27Jyqn46R7CjmrfV3juYeRjeFNZAPXeklJo5ztAny2Pl5A3i3sFFrO7JEdqxvJa24/smPxZeCSlq8fb4nuB8llR82N8g+r/0/ZsWTpTtx3gPMbv9uQ7Dz3BgCm3Jtby8Q7yBUA32mUh3PIpY+D3ojp1FpOX1bL16+pN6Spx912GeHe5NKin5EBxo7kEp6f1Ovpn2oZeT01cJsgndXIYOJysq5fpV477wKeVv9mfXLEvNXsceM6ejbZMdyiXju71XJy3URpjVMmXkqujvgeWd80byzxHLK9Gnep6jhpP5isw3YkZ7teQgYGO5Od13dP9XnSPni8CHhhy3QOqa9/dH38ZHLAqTcguQqT7Hchl+edRrb5q9R83V7L7TqNv3sHuQJi3IEvHrj/7KH1/OxSH69LDuZ8mP7NQqa6eVhzwPKE+nn29letRb/dewZZl7ddfrZx4+eja/l9Lv1gaDOm3jd5BfVu1fSXXZ5O1kFH1/xOOcA9tszW586q5XVh47mDyLZgqoHHmZjZPr2WiWvJwZuVyHr7UjIguofBBswfR10BUx9fAJzduC6/Qvvlqi8h27Ivk8HjamTQ/fp6Dm9m8q0129C/ScmW5KqL3s2mtiMHw/YhA60rBzzOo1m6z/MYcttOqzpnefo35xmYtQPNDtUZ5FKEt7D0iO3TyY73WuNd+BOktzm5n+Fd5LKzz5IjXIdPI29bksHj28c8fw7ZSVllogqGpSvQ55MjlFfRmAGov+vNnEx6A4dJ8rhvrVB6a873rJXqhuRo0A1M0Jlj6ZGaNwAfbjx+a0134WTnfsxxHkrOjO5SH19Gdhp2I0edv9+moiJnkH5HvwN4FNkxuZt+AHIocErjNWcyRfBdz9WPG+meRgb0j6uPV2uRtz3ITt/O5DK9m4BTG79/JQOsxSY7SreSs337kZ2Z3nLHY+p7TTlbQgaqN5Od1Q3q+b6QHAm9lpxxHCRAO4G8Jg8kO7i9GYkVyEGF79J+yd6LyKD/jeQexxvJyv0ysqH8Oi0DWzLYfhPZ2HwNeO+YMvwWWi4NGift7Wo5exjZSbqOlksmyZHwe2vZPame7+eRHcYfARdM8frmdbSoUc4vJAPaRfXx4eSStnUGOK7Hk43y8eQKheZ18xJyZuxRLfL1WHJ/TPNOXt+gv8TwTAbbS3tyPd9bkNf768mA/fe1zP51n8gU6exQy+O29VjfUP9tRnakdyY76k8g67QH5HHMca5OBgrvpD/6/+Jadq8iO7JtBxR619EBveuIDFrOJGds72WSeot+J3sBWY9+iZwx2bge40W1jD2J3Bc1yPLqRwKfbzzengx0P1jPaat6vz5uEzy2qftPJjunp5Btb29Z9JPIgZnntLmOyABrR7KO2Zpsy28jBwPWafz9ZJ3dZkB1ZP3Mzifbxt5qms3I28ifx9Q3vDiQuj+crBM/T7bX95L7hHp5P74+17Z+PYVc+ncJ/b29x9V8Pr9Fvnrv+1DyOvps43eb1bJ7PRn4TZonlr6OnkwOfG1GDph8kryudiBX03ydKVYoMKKZbZYOtg8m+4VrU5dn9z7v+jlfzGDXUW/p5f2N8noYOUP48vo579EyrYOB2+rP1wIfrT/vWsvfDZPljWz7r6jldGWyvf5ws5yT7dJr6s/jDmYy8YzvvmR70VzV8TfMk9mzpc7FXGdgVg4yRx8uonYea6G+qv58JNm4thoRJxuCs8mA7gAySNuV7LyezQC3qSZHd99R/9+dHFF/6Zi/aXsr+gNqxbI52WC8q1G5PJtsWNcZ8jzuQ45AX0HOPjZn/sZdWkV2SL9NfwR7JbIy3KvxN61HR8hG54u1cvgsWakuJDuY7yaDmdYXcj2mn5AzW1tTbzVOf3/JvcC+0zhXYwO1s2u+pwxGyfXr36He9a+Wrb3JoOOlg+alprEB2dn6CI113GSH8l4aG3JbpLWE7DR/nGywTyUDo1UnKgcTHOfaZIO8ATly9tcN4rUcH8Xkd2Zb0EhrBRrBDtlgv4q6x5TsRLedqdqc7Oi+vz5eg+wUXlfL35doMeM4Sfqr1Ovh9npttB35XIvsIDRvQb8n2fHq3c1swmCDpTs2LyY7Sp8gA6iFZDD0ofrvHloEtPQ7XrvXY3k/GQR9mdz7dTbZWH+BCYL3Mfl6BDlCezUPvJPXfQxYhzH+EtPrycDoGeS1PmmA1jjGx7F0sPFwMjD6W/p7c3ao5WSXKY5z3cbPB9Y8vqA+3ruetzZ748a7jm6nfx2tV499wgEFHrhM+zHkzM/a9fHG5CDms8hrfNKZhMb5Wr3x3E3UEf/6+ExyJuFcJh+EHGnwWM/XlrWcrkYur76jptl7rz2Zok2iX/fsTw7CXUoOdGxODn7cQs5yrD1FOgfSr+efTl6P65BLAF9FtrU71ON7H1MvPVu/Hs9DGmVxNbIT/1GyDflIozy3rS+Ored+Ef36oTeDcwpZ/085AEzWpSuQ/YBryOtxtfq7getUsu35GjkT9GWyrD+R7GN8grwW2tRjI53Zrp9Xb3/WGfU4e7NOD6gbWqb3LbK9fhXZD9uXrLePIgc8JuynMOb6IpdJHlbTuo3+ktWtx/v7cdJbqZbdN9NfkXAVNRCtj88i69lg4qC2OUBxPLkU/rJ6/t9LziDvQ/Y5vsc090cvy//mPAOzcpA58vk2slLfthaAz9cK68cM1qnfjcYehMbzR5Ed17ZLHMe7gcAzyTtxvbrxd232xj2sXmgX1Mcr1mO7kaxQv9amomqZ74fWC+bxU+WP7MS8jpxCf2u9+O6oFcqL2xwjS49MPaq+dgHZsfxGrRhOoN8pafU1B2Pe4xnkcpzVa4VwJdkgfYEWN0OZJN19yY5lr/MzbudmvOMnO7ZfpD/7tnKtFK9jilt6jy37jZ97+4M+RXZU1idnxfZve0w1nYXk8qLecR1JBsyTzhBOcJyvIhvX2xrPnUY2bm1vuLOI7Kh9gzojUZ8/FHjnND+7Q8iGsLn06TKyURr4e17GSX+l+hm06piQ9cUZZHB3D/Xug7XMfpKW9U59zWNqOdqGnAW4m/p1BPXxvgx2u+XdyE59b0nvtmTn6X31WnofLb7qo5ajW+jfoKe5rGfad/Ji/CWmvyc7dhMub6cfbKxV/1+DOutFf+nw2eRI9MmNa2Oqr5s4pX5mb6B/B8EDyGDvRS2OZ5Dr6Gktz9EBZB24Htl+vIusb3p7o86jcXfaFuntS7Y9l5H1zD707wS5P3lzh2PJ+mfcpcyMKHgce77ITuP5ZPt4G/2A6zSmDs42p7/F4YnU70ckB3pfT7bjm5MrF25ikplQ+gHV9uSSwc8Ab2j8/uG1jH6tnq82NxRak/oVL/Vcb0128L/SuFb/jcbKgKnOGTmD8yyy3Tm1pv9EcrniS+rfrN0ib6eQ18qr6S9zfB8ZTJ1JdsLXH698T5DeXmQbvRoZIH+7luHekrt1JyoTExzntGe2yZUvhzcef46sr3/P0vu1jyMHoFrfIKe+7kXAGxuPDwf+izrYTT/ImmqLx8HkYMAF5LX3MfqDE6eT9dJqE6Uz5nytRF7n76Cu8qnl7jPk4MI3mfyOqM0Z32PIWcp9yevmllqWX0quaPnYZGktz//mPAMzenA5ktG7280i+muBNyMboj1p/x0cD6HfMXpULYC9Nd671cLd9iYhE43uXk2ObvyYSW5eMvZ5ci/QReSobnN269HkaNmsjz6QDfGnyYr8w2Rgt5AMqG4k1yvvMkB6m9dzsm393D5fP8NXk7NOp5Cdr4E24DbS349sJHrfx7QNA36R5gTpHlTLypR5IzupryY75GuRDfcd9L8+YSVaVO70O5cT7Q+6vn426zPEl0HW9F9AiyUzLL1HYFf6y2/3Jxu03vKNw2ueJ/xyXBoNYv3cv0n/bn93NtI6khw9Xn2ax7c/2fAf2TzmmbpmJsnH2D2AryEbsq3I+uKvt9SeqlyQwcq3yVnn3sDG1jX9t00zf08j94n1Zi17o6yXjimPkw3GjL1b3HPq5/oqRnAnL8ZfYtpmRLy3l+uiei2fRLYhV9RjvJMMIG+jXUd1CVl3bV/L5jeoN+AgBxXewiQdywGvo3sZbFnovuQy09Xrsb2TbJPOJTuorb6wmv6M18Hk6o5LySWP25GDHFeSbeju5ODOZMc7VPDI0p3KRfSvofNqmd+hUd7uZZK9iWQbdDm51P7QWl6bd3PelRyUfAu1jzHFeeoFVL0lt1eRndE9G3+zgLw+J9wzNk66LyVvof7q+vhJwJvqz88lBxYWtUzrpFpOtyXbjpvp94M+Qnamp1ztQwbkX66fwU/INmi3+ruX13PWeoljfbxNPc/Pp36xOrka4P8y+BePDzWzzQP37X+FvI7OIa+p3cmA79tTHeeYdLer//f2Uy+gX2/fSC4rn/AL4Mcc4+Hk6obXkNsCfkpem4c08jbl3VDrz829iU8l64pT6uM9yev2wZOk1RugeCjZD76exhLjeqyfaDwe6C6my9O/Oc/AjB1YXiCX1kplUX3uQbWiuIEpZpZYegbnweR62NPoNw6LyVGSs8kR1oH2ejHxDQROYpLv/BlzoRxcL7yHkxXoWeSoxhPm+NxvTAaMvQbwJHJpy2FkQxu18jpkkjTG64i/p36mr6bfIXxe/Wxa36Z3kvfcl6zgW98xrmW6Uy6zq8d4Jzn69laykl+PnKH9KlN86e846U21P+hmJtgfNMB7rEY2kFPt0XtYzceq9Th/SI6AXl9/f3q9Jv+uHutUjfV+9ZguIJfMPIgMFHp3VP03snP5AwbYHzdJmfg5k3zh+Ez+Y+k9gOvT3wP4L/RnAiasyxh/1uXIen3uQX8JzjZkh3nSu59N8j4HkXviendu25PcC7LRBHmY6G5xr6V/C+4n1nIz9J28mMYSU3KQ63tkcPEFsm5dl/5+tGvJYOOxtexO9V1ta5LLs9cl25JPkwHjndQvCWeSumLU19EE7/GMesyr0f/+q9fQMtgjO+Efob8XZTUy+HgL/RtGRb1e76bFQB3TDB5Zuq1sLu89u5a5q8nO4UfIYHnKIIEMNl5b3/s2sjPf3HO9mJyla3sXx7PJGZGX1scXkjNyj2/z+gnS3JrsOH+/lrNtyWv7PeQy7lZ32avn+tv0l8BtSt7pdQ9yWe11tPg+QXIA4c3kstzTyZmWS8kVHb27HE4V0DY/y61YepnchfS/SunM+rlOGCCMk/ZQM9tjyum3ybrwIvo3eXk5OVB9DS2XttfXrUluKbigltfbyAGr7ci68Yqa58tbnLOtyYGIB9fHB5ErMj5H1knvov0qsObexOPqc08m+2Gvot2NoXoDFB8iZ9/eRuOu12S7cD39uxhPe6B8Wf835xkY2YEsXSB7U78bkDdY+CD9OwieQ651bbshe3+yQbmUrFBPbFx8V5KN4XRvxjHMDQROIgPOJcCfyKUg29ZK6nrq0qM5+izWrXnrLYlcqVZ4n29UglcCfztJGuN1xJ9aK7wvkptnryGXEw50p7cp8r4fjdnIGTxHf91PVf9/B0tvgj+H/l6FU2g36j/0/qBpHEeb5bj7kx2hM8hO5Dr1+a8D19afVySXDLX9EuGnkTNuvX1jK5N7MC4jG6AHM6LvUKnvNeWNJWawrIy3B/AN9Tprezfa4+l3LtciZwNuJQOOXqDWaj/hJO9xAHl3vA+SHcJxl9Ey8Yhs725xRwyblwnet/USU7JTczrZsXksOeuyTTPPtczuRy7T2XWy4xwn7b/uEa4/38oU++1m4jqa4H32Y+k7arZddryQ3E/1NnKQ6TGN568l24DVG+8xSEd62sEjky/v3Y5sV6a6W2Kvbn1e/axuJevTvyeXJDbL8SBfIrw1uWzvPnJVwqbkIOQVDNmGk7OX/0gOjm5KBpCt7mJaX38ijdnx+v9Z5D6hL9MuwF6bDNCPree6d7OKBWT7/loGWCZP9m++T9Yv59bnevvRL6z52niAYxxqZnuCa+c+6l2Ba/l4T/150IH8FepndjNZF61Sj/Ma8vrclZwZfUCQNuac9QaAv0fW+72l2geTA5CPHuD8H8vSexO/RZ1NJgeFL6V937o34/vSeh18t5bVTepxfZ2WX5WzPP+b8wyM5CAeeNvy95IjI7vXgvxycibmvHoRT7akYez08K/JQOFr9WJ7KXlXnheTneBpLyVkgNFdGkuGaoG+iawAj6sVQa/Tv1m9oOf0S/7q+XkV/eVLe9WL+lqyk/g2ph65HK8jvhO5pORgch1z6+/emG6ZmuHztD3ZefwkS2+sfzjw7mmkN5L9QSM6tuZs9FlkA3MbS++T+wqNNfsDpn8Qebe+5jKJm5mjWa8ZPI/T2gPYeP3p5NKSPclOSK/jdRrZeA80SzvFex1Y3+PF9fFkm8aHulvcDJ7vjcgguFe33svSd3u9hH6n9ZlMvY/pVHLw4N31el+7fh47kp2Rq5kkqJrp62iC9zyY7IAtoEWQRgZA99X6ZlWy7r+S/u3tFzKNmb0x7zFQ8MiIl/fW6+7rZN18eT3GV5EzAhcyxGoO+gHVMeSyynOGSa+R7i7kyoIl03jtvmQwukPjuf3JjvqUX+NDnf2u19HLyCDtPvLmQL0bYbW+Qy7Zn7uKHNR+Ajlg8RJyYOIEMkAY5G6JQ81sT5LuU8n+5ik1b6vT8ms06uv3oH9XzxXqZ/jJ5mdIrrLZt77PZHdhPIicJduOnIV7K7n8tbcP7XDa3zxmsr2JvZuHtGqT6t/2Bih+QN60ZU9yIPI6MhAcqr5YXv7NeQaGPoClC9EBtdAuIjvyV9ZCuoAcSX4jk9+GeLLp4UPJyv4SsgNxByOYjWDwGwisQQYrrySXfXyafofhxeQoxJxPDZM3RLmQrORfR46SPIpcw76obR4ZvyP+MQa82UVX/rH0Ms7TyCVil9Rz9Av6yweOJDuI6wzyeTKC/UEzcMwnkgMaR5DLPp7P0t9VeAfTHOwgOw0/IQdS9iMDhNYj9MvSP1ruAeSBs7SX1HJwFjlrs0qjzjiOAToQLfP59FqWDxnz/MjvFjeD5/l6cmnc5WQHc2cyUL5nkLqHnPG5g1wJcDd11JtcDvUxWt5Fs75mxq6jCd5voA4qOQN0DxmwrUN/1crIVnUwRfA4Xr3GiJb3kjN3vWWJK5OzOjfV5z/OAN8DNUH6vYDqCEY4QEHu+xm4TiQHUy+sZXV/cun912kxMErOPv+MDJ62JwcRjqnH9mWynp60/zSmvtgT+FfgzfXxKmSwdzN1aW2LPI10ZnuK99qbrAOfPeDrtiED0Z/S/4qhlcgZvx8B5zU+m/OZZDaT/l2KeytyFpIzl5eT/YTWqxWYem/iJ6db/sl+4Y/IgHFhPbZWy4Xnw785z8BQmV/6Ij6ODACaX4B3Qq0cerd4bfuF0BNNDx9WL7xHDlLAR3i8+9ULZWVy6ch36N957DCygVw0159LI79rkVPgLyNHv3rL8Fpvgq7pLDcdcfrLOF9NjnA9uFaAvS+X/mWtRL/DNAcBGHB/0Awf74FkUNH7YvD9ydm84xhg6c0U73EwGZje0KXyPwPnstUewMbfb0POanyU7Hh8lH4n9UTqTSZmKK9LLRGl/Yhs67vFzUCeN6M/KLcBGahdTA6IfZbsiBw09njGpDE2QD6/pnUW2T6t0vibVdse52xcR9M8Z9vR6FCRq1a+S//usecz4hFx2u3xHfny3lrP3Nysl2uZfTFDLC8d8x4PY4ZWh0wzP5vWuuJT5DLHNrezX5kcpO0FYweSM8n3kIHVBkyxJHFMfXEyucz0PHK28WH1+RXIgZP3M0CnniFntgd4n4GWydNfhbAxOYD7PZZeiXQZjQEP2s1wH0L2KXp9gRXJuyVeSvuVGCPZmzjFe/QGKE6a6zLftX9znoGRHET/+8pOIDcv7tj43c0Mdov9gxhieniGj3Mj6hI4cjTjDnL54A3kvojOTg+TG0vvZBrfEVJffzDLSUe8Vt73Um8PT3bcjiADtdeTo+7Djsq22h80C8fa3NfQu4b2JWcCnlsbjaEDR7KTv/Vcf7azcD4nu0Pi2JvtfIPcsH8L8Cv6+0GPJfd1zPpAByO6W9wM5Gt1ch/QzdRb7JN7jg6uv1+L/nLTNvswt69l+xpyc/4NjfJ/Wr0uBpkhn5XrqGVeessGg5xBupil92S9h+xMb8ncDGbOyPJecnbwdeTs0lPIAbdPMMdbC2bpnK5M3es/xd/tQQ6mPoQcJPp4rW+WkPu0zh/wfU8g+za95cbn1s+0t5RyBQbbAziyme0Rn99dyRninRrPvY/cL/dacmC393U8A13ntZx+m6UDtUGC2qH3JrZ8n2nN+C7v/1ZkGRcRm5PLGv+ObBzeDhwaEffVP9menCZvm9bl5Bfy/WNEnEdWCs8CVoqIz5ZSbhj1MUyQlyi9KzLiCHJK/n+ALSNil1LKPcBeEbE7OSL741LKP81G3qbpPnLJ4s+m8+JSys0R8RTgp9NNoytKKbdHxCuBd0bEZ0opN0TEDWRjtjbw21LK74d8j09ExFHkjN11pZRPRkTU35WhDmAwPwMOiogdSik/qM8tIEfNPltK+fMo3qSU8vlRpNN1U3x26wIXR8RO5DLCQ8lg6J/IWbgrImI/crT+WaWUH89wdpcSEQeSnaQDSik/i4hNyeBn+4jYF/hvct/Fb2czXwCllP+KiHPI/UZvIpeNPwk4ISLuK6Xc1/jbB3wGEbEHOct1Q0ScRgYKHyc7V/sDN5RS/hwRx5Ln4KABr8NZuY4mExFrllL+s5Tyl1oXr0fOOpwMHBsR15RSfkXul9uUHEz7+Szka0Ep5f5Gm7klGcCeTg5OXBYRK5VSLo+I/yK/i3RgpZTfR8SV5OzEK4E/A2eVUv5lNEfSXaWU/235pz+v/64h+2W3AP9RSvlIRPyFHLBoJSJWJT/Hc4E/RcQJ5NK/tYFPRMRBpZTvkIO3E6UxtmxsRA60H0OWjZfUvzmnvt/KpZR/b5vHEfojGaTtGRGHkXXPL8n6417y6wC+BIO336WUWyLifuCqiPhzKeXDwG8GSGK8uucHZN3zwVLK/xskP5Pk8zujSGd5EwN+3p0UEYeQy/+OIEdZnkeuiV6B/K6QewdM6wqy8v1ARKxIzqjdT64H/u9R53+cPDQDtK3JUZbePo/XkbdevpEMzq6tAZuWMbXDfDFwUe3cLSDvfvafI3yPp5PLOk4vpXxkVOkO8P5rkTMSC8hRt3XIztPhpZSfzHZ+lncR8TRy9uyeUsqREbEyuaT2WPKuqF8mZ0IGaaRHlbcTydmoi2qn+U8RcRa5/HFrcqnLnNdlEbEZuQftQHI54Z6llG9M8Zr9yHbjGnKp18Vknb0WuYzqSWSH9RHkTQm+N2Ce5vQ6iojVyGWpbyNnGz5GdtR+CBRy2dk/AP+PnMU/vpTyvWZbNgt53IbseF9Prk74I7kn6E+17P1zKeUTI3qv3ncv/mEU6S1vImIXclXImuRM+Y7TTGcJOZPzc7Ks/Yy8pv4EfKTtQFNEbE9umbiarGt+RX6/3p/roMqfyLtNz0mHOCLWIOvo55LLGn9IzgT/rpTygRG9x9PIwfyB6osJ6p4zyJm5H40ib5rYchGkAUTEAeSU9StKKZ+sz60xnUq00Xm+uBGorTvbHZuIOJVcGncXObtyZUS8nmyAPkmO6L1xWZ9Zms/qDMJV5HeE3DhD7zGtynmE778puYz4QPK7AC8upXx7LvIyH0TEQeT+xhNLKR+sz91MzqjOSBlrma/e7MaLeiOyEbE/uUdlZCOyoxQR25dSftjyb59GzsLdWUo5PiJWIVdhbEl2LN8K/HG6I/VzfR1FxDPJPWe/Je/mdk+drd+anKn9H3KZ6FdLKR+fhfw0Zy9PIW+q07ud+qPI74a8qc5evoxc8j2rs8fzWURsRN7p8AxyMOGn00hjITnz/+NSym8j4mgymNl3stm9SWa2/5Nc9npuKeUdtWycTc5s/+Og+Ru1iFi5lPK/EbGYrMPPKKV8Zo6zNed1z3y23ARpABHxDHJGbegOb6Pz/OI6PTyrIuJgct3vgeQ+iR+UUpZExNnkhtHXz3aeNDPmOoiaLXVWZ5ClM5qmGvy8jdzX8DVyX8Nhc9lJXZZGZHvLpBqPW80I1QD5neTMdW92/Fhyj/MbypDLmOt7zNl1VOuqD5Gz/5fUAcxnkzfT+jm5x+f+tudryLz0Zi+vJZf3XkAu730I2W7uSN4s5GHkVzwMNHup0ejNmg+ZxgJyr+iLyPpi0qVxMz2zPVMiYgVy5dTbyWvsY3Obo6XZhs++5SpIg9F2eOey8xwRx5CjPmuRyzj3ryMsLyBvwrEE+J9mR0KSeupAz03kzSVePp2R7FGbDyOys7GMeS7VcvU64MK60mQF8utqvjXbnd0uL+/V6NTlts8hZ6m/3/I1MzqzPVPqUtqNSin/ZzaXC6ublrsgbXkREU8k9xL9spTyhPrc6eStSs8tuUFbkiZU65HO3WxneR+RnY1lzHOprlp5LfmF0NfMcV46ubxXozWdgGU2ZralmbTM391xOXY3uTn7/oh4ErAVeUei5xmgSWqjdPSul8trcNZTSrk1Io4Dlss9UKWUT9Wljq+PiNuBX83Vqo5SysfqXqW3RcTO5PLerYBvzkV+NDOmM6NUy8afybveUgO197IczWxr+eZMWofVpUEH1n//BlxSBrhTpSRJMyUiNuzKUsIuLu9VNyzvM9tafhmkLQMiYiWAYTffSpK0vOrq8l7Nvflygy4tXwzSJEmSJKlDFsx1BiRJkiRJfQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIQZpkiRJktQhBmmSJEmS1CEGaZIkSZLUIf8fRbmbEw0pS+sAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1080x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_coefficients(model=final, feature_names=tvec.get_feature_names(), top_features=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Top Predictors:\n",
    "- workout: **routin(e),muscle, arm, exercis(e), up** \n",
    "- diet   : **food, calori(e), meal, sugar, keto** \n",
    "</font>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 464,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.roc_curve.RocCurveDisplay at 0x21b26d60108>"
      ]
     },
     "execution_count": 464,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(nrows=1, ncols=2, figsize=(15,5)) \n",
    "ax = ax.ravel()\n",
    "plot_confusion_matrix(final, X_val_tvec, y_val, display_labels=labels, cmap='Blues', values_format='d', ax=ax[0])\n",
    "plot_roc_curve(final, X_val_tvec, y_val, ax=ax[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Now to look at the 53 misclassified diet class and the 37 misclassified workout class\n",
    "</font>    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Misclassified data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "results = y_val.to_frame()\n",
    "results.rename(columns={'is_diet':'actual'}, inplace=True)\n",
    "results['predicted'] = predictions\n",
    "\n",
    "wrongresults = results[results['predicted']!=results['actual']].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 323,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "wrongresults_df = df_combine.loc[wrongresults][['subreddit', 'wordslist', 'stemmed_post']].sort_values(by='subreddit')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 405,
   "metadata": {
    "scrolled": true
   },
   "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>subreddit</th>\n",
       "      <th>wordslist</th>\n",
       "      <th>stemmed_post</th>\n",
       "      <th>predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>workout</td>\n",
       "      <td>[protein, calori, day, mayb, day, week, questi...</td>\n",
       "      <td>protein calori day mayb day week question day ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>workout</td>\n",
       "      <td>[good, preworkout, good, preworkout, trustwort...</td>\n",
       "      <td>good preworkout good preworkout trustworthi on...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>workout</td>\n",
       "      <td>[fast, run, fast, plan, remaind, year, lift, c...</td>\n",
       "      <td>fast run fast plan remaind year lift cardio pe...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>611</th>\n",
       "      <td>workout</td>\n",
       "      <td>[benefit, bcaa, read, watch, non, bias, review...</td>\n",
       "      <td>benefit bcaa read watch non bias review bcaa l...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>workout</td>\n",
       "      <td>[love, proudct, help, whole, bodi, overweight,...</td>\n",
       "      <td>love proudct help whole bodi overweight found wow</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>696</th>\n",
       "      <td>workout</td>\n",
       "      <td>[food, plan, muscl, build, hey, recent, chang,...</td>\n",
       "      <td>food plan muscl build hey recent chang food pl...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>224</th>\n",
       "      <td>workout</td>\n",
       "      <td>[weight, loss, work, may, sound, stupid, burn,...</td>\n",
       "      <td>weight loss work may sound stupid burn cal nee...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>workout</td>\n",
       "      <td>[male, good, idea, start, weightlift]</td>\n",
       "      <td>male good idea start weightlift</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>894</th>\n",
       "      <td>workout</td>\n",
       "      <td>[n, perca, isso, alimento, que, te, ajudam, em...</td>\n",
       "      <td>n perca isso alimento que te ajudam emagrec</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>575</th>\n",
       "      <td>workout</td>\n",
       "      <td>[look, advic, lose, weight, hey, guy, hope, so...</td>\n",
       "      <td>look advic lose weight hey guy hope someon hel...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>159</th>\n",
       "      <td>workout</td>\n",
       "      <td>[excess, elimin, hi, wonder, best, elimin, exc...</td>\n",
       "      <td>excess elimin hi wonder best elimin excess mad...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>565</th>\n",
       "      <td>workout</td>\n",
       "      <td>[look, advic, hi, male, fairli, activ, decent,...</td>\n",
       "      <td>look advic hi male fairli activ decent health ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>806</th>\n",
       "      <td>workout</td>\n",
       "      <td>[weight, fat, loss, cut, titl, say, lose, fat,...</td>\n",
       "      <td>weight fat loss cut titl say lose fat weight e...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>217</th>\n",
       "      <td>workout</td>\n",
       "      <td>[nutrit, q, hey, everybodi, hope, stay, safe, ...</td>\n",
       "      <td>nutrit q hey everybodi hope stay safe start ge...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>workout</td>\n",
       "      <td>[pre, help, tri, find, pre, supplement, work, ...</td>\n",
       "      <td>pre help tri find pre supplement work best als...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>workout</td>\n",
       "      <td>[eat, enough, bulk, hey, guy, first, bulk, win...</td>\n",
       "      <td>eat enough bulk hey guy first bulk winter tri ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>641</th>\n",
       "      <td>workout</td>\n",
       "      <td>[question, someon, obes, hey, guy, first, post...</td>\n",
       "      <td>question someon obes hey guy first post tri ke...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>workout</td>\n",
       "      <td>[start, lb, lb, goal, lb, abl, maintain, weight]</td>\n",
       "      <td>start lb lb goal lb abl maintain weight</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>workout</td>\n",
       "      <td>[day, zac, efron, week, baywatch, programm]</td>\n",
       "      <td>day zac efron week baywatch programm</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>workout</td>\n",
       "      <td>[new, whole, thing, look, help, basic, home, s...</td>\n",
       "      <td>new whole thing look help basic home stuff yo ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    subreddit                                          wordslist  \\\n",
       "207   workout  [protein, calori, day, mayb, day, week, questi...   \n",
       "749   workout  [good, preworkout, good, preworkout, trustwort...   \n",
       "819   workout  [fast, run, fast, plan, remaind, year, lift, c...   \n",
       "611   workout  [benefit, bcaa, read, watch, non, bias, review...   \n",
       "490   workout  [love, proudct, help, whole, bodi, overweight,...   \n",
       "696   workout  [food, plan, muscl, build, hey, recent, chang,...   \n",
       "224   workout  [weight, loss, work, may, sound, stupid, burn,...   \n",
       "521   workout              [male, good, idea, start, weightlift]   \n",
       "894   workout  [n, perca, isso, alimento, que, te, ajudam, em...   \n",
       "575   workout  [look, advic, lose, weight, hey, guy, hope, so...   \n",
       "159   workout  [excess, elimin, hi, wonder, best, elimin, exc...   \n",
       "565   workout  [look, advic, hi, male, fairli, activ, decent,...   \n",
       "806   workout  [weight, fat, loss, cut, titl, say, lose, fat,...   \n",
       "217   workout  [nutrit, q, hey, everybodi, hope, stay, safe, ...   \n",
       "143   workout  [pre, help, tri, find, pre, supplement, work, ...   \n",
       "40    workout  [eat, enough, bulk, hey, guy, first, bulk, win...   \n",
       "641   workout  [question, someon, obes, hey, guy, first, post...   \n",
       "150   workout   [start, lb, lb, goal, lb, abl, maintain, weight]   \n",
       "4     workout        [day, zac, efron, week, baywatch, programm]   \n",
       "804   workout  [new, whole, thing, look, help, basic, home, s...   \n",
       "\n",
       "                                          stemmed_post  predicted  \n",
       "207  protein calori day mayb day week question day ...          1  \n",
       "749  good preworkout good preworkout trustworthi on...          1  \n",
       "819  fast run fast plan remaind year lift cardio pe...          1  \n",
       "611  benefit bcaa read watch non bias review bcaa l...          1  \n",
       "490  love proudct help whole bodi overweight found wow          1  \n",
       "696  food plan muscl build hey recent chang food pl...          1  \n",
       "224  weight loss work may sound stupid burn cal nee...          1  \n",
       "521                    male good idea start weightlift          1  \n",
       "894        n perca isso alimento que te ajudam emagrec          1  \n",
       "575  look advic lose weight hey guy hope someon hel...          1  \n",
       "159  excess elimin hi wonder best elimin excess mad...          1  \n",
       "565  look advic hi male fairli activ decent health ...          1  \n",
       "806  weight fat loss cut titl say lose fat weight e...          1  \n",
       "217  nutrit q hey everybodi hope stay safe start ge...          1  \n",
       "143  pre help tri find pre supplement work best als...          1  \n",
       "40   eat enough bulk hey guy first bulk winter tri ...          1  \n",
       "641  question someon obes hey guy first post tri ke...          1  \n",
       "150            start lb lb goal lb abl maintain weight          1  \n",
       "4                 day zac efron week baywatch programm          1  \n",
       "804  new whole thing look help basic home stuff yo ...          1  "
      ]
     },
     "execution_count": 405,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrongresults_df['predicted'] = results.loc[wrongresults][['predicted']]\n",
    "wrongresults_df.tail(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 325,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "diet       54\n",
       "workout    34\n",
       "Name: subreddit, dtype: int64"
      ]
     },
     "execution_count": 325,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wrongresults_df['subreddit'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "metadata": {},
   "outputs": [],
   "source": [
    "def exclude_words(data, ex_words):\n",
    "    \"\"\"\n",
    "    Function to check if item in parsed in data is in list of words to exclude\n",
    "    Takes in arguements:\n",
    "    - data - list of words to check\n",
    "    - excludewords - list of words to check against to exclude\n",
    "    \n",
    "    returns string that does not include the excludewords\n",
    "    \"\"\"\n",
    "    checked_words = [w for w in data.split() if w not in ex_words]\n",
    "    return checked_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong_diet = ' '.join(wrongresults_df[wrongresults_df['subreddit']=='diet']['stemmed_post'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "metadata": {},
   "outputs": [],
   "source": [
    "wrong_workout = ' '.join(wrongresults_df[wrongresults_df['subreddit']=='workout']['stemmed_post'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "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": [
    "wrong_diet_df= findcommonwords(exclude_words(wrong_diet, common_words), isstr=False, num_words=20, plot=True, color=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "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": [
    "wrong_workout_df = findcommonwords(exclude_words(wrong_workout, common_words), isstr=False, num_words=20, plot=True, color=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Some of the top misclassified data has words that are promminent in the other category. Examine 2 of each misclassified record to investigate\n",
    "</font>   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 403,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Any tips for what would be the best diet for my ideal body type? What should I cut out? I’m naturally a big boi but I’m pretty thin now for the most part. I’m 5’11, 164 lbs. I have a decent amount of fat on my lower back and my thighs that I’ve never in my life been able to get rid of. I have very little definition and I’ve never had a hint of abs. Most of all I wanna lose that fat and get ab definition. Then I wanna bulk up a little. I’m less worried about staying bulked up then I am about keeping that pesky fat off.\\n\\nI’ve been doing a 15 hour fast between 8pm and 12pm every day and it’s helped me sleep and my skin has been way less flabby (except for the past couple days but I’ve been eating junk food). I mostly avoid fast food and I eat once or twice a day with “healthy” snacks in between. For the past month I’ve been eating chicken breast and eggs every day. I eat fruit and sometimes veggies every day. I honestly don’t know a lot about calories and nutrition.\\n\\nI’m a runner but I haven’t kept up with it every day recently. I do about 3 sets of 6 different ab exercises every morning always ending with a plank and I try and do at least a 100 push ups a day. I do various Dumbell workouts every day for my arms. My gym opened back up 2 days ago so I’ll be going 3-5 days a week.\\n\\nSo if a nutritionist or anybody can help me, I’m not looking for a detailed diet plan unless you have one. I just need tips or just something to cut out. I’m struggling with bread Ngl. Should I cut out cheese? I need to know these things. Any tips on hobbies or exercise that would help are great too.'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 403,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#post diet wrongly classified as workout \n",
    "df_combine.loc[1741][['post']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Includes words like runner, workouts, Mixed description with dieting queries\n",
    "</font>   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 404,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Can't figure out the right way for me to eat I’m 17, 5’3 and 115lbs. But I’m “skinny fat”, so I’m trying to lose the fat and build a little bit of muscle. I’m vegan, and starting a couple days ago, I’m trying to consume 50g+ of protein. I don’t like eating to be honest, but I’m doing my best so that I can hit my protein intake. My eating habits have been really clean for the past month and a half (or so). I exercise almost everyday as well. But it feels like no matter what, I can’t lose the extra fat. Building muscle underneath isn’t as hard, but fat loss feels nearly impossible at this point. Its all definitely taking a toll on my mental health, so any advice is appreciated.\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 404,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#post diet wrongly classified as workout \n",
    "df_combine.loc[1337][['post']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Includes words like exercise, muscle, Mixed description with dieting queries\n",
    "</font>   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 407,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"No weight/fat loss while cutting As the title says i am not losing any fat/weight even though i am cutting. It's weird cause i seem to be doing everything right on paper, i've been working out for 4 months now (started with bulking) i'm at a 200-300 calorie deficit and am working out 3 times a week. I consume about 0.9gr protein per pound. Over the past 3-4 weeks nothing changed, my bodyfat% and weight are still the same so i know i'm doing something wrong. If anyone has an idea or tips it would be much appreciated tnx\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 407,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#post workout wrongly classified as diet \n",
    "df_combine.loc[806][['post']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Includes words like calorie, Mixed description with dieting queries\n",
    "</font>   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 408,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([\"Eating enough during a bulk Hey guys. I'm doing my first bulk this winter and I'm trying to ease myself into the eating for the first week or two. With this, I'm struggling to eat enough calories to put myself in a surplus.\\n\\nBased on what my fitbit says (which I take with a grain of salt) I'm burning approx. 3200 cal/day. So I'm trying work my way up to between 3300-3400 cal/day.\\n\\nAny suggestions on getting the calories in? I'm trying to do a lean bulk so I'm sticking to as much whole foods as I can.\"],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 408,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#post workout wrongly classified as diet \n",
    "df_combine.loc[40][['post']].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color = \"blue\"> \n",
    "Includes words like calorie, bulking. Generalized qn\n",
    "</font>   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Classification Model:**\n",
    "- Logistic Regression with TFIDF give us the highest accuracy (0.85) and MCC (0.703) score\n",
    "- This will be a good start to see how much time we can cut down from tagging the queries, and how fast we can channel the queries to the relevant parties.\n",
    "\n",
    "**Next Steps:**\n",
    "- More data Collection (scrapping from comments section)\n",
    "- Fine tune accuracy using Ensembling of different models\n",
    "\n",
    "**Final Goal:**\n",
    "- Topic Modelling and Chatbot creation\n"
   ]
  }
 ],
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