--- a
+++ b/finalProject/240 final project_newest.ipynb
@@ -0,0 +1,3266 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "719a655c-4a20-493c-9475-582ae6c39e32",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import warnings\n",
+    "warnings.filterwarnings('ignore')\n",
+    "# Load raw data\n",
+    "df = pd.read_csv('overview-of-recordings.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3915fae5-3aa7-4895-a07e-99003db61a2e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 6661 entries, 0 to 6660\n",
+      "Data columns (total 13 columns):\n",
+      " #   Column                               Non-Null Count  Dtype  \n",
+      "---  ------                               --------------  -----  \n",
+      " 0   audio_clipping                       6661 non-null   object \n",
+      " 1   audio_clipping:confidence            6661 non-null   float64\n",
+      " 2   background_noise_audible             6661 non-null   object \n",
+      " 3   background_noise_audible:confidence  6661 non-null   float64\n",
+      " 4   overall_quality_of_the_audio         6661 non-null   float64\n",
+      " 5   quiet_speaker                        6661 non-null   object \n",
+      " 6   quiet_speaker:confidence             6661 non-null   float64\n",
+      " 7   speaker_id                           6661 non-null   int64  \n",
+      " 8   file_download                        6661 non-null   object \n",
+      " 9   file_name                            6661 non-null   object \n",
+      " 10  phrase                               6661 non-null   object \n",
+      " 11  prompt                               6661 non-null   object \n",
+      " 12  writer_id                            6661 non-null   int64  \n",
+      "dtypes: float64(4), int64(2), object(7)\n",
+      "memory usage: 676.6+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b2c4477c-b934-4da8-8333-5b9e336bb0e0",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "There are 0 duplicate .\n"
+     ]
+    }
+   ],
+   "source": [
+    "#start cleansing\n",
+    "#count duplicate\n",
+    "duplicate=df.duplicated().sum()\n",
+    "print (f'There are', duplicate ,'duplicate .')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "45abc48b-790b-4f8d-98aa-2c29e1b8cf92",
+   "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>phrase</th>\n",
+       "      <th>prompt</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>When I remember her I feel down</td>\n",
+       "      <td>Emotional pain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>When I carry heavy things I feel like breaking...</td>\n",
+       "      <td>Hair falling out</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>there is too much pain when i move my arm</td>\n",
+       "      <td>Heart hurts</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>My son had his lip pierced and it is swollen a...</td>\n",
+       "      <td>Infected wound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>My muscles in my lower back are aching</td>\n",
+       "      <td>Infected wound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>I feel a burning sensation in my guts about 2 ...</td>\n",
+       "      <td>Stomach ache</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6657</th>\n",
+       "      <td>I have a split on my thumb that will not heal.</td>\n",
+       "      <td>Open wound</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6658</th>\n",
+       "      <td>I feel a lot of pain in the joints.</td>\n",
+       "      <td>Joint pain</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6659</th>\n",
+       "      <td>The area around my heart doesn't feel good.</td>\n",
+       "      <td>Heart hurts</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6660</th>\n",
+       "      <td>I complain alot with skin allergy</td>\n",
+       "      <td>Skin issue</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6661 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 phrase            prompt\n",
+       "0                       When I remember her I feel down    Emotional pain\n",
+       "1     When I carry heavy things I feel like breaking...  Hair falling out\n",
+       "2             there is too much pain when i move my arm       Heart hurts\n",
+       "3     My son had his lip pierced and it is swollen a...    Infected wound\n",
+       "4                My muscles in my lower back are aching    Infected wound\n",
+       "...                                                 ...               ...\n",
+       "6656  I feel a burning sensation in my guts about 2 ...      Stomach ache\n",
+       "6657     I have a split on my thumb that will not heal.        Open wound\n",
+       "6658                I feel a lot of pain in the joints.        Joint pain\n",
+       "6659        The area around my heart doesn't feel good.       Heart hurts\n",
+       "6660                  I complain alot with skin allergy        Skin issue\n",
+       "\n",
+       "[6661 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# collection the texts needed\n",
+    "Text = df[['phrase', 'prompt']]\n",
+    "Text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "73ba1336",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{'my', 'just', \"you've\", 'you', 'o', 'couldn', 'isn', 'do', 's', 're', \"wouldn't\", 'she', 'this', 'against', 'been', 'ain', \"isn't\", 'which', 'he', 'as', 'him', 'on', 'shan', 'did', 'further', 'am', 'is', 'out', \"couldn't\", 'then', \"weren't\", 'have', 'now', 'because', 'ourselves', 'll', 'under', 'can', 'what', 've', 'up', 'they', 'mightn', 'won', \"that'll\", 'we', 'where', \"didn't\", \"mightn't\", 'theirs', 'who', \"shouldn't\", 'these', \"you'd\", 'herself', 'by', 'down', 'when', \"aren't\", 'of', \"mustn't\", 'it', 'here', 'all', 'didn', 'his', 'but', 'y', 'our', 'those', 'some', \"should've\", 'ma', 'into', 'other', 'mustn', 'ours', 'myself', 'a', \"you're\", 'below', 'yourselves', 'me', 'more', 'i', 'yours', 'for', 'aren', \"hadn't\", 'their', 'are', 'there', 'doing', 'hasn', \"doesn't\", 'himself', 'few', 'same', 'between', 'having', \"you'll\", 'not', 'should', 'hers', 'themselves', 'has', 'if', 'haven', 'about', 'during', 'off', \"needn't\", 'own', 'was', 'does', 'only', \"wasn't\", 'were', 'nor', 'm', 'had', 'such', 'than', 'from', 'at', \"she's\", 'to', 'in', \"don't\", \"hasn't\", 'be', 'any', 'that', 'her', 'the', \"it's\", 'd', 'your', \"haven't\", \"won't\", 'them', 'doesn', 'how', 'don', 'weren', 'why', 'an', 'yourself', 'before', 'hadn', 'with', 'wasn', 'wouldn', \"shan't\", 'while', 'both', 'each', 'most', 'or', 'its', 'and', 'itself', 'through', 'after', 'will', 'very', 'again', 'once', 'no', 'being', 'until', 'shouldn', 'needn', 'too', 'whom', 'above', 't', 'so', 'over'}\n"
+     ]
+    }
+   ],
+   "source": [
+    "import nltk\n",
+    "#nltk.download() \n",
+    "from nltk.corpus import stopwords\n",
+    "# save English stopwords\n",
+    "stopwords_list = set(stopwords.words(\"english\"))\n",
+    "print(stopwords_list)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6bd0c9a",
+   "metadata": {},
+   "source": [
+    "## Clean Text Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "39b72f50",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Clean text data\n",
+    "from nltk.corpus import wordnet as wn \n",
+    "from nltk.stem import WordNetLemmatizer \n",
+    "from nltk.tokenize import word_tokenize\n",
+    "import string\n",
+    "import re\n",
+    "\n",
+    "def phrase_cleanse(phrase):\n",
+    "    #Tokenize and divide phrase into separate words\n",
+    "    token_words = word_tokenize(phrase)\n",
+    "    \n",
+    "    # Convert all texts to lower cases\n",
+    "    words_step1 = []\n",
+    "    for word_1 in token_words:\n",
+    "        words_step1.append(word_1.lower())\n",
+    "    \n",
+    "    #Clear all punctuation\n",
+    "    words_step2 = [] \n",
+    "    for word_2 in words_step1:\n",
+    "        word_cleaned = re.sub(r'[^\\w\\s]','',word_2)\n",
+    "        words_step2.append(word_cleaned)\n",
+    "    \n",
+    "    #Clean the text list\n",
+    "    words_step3 = []\n",
+    "    for word_3 in words_step2:\n",
+    "        # check if every characters are alphbets\n",
+    "        if word_3.isalpha():\n",
+    "            # get rid of stop words\n",
+    "            if word_3 not in list(stopwords_list):\n",
+    "                words_step3.append(word_3)\n",
+    "            else:\n",
+    "                continue\n",
+    "    \n",
+    "    #Lemmatization - group different forms of same word which has more than 2 characters into one word\n",
+    "    lem = nltk.stem.WordNetLemmatizer()\n",
+    "    lem_list = []\n",
+    "    for word_4 in words_step3:\n",
+    "        if(len(word_4) > 2):\n",
+    "            lem_list.append(lem.lemmatize(word_4))\n",
+    "    \n",
+    "    join_text = \" \".join(lem_list)\n",
+    "    \n",
+    "    return join_text\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c6435d05",
+   "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>phrase</th>\n",
+       "      <th>prompt</th>\n",
+       "      <th>new_text</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>When I remember her I feel down</td>\n",
+       "      <td>Emotional pain</td>\n",
+       "      <td>remember feel</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>When I carry heavy things I feel like breaking...</td>\n",
+       "      <td>Hair falling out</td>\n",
+       "      <td>carry heavy thing feel like breaking back</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>there is too much pain when i move my arm</td>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>much pain move arm</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>My son had his lip pierced and it is swollen a...</td>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>son lip pierced swollen skin inside lip grey l...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>My muscles in my lower back are aching</td>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>muscle lower back aching</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>I feel a burning sensation in my guts about 2 ...</td>\n",
+       "      <td>Stomach ache</td>\n",
+       "      <td>feel burning sensation gut hour meal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6657</th>\n",
+       "      <td>I have a split on my thumb that will not heal.</td>\n",
+       "      <td>Open wound</td>\n",
+       "      <td>split thumb heal</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6658</th>\n",
+       "      <td>I feel a lot of pain in the joints.</td>\n",
+       "      <td>Joint pain</td>\n",
+       "      <td>feel lot pain joint</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6659</th>\n",
+       "      <td>The area around my heart doesn't feel good.</td>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>area around heart feel good</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6660</th>\n",
+       "      <td>I complain alot with skin allergy</td>\n",
+       "      <td>Skin issue</td>\n",
+       "      <td>complain alot skin allergy</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6661 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                                 phrase            prompt  \\\n",
+       "0                       When I remember her I feel down    Emotional pain   \n",
+       "1     When I carry heavy things I feel like breaking...  Hair falling out   \n",
+       "2             there is too much pain when i move my arm       Heart hurts   \n",
+       "3     My son had his lip pierced and it is swollen a...    Infected wound   \n",
+       "4                My muscles in my lower back are aching    Infected wound   \n",
+       "...                                                 ...               ...   \n",
+       "6656  I feel a burning sensation in my guts about 2 ...      Stomach ache   \n",
+       "6657     I have a split on my thumb that will not heal.        Open wound   \n",
+       "6658                I feel a lot of pain in the joints.        Joint pain   \n",
+       "6659        The area around my heart doesn't feel good.       Heart hurts   \n",
+       "6660                  I complain alot with skin allergy        Skin issue   \n",
+       "\n",
+       "                                               new_text  \n",
+       "0                                         remember feel  \n",
+       "1             carry heavy thing feel like breaking back  \n",
+       "2                                    much pain move arm  \n",
+       "3     son lip pierced swollen skin inside lip grey l...  \n",
+       "4                              muscle lower back aching  \n",
+       "...                                                 ...  \n",
+       "6656               feel burning sensation gut hour meal  \n",
+       "6657                                   split thumb heal  \n",
+       "6658                                feel lot pain joint  \n",
+       "6659                        area around heart feel good  \n",
+       "6660                         complain alot skin allergy  \n",
+       "\n",
+       "[6661 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Cleaned Data Result\n",
+    "import numpy as np\n",
+    "#Text[\"new_text\"] = Text[\"phrase\"].apply(text_clean)\n",
+    "#Text \n",
+    "text = np.array(Text.loc[:,'phrase'])\n",
+    "new_text = []\n",
+    "for i in text:\n",
+    "    new_text.append(phrase_cleanse(i))\n",
+    "Text.insert(2,'new_text',new_text)\n",
+    "Text"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "49e0d8f0",
+   "metadata": {},
+   "source": [
+    "## TF-IDF (Term Frequency-Inverse Document Frequency)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "071e44fc",
+   "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>prompt</th>\n",
+       "      <th>abdomen</th>\n",
+       "      <th>abdominal</th>\n",
+       "      <th>able</th>\n",
+       "      <th>abronchial</th>\n",
+       "      <th>accident</th>\n",
+       "      <th>accidentally</th>\n",
+       "      <th>accompanied</th>\n",
+       "      <th>ache</th>\n",
+       "      <th>aching</th>\n",
+       "      <th>...</th>\n",
+       "      <th>wound</th>\n",
+       "      <th>wrap</th>\n",
+       "      <th>write</th>\n",
+       "      <th>wrong</th>\n",
+       "      <th>yard</th>\n",
+       "      <th>year</th>\n",
+       "      <th>yellow</th>\n",
+       "      <th>yesterday</th>\n",
+       "      <th>young</th>\n",
+       "      <th>zit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Emotional pain</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hair falling out</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.593903</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>Stomach ache</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6657</th>\n",
+       "      <td>Open wound</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6658</th>\n",
+       "      <td>Joint pain</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6659</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6660</th>\n",
+       "      <td>Skin issue</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6661 rows × 954 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                prompt  abdomen  abdominal  able  abronchial  accident  \\\n",
+       "0       Emotional pain      0.0        0.0   0.0         0.0       0.0   \n",
+       "1     Hair falling out      0.0        0.0   0.0         0.0       0.0   \n",
+       "2          Heart hurts      0.0        0.0   0.0         0.0       0.0   \n",
+       "3       Infected wound      0.0        0.0   0.0         0.0       0.0   \n",
+       "4       Infected wound      0.0        0.0   0.0         0.0       0.0   \n",
+       "...                ...      ...        ...   ...         ...       ...   \n",
+       "6656      Stomach ache      0.0        0.0   0.0         0.0       0.0   \n",
+       "6657        Open wound      0.0        0.0   0.0         0.0       0.0   \n",
+       "6658        Joint pain      0.0        0.0   0.0         0.0       0.0   \n",
+       "6659       Heart hurts      0.0        0.0   0.0         0.0       0.0   \n",
+       "6660        Skin issue      0.0        0.0   0.0         0.0       0.0   \n",
+       "\n",
+       "      accidentally  accompanied  ache    aching  ...  wound  wrap  write  \\\n",
+       "0              0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "1              0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "2              0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "3              0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "4              0.0          0.0   0.0  0.593903  ...    0.0   0.0    0.0   \n",
+       "...            ...          ...   ...       ...  ...    ...   ...    ...   \n",
+       "6656           0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "6657           0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "6658           0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "6659           0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "6660           0.0          0.0   0.0  0.000000  ...    0.0   0.0    0.0   \n",
+       "\n",
+       "      wrong  yard  year  yellow  yesterday  young  zit  \n",
+       "0       0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "1       0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "2       0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "3       0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "4       0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "...     ...   ...   ...     ...        ...    ...  ...  \n",
+       "6656    0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "6657    0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "6658    0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "6659    0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "6660    0.0   0.0   0.0     0.0        0.0    0.0  0.0  \n",
+       "\n",
+       "[6661 rows x 954 columns]"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use TF-IDF to evaluate how relevant the words are in the file\n",
+    "# tf_idf = tf(word)*idf(word)\n",
+    "\n",
+    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
+    "\n",
+    "# Create and fit tf_idf model\n",
+    "text_vectorize = TfidfVectorizer()\n",
+    "X_tf_idf = text_vectorize.fit_transform(Text[\"new_text\"])\n",
+    "\n",
+    "dense_list = X_tf_idf.todense().tolist()\n",
+    "feature_names = text_vectorize.get_feature_names()\n",
+    "df_tf_idf = pd.DataFrame(dense_list, columns = feature_names)\n",
+    "\n",
+    "# concatenate prompt column with tf_idf matrix\n",
+    "text_tf_idf = pd.concat([Text[\"prompt\"], df_tf_idf], axis = 1)\n",
+    "text_tf_idf\n",
+    "\n",
+    "#text_tf_idf.to_csv(f\"tf_idf.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "77627c3d",
+   "metadata": {},
+   "source": [
+    "## HashingVectorizer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "92cce5d7",
+   "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>prompt</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>65</th>\n",
+       "      <th>66</th>\n",
+       "      <th>67</th>\n",
+       "      <th>68</th>\n",
+       "      <th>69</th>\n",
+       "      <th>70</th>\n",
+       "      <th>71</th>\n",
+       "      <th>72</th>\n",
+       "      <th>73</th>\n",
+       "      <th>74</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Emotional pain</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.707107</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hair falling out</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.377964</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.377964</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.534522</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.534522</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>Stomach ache</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.353553</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.353553</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.707107</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.353553</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6657</th>\n",
+       "      <td>Open wound</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.57735</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6658</th>\n",
+       "      <td>Joint pain</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>-0.500000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.5</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6659</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>-0.447214</td>\n",
+       "      <td>0.447214</td>\n",
+       "      <td>-0.447214</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6660</th>\n",
+       "      <td>Skin issue</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.00000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6661 rows × 76 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                prompt    0    1    2         3         4         5    6    7  \\\n",
+       "0       Emotional pain  0.0  0.0  0.0  0.000000  0.000000 -0.707107  0.0  0.0   \n",
+       "1     Hair falling out  0.0  0.0  0.0 -0.377964  0.000000 -0.377964  0.0  0.0   \n",
+       "2          Heart hurts  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.0  0.0   \n",
+       "3       Infected wound  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.0  0.0   \n",
+       "4       Infected wound  0.0  0.0  0.0  0.000000  0.500000  0.000000  0.0  0.0   \n",
+       "...                ...  ...  ...  ...       ...       ...       ...  ...  ...   \n",
+       "6656      Stomach ache  0.0  0.0  0.0  0.000000  0.000000 -0.353553  0.0  0.0   \n",
+       "6657        Open wound  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.0  0.0   \n",
+       "6658        Joint pain  0.5  0.0  0.0  0.000000  0.000000 -0.500000  0.0  0.0   \n",
+       "6659       Heart hurts  0.0  0.0  0.0 -0.447214  0.447214 -0.447214  0.0  0.0   \n",
+       "6660        Skin issue  0.0  0.0  0.0  0.000000  0.000000  0.000000  0.0  0.0   \n",
+       "\n",
+       "             8  ...        65   66       67        68   69   70   71  \\\n",
+       "0     0.000000  ...  0.000000  0.0  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "1     0.000000  ...  0.000000  0.0  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "2     0.000000  ...  0.000000  0.0  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "3     0.000000  ...  0.534522  0.0  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "4     0.000000  ...  0.000000  0.0  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "...        ...  ...       ...  ...      ...       ...  ...  ...  ...   \n",
+       "6656  0.353553  ...  0.000000  0.0  0.00000  0.707107  0.0  0.0  0.0   \n",
+       "6657  0.000000  ...  0.000000  0.0 -0.57735  0.000000  0.0  0.0  0.0   \n",
+       "6658  0.000000  ...  0.000000  0.5  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "6659  0.000000  ...  0.000000  0.0  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "6660  0.000000  ...  0.000000  0.0  0.00000  0.000000  0.0  0.0  0.0   \n",
+       "\n",
+       "            72   73   74  \n",
+       "0     0.000000  0.0  0.0  \n",
+       "1     0.000000  0.0  0.0  \n",
+       "2     0.000000  0.0  0.0  \n",
+       "3    -0.534522  0.0  0.0  \n",
+       "4     0.000000  0.0  0.0  \n",
+       "...        ...  ...  ...  \n",
+       "6656  0.353553  0.0  0.0  \n",
+       "6657  0.000000  0.0  0.0  \n",
+       "6658  0.000000  0.0  0.0  \n",
+       "6659  0.000000  0.0  0.0  \n",
+       "6660  0.000000  0.0  0.0  \n",
+       "\n",
+       "[6661 rows x 76 columns]"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Use hashing vectorizer to consider the importance of counts and frequencies of words\n",
+    "# carefully initiate the pre-defined matrix size. \n",
+    "#Small numbers of features are likely to cause hash collisions\n",
+    "#large numbers will cause larger coefficient dimensions in linear learners\n",
+    "# good for large dataset since it don't store the vocabulary and easily been loaded when needed\n",
+    "\n",
+    "from sklearn.feature_extraction.text import HashingVectorizer\n",
+    "\n",
+    "# determine the size of matrix and generate hash vectorizer\n",
+    "n = Text['prompt'].nunique()\n",
+    "text_hashvectorize = HashingVectorizer(n_features = n*3)\n",
+    "X_hash = text_hashvectorize.fit_transform(Text[\"new_text\"])\n",
+    "\n",
+    "df_hash_vectorize = pd.DataFrame(X_hash.toarray())\n",
+    "\n",
+    "# concatenate prompt column with hash vectorized matrix\n",
+    "text_hash_vectorize = pd.concat([Text[\"prompt\"], df_hash_vectorize], axis = 1)\n",
+    "text_hash_vectorize\n",
+    "\n",
+    "#text_hash_vectorize.to_csv(f\"hash_vectorize.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "81396cbc",
+   "metadata": {},
+   "source": [
+    "## CountVectorizer (Bag of Words)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "bcf420d8",
+   "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>prompt</th>\n",
+       "      <th>abdomen</th>\n",
+       "      <th>abdominal</th>\n",
+       "      <th>able</th>\n",
+       "      <th>abronchial</th>\n",
+       "      <th>accident</th>\n",
+       "      <th>accidentally</th>\n",
+       "      <th>accompanied</th>\n",
+       "      <th>ache</th>\n",
+       "      <th>aching</th>\n",
+       "      <th>...</th>\n",
+       "      <th>wound</th>\n",
+       "      <th>wrap</th>\n",
+       "      <th>write</th>\n",
+       "      <th>wrong</th>\n",
+       "      <th>yard</th>\n",
+       "      <th>year</th>\n",
+       "      <th>yellow</th>\n",
+       "      <th>yesterday</th>\n",
+       "      <th>young</th>\n",
+       "      <th>zit</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Emotional pain</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hair falling out</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>Stomach ache</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6657</th>\n",
+       "      <td>Open wound</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6658</th>\n",
+       "      <td>Joint pain</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6659</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6660</th>\n",
+       "      <td>Skin issue</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6661 rows × 954 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                prompt  abdomen  abdominal  able  abronchial  accident  \\\n",
+       "0       Emotional pain        0          0     0           0         0   \n",
+       "1     Hair falling out        0          0     0           0         0   \n",
+       "2          Heart hurts        0          0     0           0         0   \n",
+       "3       Infected wound        0          0     0           0         0   \n",
+       "4       Infected wound        0          0     0           0         0   \n",
+       "...                ...      ...        ...   ...         ...       ...   \n",
+       "6656      Stomach ache        0          0     0           0         0   \n",
+       "6657        Open wound        0          0     0           0         0   \n",
+       "6658        Joint pain        0          0     0           0         0   \n",
+       "6659       Heart hurts        0          0     0           0         0   \n",
+       "6660        Skin issue        0          0     0           0         0   \n",
+       "\n",
+       "      accidentally  accompanied  ache  aching  ...  wound  wrap  write  wrong  \\\n",
+       "0                0            0     0       0  ...      0     0      0      0   \n",
+       "1                0            0     0       0  ...      0     0      0      0   \n",
+       "2                0            0     0       0  ...      0     0      0      0   \n",
+       "3                0            0     0       0  ...      0     0      0      0   \n",
+       "4                0            0     0       1  ...      0     0      0      0   \n",
+       "...            ...          ...   ...     ...  ...    ...   ...    ...    ...   \n",
+       "6656             0            0     0       0  ...      0     0      0      0   \n",
+       "6657             0            0     0       0  ...      0     0      0      0   \n",
+       "6658             0            0     0       0  ...      0     0      0      0   \n",
+       "6659             0            0     0       0  ...      0     0      0      0   \n",
+       "6660             0            0     0       0  ...      0     0      0      0   \n",
+       "\n",
+       "      yard  year  yellow  yesterday  young  zit  \n",
+       "0        0     0       0          0      0    0  \n",
+       "1        0     0       0          0      0    0  \n",
+       "2        0     0       0          0      0    0  \n",
+       "3        0     0       0          0      0    0  \n",
+       "4        0     0       0          0      0    0  \n",
+       "...    ...   ...     ...        ...    ...  ...  \n",
+       "6656     0     0       0          0      0    0  \n",
+       "6657     0     0       0          0      0    0  \n",
+       "6658     0     0       0          0      0    0  \n",
+       "6659     0     0       0          0      0    0  \n",
+       "6660     0     0       0          0      0    0  \n",
+       "\n",
+       "[6661 rows x 954 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# bag_of_words feature extract\n",
+    "\n",
+    "from sklearn.feature_extraction.text import CountVectorizer\n",
+    "\n",
+    "# extract feature using bag_of_words\n",
+    "bag_word = CountVectorizer()\n",
+    "feature_bow = bag_word.fit_transform(Text[\"new_text\"].values)\n",
+    "\n",
+    "# maping feature \n",
+    "df_bow = pd.DataFrame(feature_bow.todense().tolist(), columns = bag_word.get_feature_names())\n",
+    "\n",
+    "# concatenate prompt column with bow matrix\n",
+    "bag_word_df = pd.concat([Text['prompt'], df_bow], axis = 1)\n",
+    "bag_word_df\n",
+    "\n",
+    "#bag_word_df.to_csv('bag_word_df.csv',index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fa5c3196",
+   "metadata": {},
+   "source": [
+    "# Word2Vec"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "4f1d0ab2",
+   "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>prompt</th>\n",
+       "      <th>0</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>4</th>\n",
+       "      <th>5</th>\n",
+       "      <th>6</th>\n",
+       "      <th>7</th>\n",
+       "      <th>8</th>\n",
+       "      <th>...</th>\n",
+       "      <th>90</th>\n",
+       "      <th>91</th>\n",
+       "      <th>92</th>\n",
+       "      <th>93</th>\n",
+       "      <th>94</th>\n",
+       "      <th>95</th>\n",
+       "      <th>96</th>\n",
+       "      <th>97</th>\n",
+       "      <th>98</th>\n",
+       "      <th>99</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Emotional pain</td>\n",
+       "      <td>-0.076306</td>\n",
+       "      <td>0.144870</td>\n",
+       "      <td>0.083767</td>\n",
+       "      <td>0.095612</td>\n",
+       "      <td>0.066345</td>\n",
+       "      <td>-0.226576</td>\n",
+       "      <td>0.062405</td>\n",
+       "      <td>0.405740</td>\n",
+       "      <td>-0.126576</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.176404</td>\n",
+       "      <td>0.113494</td>\n",
+       "      <td>0.057781</td>\n",
+       "      <td>0.016227</td>\n",
+       "      <td>0.233634</td>\n",
+       "      <td>0.097241</td>\n",
+       "      <td>0.095603</td>\n",
+       "      <td>-0.143557</td>\n",
+       "      <td>0.059992</td>\n",
+       "      <td>-0.057431</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Hair falling out</td>\n",
+       "      <td>-0.129883</td>\n",
+       "      <td>0.241534</td>\n",
+       "      <td>0.109110</td>\n",
+       "      <td>0.142094</td>\n",
+       "      <td>0.119210</td>\n",
+       "      <td>-0.391520</td>\n",
+       "      <td>0.095924</td>\n",
+       "      <td>0.651806</td>\n",
+       "      <td>-0.195424</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.297623</td>\n",
+       "      <td>0.181590</td>\n",
+       "      <td>0.079316</td>\n",
+       "      <td>0.022262</td>\n",
+       "      <td>0.362759</td>\n",
+       "      <td>0.156125</td>\n",
+       "      <td>0.161772</td>\n",
+       "      <td>-0.229045</td>\n",
+       "      <td>0.110560</td>\n",
+       "      <td>-0.102366</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>-0.138489</td>\n",
+       "      <td>0.248988</td>\n",
+       "      <td>0.117522</td>\n",
+       "      <td>0.148499</td>\n",
+       "      <td>0.124959</td>\n",
+       "      <td>-0.413804</td>\n",
+       "      <td>0.112814</td>\n",
+       "      <td>0.685549</td>\n",
+       "      <td>-0.227354</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.313381</td>\n",
+       "      <td>0.186616</td>\n",
+       "      <td>0.080913</td>\n",
+       "      <td>0.023393</td>\n",
+       "      <td>0.373568</td>\n",
+       "      <td>0.158586</td>\n",
+       "      <td>0.175424</td>\n",
+       "      <td>-0.229141</td>\n",
+       "      <td>0.104984</td>\n",
+       "      <td>-0.111788</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>-0.133123</td>\n",
+       "      <td>0.237146</td>\n",
+       "      <td>0.101629</td>\n",
+       "      <td>0.131735</td>\n",
+       "      <td>0.109698</td>\n",
+       "      <td>-0.401296</td>\n",
+       "      <td>0.086977</td>\n",
+       "      <td>0.651547</td>\n",
+       "      <td>-0.183476</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.297889</td>\n",
+       "      <td>0.185091</td>\n",
+       "      <td>0.068119</td>\n",
+       "      <td>0.004698</td>\n",
+       "      <td>0.345084</td>\n",
+       "      <td>0.155465</td>\n",
+       "      <td>0.173034</td>\n",
+       "      <td>-0.211400</td>\n",
+       "      <td>0.103105</td>\n",
+       "      <td>-0.107905</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Infected wound</td>\n",
+       "      <td>-0.151671</td>\n",
+       "      <td>0.268915</td>\n",
+       "      <td>0.121587</td>\n",
+       "      <td>0.152607</td>\n",
+       "      <td>0.134066</td>\n",
+       "      <td>-0.438037</td>\n",
+       "      <td>0.122253</td>\n",
+       "      <td>0.726806</td>\n",
+       "      <td>-0.223599</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.338446</td>\n",
+       "      <td>0.204209</td>\n",
+       "      <td>0.077224</td>\n",
+       "      <td>0.024273</td>\n",
+       "      <td>0.405920</td>\n",
+       "      <td>0.180556</td>\n",
+       "      <td>0.188474</td>\n",
+       "      <td>-0.251705</td>\n",
+       "      <td>0.119200</td>\n",
+       "      <td>-0.121408</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6656</th>\n",
+       "      <td>Stomach ache</td>\n",
+       "      <td>-0.115034</td>\n",
+       "      <td>0.212170</td>\n",
+       "      <td>0.102355</td>\n",
+       "      <td>0.127260</td>\n",
+       "      <td>0.111012</td>\n",
+       "      <td>-0.356944</td>\n",
+       "      <td>0.088314</td>\n",
+       "      <td>0.597305</td>\n",
+       "      <td>-0.175593</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.266632</td>\n",
+       "      <td>0.163279</td>\n",
+       "      <td>0.060654</td>\n",
+       "      <td>0.009344</td>\n",
+       "      <td>0.324350</td>\n",
+       "      <td>0.141180</td>\n",
+       "      <td>0.152941</td>\n",
+       "      <td>-0.196083</td>\n",
+       "      <td>0.092586</td>\n",
+       "      <td>-0.085266</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6657</th>\n",
+       "      <td>Open wound</td>\n",
+       "      <td>-0.082318</td>\n",
+       "      <td>0.157860</td>\n",
+       "      <td>0.067382</td>\n",
+       "      <td>0.077496</td>\n",
+       "      <td>0.079493</td>\n",
+       "      <td>-0.261548</td>\n",
+       "      <td>0.057403</td>\n",
+       "      <td>0.424254</td>\n",
+       "      <td>-0.119038</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.188105</td>\n",
+       "      <td>0.113617</td>\n",
+       "      <td>0.041503</td>\n",
+       "      <td>0.008333</td>\n",
+       "      <td>0.222731</td>\n",
+       "      <td>0.104138</td>\n",
+       "      <td>0.115030</td>\n",
+       "      <td>-0.143077</td>\n",
+       "      <td>0.069211</td>\n",
+       "      <td>-0.070054</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6658</th>\n",
+       "      <td>Joint pain</td>\n",
+       "      <td>-0.131884</td>\n",
+       "      <td>0.256373</td>\n",
+       "      <td>0.128843</td>\n",
+       "      <td>0.154810</td>\n",
+       "      <td>0.125906</td>\n",
+       "      <td>-0.405019</td>\n",
+       "      <td>0.116038</td>\n",
+       "      <td>0.680855</td>\n",
+       "      <td>-0.219588</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.304285</td>\n",
+       "      <td>0.186892</td>\n",
+       "      <td>0.076803</td>\n",
+       "      <td>0.021421</td>\n",
+       "      <td>0.373776</td>\n",
+       "      <td>0.150687</td>\n",
+       "      <td>0.167834</td>\n",
+       "      <td>-0.229338</td>\n",
+       "      <td>0.108377</td>\n",
+       "      <td>-0.089778</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6659</th>\n",
+       "      <td>Heart hurts</td>\n",
+       "      <td>-0.110055</td>\n",
+       "      <td>0.223382</td>\n",
+       "      <td>0.098995</td>\n",
+       "      <td>0.134150</td>\n",
+       "      <td>0.111787</td>\n",
+       "      <td>-0.366419</td>\n",
+       "      <td>0.095155</td>\n",
+       "      <td>0.613407</td>\n",
+       "      <td>-0.187147</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.274841</td>\n",
+       "      <td>0.174951</td>\n",
+       "      <td>0.079736</td>\n",
+       "      <td>0.016118</td>\n",
+       "      <td>0.335088</td>\n",
+       "      <td>0.144033</td>\n",
+       "      <td>0.159089</td>\n",
+       "      <td>-0.211854</td>\n",
+       "      <td>0.105012</td>\n",
+       "      <td>-0.087460</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6660</th>\n",
+       "      <td>Skin issue</td>\n",
+       "      <td>-0.101181</td>\n",
+       "      <td>0.170834</td>\n",
+       "      <td>0.069351</td>\n",
+       "      <td>0.099832</td>\n",
+       "      <td>0.083726</td>\n",
+       "      <td>-0.287154</td>\n",
+       "      <td>0.061585</td>\n",
+       "      <td>0.473658</td>\n",
+       "      <td>-0.139816</td>\n",
+       "      <td>...</td>\n",
+       "      <td>0.218798</td>\n",
+       "      <td>0.127264</td>\n",
+       "      <td>0.041160</td>\n",
+       "      <td>0.004311</td>\n",
+       "      <td>0.256762</td>\n",
+       "      <td>0.120410</td>\n",
+       "      <td>0.131737</td>\n",
+       "      <td>-0.151181</td>\n",
+       "      <td>0.088392</td>\n",
+       "      <td>-0.077834</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6661 rows × 101 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                prompt         0         1         2         3         4  \\\n",
+       "0       Emotional pain -0.076306  0.144870  0.083767  0.095612  0.066345   \n",
+       "1     Hair falling out -0.129883  0.241534  0.109110  0.142094  0.119210   \n",
+       "2          Heart hurts -0.138489  0.248988  0.117522  0.148499  0.124959   \n",
+       "3       Infected wound -0.133123  0.237146  0.101629  0.131735  0.109698   \n",
+       "4       Infected wound -0.151671  0.268915  0.121587  0.152607  0.134066   \n",
+       "...                ...       ...       ...       ...       ...       ...   \n",
+       "6656      Stomach ache -0.115034  0.212170  0.102355  0.127260  0.111012   \n",
+       "6657        Open wound -0.082318  0.157860  0.067382  0.077496  0.079493   \n",
+       "6658        Joint pain -0.131884  0.256373  0.128843  0.154810  0.125906   \n",
+       "6659       Heart hurts -0.110055  0.223382  0.098995  0.134150  0.111787   \n",
+       "6660        Skin issue -0.101181  0.170834  0.069351  0.099832  0.083726   \n",
+       "\n",
+       "             5         6         7         8  ...        90        91  \\\n",
+       "0    -0.226576  0.062405  0.405740 -0.126576  ...  0.176404  0.113494   \n",
+       "1    -0.391520  0.095924  0.651806 -0.195424  ...  0.297623  0.181590   \n",
+       "2    -0.413804  0.112814  0.685549 -0.227354  ...  0.313381  0.186616   \n",
+       "3    -0.401296  0.086977  0.651547 -0.183476  ...  0.297889  0.185091   \n",
+       "4    -0.438037  0.122253  0.726806 -0.223599  ...  0.338446  0.204209   \n",
+       "...        ...       ...       ...       ...  ...       ...       ...   \n",
+       "6656 -0.356944  0.088314  0.597305 -0.175593  ...  0.266632  0.163279   \n",
+       "6657 -0.261548  0.057403  0.424254 -0.119038  ...  0.188105  0.113617   \n",
+       "6658 -0.405019  0.116038  0.680855 -0.219588  ...  0.304285  0.186892   \n",
+       "6659 -0.366419  0.095155  0.613407 -0.187147  ...  0.274841  0.174951   \n",
+       "6660 -0.287154  0.061585  0.473658 -0.139816  ...  0.218798  0.127264   \n",
+       "\n",
+       "            92        93        94        95        96        97        98  \\\n",
+       "0     0.057781  0.016227  0.233634  0.097241  0.095603 -0.143557  0.059992   \n",
+       "1     0.079316  0.022262  0.362759  0.156125  0.161772 -0.229045  0.110560   \n",
+       "2     0.080913  0.023393  0.373568  0.158586  0.175424 -0.229141  0.104984   \n",
+       "3     0.068119  0.004698  0.345084  0.155465  0.173034 -0.211400  0.103105   \n",
+       "4     0.077224  0.024273  0.405920  0.180556  0.188474 -0.251705  0.119200   \n",
+       "...        ...       ...       ...       ...       ...       ...       ...   \n",
+       "6656  0.060654  0.009344  0.324350  0.141180  0.152941 -0.196083  0.092586   \n",
+       "6657  0.041503  0.008333  0.222731  0.104138  0.115030 -0.143077  0.069211   \n",
+       "6658  0.076803  0.021421  0.373776  0.150687  0.167834 -0.229338  0.108377   \n",
+       "6659  0.079736  0.016118  0.335088  0.144033  0.159089 -0.211854  0.105012   \n",
+       "6660  0.041160  0.004311  0.256762  0.120410  0.131737 -0.151181  0.088392   \n",
+       "\n",
+       "            99  \n",
+       "0    -0.057431  \n",
+       "1    -0.102366  \n",
+       "2    -0.111788  \n",
+       "3    -0.107905  \n",
+       "4    -0.121408  \n",
+       "...        ...  \n",
+       "6656 -0.085266  \n",
+       "6657 -0.070054  \n",
+       "6658 -0.089778  \n",
+       "6659 -0.087460  \n",
+       "6660 -0.077834  \n",
+       "\n",
+       "[6661 rows x 101 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# word to vec\n",
+    "# using the Word2Vec algorithm with our corpus\n",
+    "# it returns a vector representation for each word\n",
+    "# to extend these word vectors and generate vectors on document level\n",
+    "# we compute an average of all the words in the document \n",
+    "\n",
+    "from gensim.models import Word2Vec\n",
+    "\n",
+    "# Create the list of list format for gensim modeling \n",
+    "Text['new_text_clean'] = Text['new_text'].apply(lambda x: x.split(\" \"))\n",
+    "\n",
+    "# Train the word2vec model\n",
+    "w2v_model = Word2Vec(Text['new_text_clean'], min_count = 1,vector_size = 100, window = 5)\n",
+    "\n",
+    "# Take the average of the word vectors for the words contained in each sentence\n",
+    "def word_avg_vect(data, model, num_features):\n",
+    "    words = set(model.wv.index_to_key)\n",
+    "    X_vect = np.array([np.array([model.wv[i] for i in s if i in words]) for s in data])\n",
+    "    X_vect_avg = []\n",
+    "    for v in X_vect:\n",
+    "        if v.size:\n",
+    "            X_vect_avg.append(v.mean(axis = 0))\n",
+    "        else:\n",
+    "            X_vect_avg.append(np.zeros(num_features, dtype = float))\n",
+    "\n",
+    "    df_vect_avg = pd.DataFrame(X_vect_avg)\n",
+    "    return df_vect_avg\n",
+    "\n",
+    "X_w2v = word_avg_vect(Text['new_text_clean'], w2v_model, 100)\n",
+    "# concatenate prompt column with averaged w2v matrix\n",
+    "df_w2v = pd.concat([Text[\"prompt\"], X_w2v], axis = 1)\n",
+    "df_w2v\n",
+    "\n",
+    "#df_w2v.to_csv(f\"df_w2v.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2588ab9e",
+   "metadata": {},
+   "source": [
+    "## PCA "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "14c6e468",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# fucntion for PCA as feature selection \n",
+    "# set cutoff value is number of components that represents 99% of variance \n",
+    "# return reduced dataset with appropriate PCA components represented 99% variance\n",
+    "from sklearn.decomposition import PCA\n",
+    "import pickle\n",
+    "def PCA_project(data, data_name=\"\", threshold = 99):\n",
+    "    max_component = data.shape[1]\n",
+    "    cutoff = threshold\n",
+    "    covar_matrix = PCA(n_components = max_component)\n",
+    "    covar_matrix.fit(data)\n",
+    "    variance = covar_matrix.explained_variance_ratio_\n",
+    "    var = np.cumsum(np.round(variance, decimals = 4)*100)\n",
+    "    index = 0\n",
+    "    for i in range(len(var)):\n",
+    "        \n",
+    "        if np.round(var[i]) < cutoff:\n",
+    "            index += 1\n",
+    "        else:\n",
+    "            break\n",
+    "    principal=PCA(n_components=index)\n",
+    "    principal.fit(data)\n",
+    "    pickle.dump(principal, open(data_name+'.pkl','wb'))\n",
+    "    print('%s reduce features from %d to %d'% (data_name,max_component, index))\n",
+    "    return principal.transform(data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "275fc24d",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "bag of words reduce features from 953 to 464\n",
+      "tf_idf reduce features from 953 to 507\n",
+      "hash_vectorize reduce features from 75 to 69\n",
+      "word2vec reduce features from 100 to 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "#apply PCA on our 4 dataset:bag of words, tf_idf, hash, word2vec\n",
+    "bow_P= PCA_project(df_bow, 'bag of words')\n",
+    "tf_idf_P= PCA_project(df_tf_idf, 'tf_idf')\n",
+    "hash_P= PCA_project(df_hash_vectorize, 'hash_vectorize')\n",
+    "w2v_P= PCA_project(np.array(X_w2v), 'word2vec')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c1823f94",
+   "metadata": {},
+   "source": [
+    "## Split Data"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "d0bd1b71",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#split data\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "y = Text[\"prompt\"]\n",
+    "X_bow_train, X_bow_test, y_bow_train, y_bow_test = train_test_split(bow_P,y,test_size = 0.2, random_state =3, stratify = y)\n",
+    "X_tf_train, X_tf_test, y_tf_train, y_tf_test = train_test_split(tf_idf_P,y,test_size = 0.2, random_state =3, stratify = y)\n",
+    "X_hash_train, X_hash_test, y_hash_train, y_hash_test = train_test_split(hash_P,y,test_size = 0.2, random_state =3, stratify = y)\n",
+    "X_w2v_train, X_w2v_test, y_w2v_train, y_w2v_test = train_test_split(w2v_P,y,test_size = 0.2, random_state =3, stratify = y)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5e9ef43f",
+   "metadata": {},
+   "source": [
+    "## Modeling and Evaluation"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e53a0d16",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#define a funtion to generate a nice matric table\n",
+    "from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, make_scorer\n",
+    "def matric_table(model_list, name_list,y_data, X_data):\n",
+    "    result = []\n",
+    "    for m,n,a,b in zip(model_list, name_list, y_data, X_data):\n",
+    "        report = []\n",
+    "        report.append(n)\n",
+    "        report.append(accuracy_score(a[0], m.predict(b[0])) * 100)\n",
+    "        report.append(accuracy_score(a[1], m.predict(b[1])) * 100)\n",
+    "        report.append(recall_score(a[1], m.predict(b[1]),average = 'weighted') * 100)\n",
+    "        report.append(precision_score(a[1], m.predict(b[1]),average = 'weighted') * 100)\n",
+    "        report.append(f1_score(a[1], m.predict(b[1]),average = 'weighted') * 100)\n",
+    "        result.append(report)\n",
+    "    df = pd.DataFrame(data = result, columns=['Model', 'Training Accuracy %', 'Testing Accuracy %','Testing precision %', 'Testing recall %', 'Testing f1_score %'])\n",
+    "    df = df.set_index('Model')\n",
+    "    return df.style.highlight_max(color = 'lightgreen', axis = 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "b9ece7e9",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "RandomForestClassifier(max_features=None, n_jobs=-1, oob_score=True,\n",
+       "                       random_state=0)"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#train model for randomforest\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "# train model with all features\n",
+    "rf_bow = RandomForestClassifier(n_estimators=100,\n",
+    "                                max_features=None,\n",
+    "                                oob_score=True,\n",
+    "                                n_jobs=-1,\n",
+    "                                random_state=0)\n",
+    "rf_tf = RandomForestClassifier(n_estimators=100,\n",
+    "                                max_features=None,\n",
+    "                                oob_score=True,\n",
+    "                                n_jobs=-1,\n",
+    "                                random_state=0)\n",
+    "rf_hash = RandomForestClassifier(n_estimators=100,\n",
+    "                                max_features=None,\n",
+    "                                oob_score=True,\n",
+    "                                n_jobs=-1,\n",
+    "                                random_state=0)\n",
+    "rf_w2v = RandomForestClassifier(n_estimators=100,\n",
+    "                                max_features=None,\n",
+    "                                oob_score=True,\n",
+    "                                n_jobs=-1,\n",
+    "                                random_state=0)\n",
+    "\n",
+    "rf_bow.fit(X_bow_train, y_bow_train)\n",
+    "rf_tf.fit(X_tf_train, y_tf_train)\n",
+    "rf_hash.fit(X_hash_train, y_hash_train)\n",
+    "rf_w2v.fit(X_w2v_train, y_w2v_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8fb3a324",
+   "metadata": {},
+   "source": [
+    "### Random Forest with word2vec extracted data is the best among all Random Forest Classifers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "c085ca5c",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_b2cf1_row0_col0, #T_b2cf1_row0_col1, #T_b2cf1_row0_col2, #T_b2cf1_row1_col0, #T_b2cf1_row1_col1, #T_b2cf1_row1_col2, #T_b2cf1_row3_col0, #T_b2cf1_row3_col1, #T_b2cf1_row3_col2, #T_b2cf1_row3_col3, #T_b2cf1_row3_col4 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_b2cf1_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >Training Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Testing Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col2\" >Testing precision %</th>\n",
+       "      <th class=\"col_heading level0 col3\" >Testing recall %</th>\n",
+       "      <th class=\"col_heading level0 col4\" >Testing f1_score %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Model</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b2cf1_level0_row0\" class=\"row_heading level0 row0\" >Random Forest with bow</th>\n",
+       "      <td id=\"T_b2cf1_row0_col0\" class=\"data row0 col0\" >99.774775</td>\n",
+       "      <td id=\"T_b2cf1_row0_col1\" class=\"data row0 col1\" >99.549887</td>\n",
+       "      <td id=\"T_b2cf1_row0_col2\" class=\"data row0 col2\" >99.549887</td>\n",
+       "      <td id=\"T_b2cf1_row0_col3\" class=\"data row0 col3\" >99.574821</td>\n",
+       "      <td id=\"T_b2cf1_row0_col4\" class=\"data row0 col4\" >99.549617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b2cf1_level0_row1\" class=\"row_heading level0 row1\" >Random Forest with tf_idf</th>\n",
+       "      <td id=\"T_b2cf1_row1_col0\" class=\"data row1 col0\" >99.774775</td>\n",
+       "      <td id=\"T_b2cf1_row1_col1\" class=\"data row1 col1\" >99.549887</td>\n",
+       "      <td id=\"T_b2cf1_row1_col2\" class=\"data row1 col2\" >99.549887</td>\n",
+       "      <td id=\"T_b2cf1_row1_col3\" class=\"data row1 col3\" >99.574821</td>\n",
+       "      <td id=\"T_b2cf1_row1_col4\" class=\"data row1 col4\" >99.549617</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b2cf1_level0_row2\" class=\"row_heading level0 row2\" >Random Forest with hash</th>\n",
+       "      <td id=\"T_b2cf1_row2_col0\" class=\"data row2 col0\" >99.493243</td>\n",
+       "      <td id=\"T_b2cf1_row2_col1\" class=\"data row2 col1\" >99.174794</td>\n",
+       "      <td id=\"T_b2cf1_row2_col2\" class=\"data row2 col2\" >99.174794</td>\n",
+       "      <td id=\"T_b2cf1_row2_col3\" class=\"data row2 col3\" >99.194755</td>\n",
+       "      <td id=\"T_b2cf1_row2_col4\" class=\"data row2 col4\" >99.176006</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_b2cf1_level0_row3\" class=\"row_heading level0 row3\" >Random Forest with word2vec</th>\n",
+       "      <td id=\"T_b2cf1_row3_col0\" class=\"data row3 col0\" >99.774775</td>\n",
+       "      <td id=\"T_b2cf1_row3_col1\" class=\"data row3 col1\" >99.549887</td>\n",
+       "      <td id=\"T_b2cf1_row3_col2\" class=\"data row3 col2\" >99.549887</td>\n",
+       "      <td id=\"T_b2cf1_row3_col3\" class=\"data row3 col3\" >99.574932</td>\n",
+       "      <td id=\"T_b2cf1_row3_col4\" class=\"data row3 col4\" >99.549645</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x26e807addf0>"
+      ]
+     },
+     "execution_count": 17,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print result\n",
+    "model_list = [rf_bow,rf_tf,rf_hash,rf_w2v]\n",
+    "name_list = [\"Random Forest with bow\",\"Random Forest with tf_idf\", \"Random Forest with hash\",\"Random Forest with word2vec\"]\n",
+    "y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]\n",
+    "X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]\n",
+    "matric_table(model_list, name_list, y_data, X_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "cab665c2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "LogisticRegression()"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#train model for logistic Regression which is not inherently multiclass classifers. \n",
+    "#In this case, we use  defualt auto setting that if input is binary using OVR otherwise using multnomial\n",
+    "from sklearn.linear_model import LogisticRegression\n",
+    "\n",
+    "lr_bow = LogisticRegression()\n",
+    "lr_tf = LogisticRegression()\n",
+    "lr_hash = LogisticRegression()\n",
+    "lr_w2v = LogisticRegression()\n",
+    "\n",
+    "lr_bow.fit(X_bow_train, y_bow_train)\n",
+    "lr_tf.fit(X_tf_train, y_tf_train)\n",
+    "lr_hash.fit(X_hash_train, y_hash_train)\n",
+    "lr_w2v.fit(X_w2v_train, y_w2v_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "0cc61a26",
+   "metadata": {},
+   "source": [
+    "### Logistic Regression with bag-of-words extracted data is the best among all logistic Regression Classifers"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "53bdebb2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_aa4d1_row0_col0, #T_aa4d1_row0_col1, #T_aa4d1_row0_col2, #T_aa4d1_row0_col3, #T_aa4d1_row0_col4 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_aa4d1_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >Training Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Testing Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col2\" >Testing precision %</th>\n",
+       "      <th class=\"col_heading level0 col3\" >Testing recall %</th>\n",
+       "      <th class=\"col_heading level0 col4\" >Testing f1_score %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Model</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa4d1_level0_row0\" class=\"row_heading level0 row0\" >Logistic Regression with bow</th>\n",
+       "      <td id=\"T_aa4d1_row0_col0\" class=\"data row0 col0\" >99.568318</td>\n",
+       "      <td id=\"T_aa4d1_row0_col1\" class=\"data row0 col1\" >99.549887</td>\n",
+       "      <td id=\"T_aa4d1_row0_col2\" class=\"data row0 col2\" >99.549887</td>\n",
+       "      <td id=\"T_aa4d1_row0_col3\" class=\"data row0 col3\" >99.574027</td>\n",
+       "      <td id=\"T_aa4d1_row0_col4\" class=\"data row0 col4\" >99.549153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa4d1_level0_row1\" class=\"row_heading level0 row1\" >Logistic Regression with tf_idf</th>\n",
+       "      <td id=\"T_aa4d1_row1_col0\" class=\"data row1 col0\" >99.399399</td>\n",
+       "      <td id=\"T_aa4d1_row1_col1\" class=\"data row1 col1\" >99.474869</td>\n",
+       "      <td id=\"T_aa4d1_row1_col2\" class=\"data row1 col2\" >99.474869</td>\n",
+       "      <td id=\"T_aa4d1_row1_col3\" class=\"data row1 col3\" >99.500348</td>\n",
+       "      <td id=\"T_aa4d1_row1_col4\" class=\"data row1 col4\" >99.474060</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa4d1_level0_row2\" class=\"row_heading level0 row2\" >Logistic Regression with hash</th>\n",
+       "      <td id=\"T_aa4d1_row2_col0\" class=\"data row2 col0\" >85.904655</td>\n",
+       "      <td id=\"T_aa4d1_row2_col1\" class=\"data row2 col1\" >84.696174</td>\n",
+       "      <td id=\"T_aa4d1_row2_col2\" class=\"data row2 col2\" >84.696174</td>\n",
+       "      <td id=\"T_aa4d1_row2_col3\" class=\"data row2 col3\" >85.447843</td>\n",
+       "      <td id=\"T_aa4d1_row2_col4\" class=\"data row2 col4\" >84.617119</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_aa4d1_level0_row3\" class=\"row_heading level0 row3\" >Logistic Regressiont with word2vec</th>\n",
+       "      <td id=\"T_aa4d1_row3_col0\" class=\"data row3 col0\" >27.496246</td>\n",
+       "      <td id=\"T_aa4d1_row3_col1\" class=\"data row3 col1\" >27.906977</td>\n",
+       "      <td id=\"T_aa4d1_row3_col2\" class=\"data row3 col2\" >27.906977</td>\n",
+       "      <td id=\"T_aa4d1_row3_col3\" class=\"data row3 col3\" >22.557786</td>\n",
+       "      <td id=\"T_aa4d1_row3_col4\" class=\"data row3 col4\" >20.560358</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x26e8a4b2c10>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print result, the warning indicate there are some type the classifers never predict. but since data is imbalence in that rare class so the accuracy won't be impacted\n",
+    "model_list = [lr_bow,lr_tf,lr_hash,lr_w2v]\n",
+    "name_list = [\"Logistic Regression with bow\",\"Logistic Regression with tf_idf\", \"Logistic Regression with hash\",\"Logistic Regressiont with word2vec\"]\n",
+    "y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]\n",
+    "X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]\n",
+    "matric_table(model_list, name_list, y_data, X_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "436bec53",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "SVC()"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#train model for linear svm, which is not inherently multiclass classifers. \n",
+    "#In this case, we use One VS Rest to save computing \n",
+    "from sklearn.svm import SVC\n",
+    "\n",
+    "svc_bow = SVC(decision_function_shape='ovr')\n",
+    "svc_tf = SVC(decision_function_shape='ovr')\n",
+    "svc_hash = SVC(decision_function_shape='ovr')\n",
+    "svc_w2v = SVC(decision_function_shape='ovr')\n",
+    "\n",
+    "svc_bow.fit(X_bow_train, y_bow_train)\n",
+    "svc_tf.fit(X_tf_train, y_tf_train)\n",
+    "svc_hash.fit(X_hash_train, y_hash_train)\n",
+    "svc_w2v.fit(X_w2v_train, y_w2v_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d8dad1fb",
+   "metadata": {},
+   "source": [
+    "### Support Vectors Machine with tf_idf extracted data is the best among all SVCs "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "97e09358",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_0e4d5_row0_col1, #T_0e4d5_row0_col2, #T_0e4d5_row0_col3, #T_0e4d5_row0_col4, #T_0e4d5_row1_col0, #T_0e4d5_row1_col1, #T_0e4d5_row1_col2, #T_0e4d5_row1_col3, #T_0e4d5_row1_col4 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_0e4d5_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >Training Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Testing Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col2\" >Testing precision %</th>\n",
+       "      <th class=\"col_heading level0 col3\" >Testing recall %</th>\n",
+       "      <th class=\"col_heading level0 col4\" >Testing f1_score %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Model</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0e4d5_level0_row0\" class=\"row_heading level0 row0\" >SVC with bow</th>\n",
+       "      <td id=\"T_0e4d5_row0_col0\" class=\"data row0 col0\" >99.587087</td>\n",
+       "      <td id=\"T_0e4d5_row0_col1\" class=\"data row0 col1\" >99.549887</td>\n",
+       "      <td id=\"T_0e4d5_row0_col2\" class=\"data row0 col2\" >99.549887</td>\n",
+       "      <td id=\"T_0e4d5_row0_col3\" class=\"data row0 col3\" >99.574027</td>\n",
+       "      <td id=\"T_0e4d5_row0_col4\" class=\"data row0 col4\" >99.549153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0e4d5_level0_row1\" class=\"row_heading level0 row1\" >SVC with tf_idf</th>\n",
+       "      <td id=\"T_0e4d5_row1_col0\" class=\"data row1 col0\" >99.605856</td>\n",
+       "      <td id=\"T_0e4d5_row1_col1\" class=\"data row1 col1\" >99.549887</td>\n",
+       "      <td id=\"T_0e4d5_row1_col2\" class=\"data row1 col2\" >99.549887</td>\n",
+       "      <td id=\"T_0e4d5_row1_col3\" class=\"data row1 col3\" >99.574027</td>\n",
+       "      <td id=\"T_0e4d5_row1_col4\" class=\"data row1 col4\" >99.549153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0e4d5_level0_row2\" class=\"row_heading level0 row2\" >SVC with hash</th>\n",
+       "      <td id=\"T_0e4d5_row2_col0\" class=\"data row2 col0\" >99.324324</td>\n",
+       "      <td id=\"T_0e4d5_row2_col1\" class=\"data row2 col1\" >99.174794</td>\n",
+       "      <td id=\"T_0e4d5_row2_col2\" class=\"data row2 col2\" >99.174794</td>\n",
+       "      <td id=\"T_0e4d5_row2_col3\" class=\"data row2 col3\" >99.249930</td>\n",
+       "      <td id=\"T_0e4d5_row2_col4\" class=\"data row2 col4\" >99.176335</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_0e4d5_level0_row3\" class=\"row_heading level0 row3\" >SVC with word2vec</th>\n",
+       "      <td id=\"T_0e4d5_row3_col0\" class=\"data row3 col0\" >46.677928</td>\n",
+       "      <td id=\"T_0e4d5_row3_col1\" class=\"data row3 col1\" >43.735934</td>\n",
+       "      <td id=\"T_0e4d5_row3_col2\" class=\"data row3 col2\" >43.735934</td>\n",
+       "      <td id=\"T_0e4d5_row3_col3\" class=\"data row3 col3\" >45.756499</td>\n",
+       "      <td id=\"T_0e4d5_row3_col4\" class=\"data row3 col4\" >42.670226</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x26ef57f6880>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# print result\n",
+    "model_list = [svc_bow,svc_tf,svc_hash,svc_w2v]\n",
+    "name_list = [\"SVC with bow\",\"SVC with tf_idf\", \"SVC with hash\",\"SVC with word2vec\"]\n",
+    "y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]\n",
+    "X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]\n",
+    "matric_table(model_list, name_list, y_data, X_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "2b700817",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "KNeighborsClassifier(n_neighbors=3)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#train model for KNN\n",
+    "from sklearn.neighbors import KNeighborsClassifier\n",
+    "\n",
+    "knn_bow = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn_tf = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn_hash = KNeighborsClassifier(n_neighbors=3)\n",
+    "knn_w2v = KNeighborsClassifier(n_neighbors=3)\n",
+    "\n",
+    "knn_bow.fit(X_bow_train, y_bow_train)\n",
+    "knn_tf.fit(X_tf_train, y_tf_train)\n",
+    "knn_hash.fit(X_hash_train, y_hash_train)\n",
+    "knn_w2v.fit(X_w2v_train, y_w2v_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fedba1e6",
+   "metadata": {},
+   "source": [
+    "### K Nearest Neighours with bag-of-words extracted data is the best among all KNNs "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "bdbf7bb1",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_a4341_row0_col1, #T_a4341_row0_col2, #T_a4341_row0_col3, #T_a4341_row0_col4, #T_a4341_row1_col0, #T_a4341_row1_col1, #T_a4341_row1_col2 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_a4341_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >Training Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Testing Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col2\" >Testing precision %</th>\n",
+       "      <th class=\"col_heading level0 col3\" >Testing recall %</th>\n",
+       "      <th class=\"col_heading level0 col4\" >Testing f1_score %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Model</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4341_level0_row0\" class=\"row_heading level0 row0\" >KNN with bow</th>\n",
+       "      <td id=\"T_a4341_row0_col0\" class=\"data row0 col0\" >99.699700</td>\n",
+       "      <td id=\"T_a4341_row0_col1\" class=\"data row0 col1\" >99.699925</td>\n",
+       "      <td id=\"T_a4341_row0_col2\" class=\"data row0 col2\" >99.699925</td>\n",
+       "      <td id=\"T_a4341_row0_col3\" class=\"data row0 col3\" >99.708187</td>\n",
+       "      <td id=\"T_a4341_row0_col4\" class=\"data row0 col4\" >99.699770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4341_level0_row1\" class=\"row_heading level0 row1\" >KNN with tf_idf</th>\n",
+       "      <td id=\"T_a4341_row1_col0\" class=\"data row1 col0\" >99.831081</td>\n",
+       "      <td id=\"T_a4341_row1_col1\" class=\"data row1 col1\" >99.699925</td>\n",
+       "      <td id=\"T_a4341_row1_col2\" class=\"data row1 col2\" >99.699925</td>\n",
+       "      <td id=\"T_a4341_row1_col3\" class=\"data row1 col3\" >99.708048</td>\n",
+       "      <td id=\"T_a4341_row1_col4\" class=\"data row1 col4\" >99.699734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4341_level0_row2\" class=\"row_heading level0 row2\" >KNN with hash</th>\n",
+       "      <td id=\"T_a4341_row2_col0\" class=\"data row2 col0\" >99.418168</td>\n",
+       "      <td id=\"T_a4341_row2_col1\" class=\"data row2 col1\" >99.324831</td>\n",
+       "      <td id=\"T_a4341_row2_col2\" class=\"data row2 col2\" >99.324831</td>\n",
+       "      <td id=\"T_a4341_row2_col3\" class=\"data row2 col3\" >99.370567</td>\n",
+       "      <td id=\"T_a4341_row2_col4\" class=\"data row2 col4\" >99.326532</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_a4341_level0_row3\" class=\"row_heading level0 row3\" >KNN with word2vec</th>\n",
+       "      <td id=\"T_a4341_row3_col0\" class=\"data row3 col0\" >99.643393</td>\n",
+       "      <td id=\"T_a4341_row3_col1\" class=\"data row3 col1\" >99.549887</td>\n",
+       "      <td id=\"T_a4341_row3_col2\" class=\"data row3 col2\" >99.549887</td>\n",
+       "      <td id=\"T_a4341_row3_col3\" class=\"data row3 col3\" >99.574682</td>\n",
+       "      <td id=\"T_a4341_row3_col4\" class=\"data row3 col4\" >99.549582</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x26e8c81c310>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_list = [knn_bow,knn_tf,knn_hash,knn_w2v]\n",
+    "name_list = [\"KNN with bow\",\"KNN with tf_idf\", \"KNN with hash\",\"KNN with word2vec\"]\n",
+    "y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]\n",
+    "X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]\n",
+    "matric_table(model_list, name_list, y_data, X_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "7e7eacec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "GaussianNB()"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#train model for Naive Bayes. \n",
+    "#Bernoulli NB can only focus on a single keyword, \n",
+    "#but will also count how many times that keyword does not occur in the document\n",
+    "from sklearn.naive_bayes import GaussianNB\n",
+    "\n",
+    "\n",
+    "gnb_bow = GaussianNB()\n",
+    "gnb_tf = GaussianNB()\n",
+    "gnb_hash = GaussianNB()\n",
+    "gnb_w2v = GaussianNB()\n",
+    "\n",
+    "gnb_bow.fit(X_bow_train, y_bow_train)\n",
+    "gnb_tf.fit(X_tf_train, y_tf_train)\n",
+    "gnb_hash.fit(X_hash_train, y_hash_train)\n",
+    "gnb_w2v.fit(X_w2v_train, y_w2v_train)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5f2c49b3",
+   "metadata": {},
+   "source": [
+    "### Gauissian Naive Bayes with tf_idf extracted data is the best among all BNBs "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "a9e7e44e",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_cdc8c_row1_col0, #T_cdc8c_row1_col1, #T_cdc8c_row1_col2, #T_cdc8c_row1_col3, #T_cdc8c_row1_col4 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_cdc8c_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >Training Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Testing Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col2\" >Testing precision %</th>\n",
+       "      <th class=\"col_heading level0 col3\" >Testing recall %</th>\n",
+       "      <th class=\"col_heading level0 col4\" >Testing f1_score %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Model</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cdc8c_level0_row0\" class=\"row_heading level0 row0\" >Gaussian Naive Bayes with bow</th>\n",
+       "      <td id=\"T_cdc8c_row0_col0\" class=\"data row0 col0\" >90.653153</td>\n",
+       "      <td id=\"T_cdc8c_row0_col1\" class=\"data row0 col1\" >87.546887</td>\n",
+       "      <td id=\"T_cdc8c_row0_col2\" class=\"data row0 col2\" >87.546887</td>\n",
+       "      <td id=\"T_cdc8c_row0_col3\" class=\"data row0 col3\" >89.318237</td>\n",
+       "      <td id=\"T_cdc8c_row0_col4\" class=\"data row0 col4\" >87.667300</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cdc8c_level0_row1\" class=\"row_heading level0 row1\" >Gaussian Naive Bayes with tf_idf</th>\n",
+       "      <td id=\"T_cdc8c_row1_col0\" class=\"data row1 col0\" >90.878378</td>\n",
+       "      <td id=\"T_cdc8c_row1_col1\" class=\"data row1 col1\" >87.921980</td>\n",
+       "      <td id=\"T_cdc8c_row1_col2\" class=\"data row1 col2\" >87.921980</td>\n",
+       "      <td id=\"T_cdc8c_row1_col3\" class=\"data row1 col3\" >90.570234</td>\n",
+       "      <td id=\"T_cdc8c_row1_col4\" class=\"data row1 col4\" >88.415025</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cdc8c_level0_row2\" class=\"row_heading level0 row2\" >Gaussian Naive Bayes with hash</th>\n",
+       "      <td id=\"T_cdc8c_row2_col0\" class=\"data row2 col0\" >79.298048</td>\n",
+       "      <td id=\"T_cdc8c_row2_col1\" class=\"data row2 col1\" >75.843961</td>\n",
+       "      <td id=\"T_cdc8c_row2_col2\" class=\"data row2 col2\" >75.843961</td>\n",
+       "      <td id=\"T_cdc8c_row2_col3\" class=\"data row2 col3\" >77.614749</td>\n",
+       "      <td id=\"T_cdc8c_row2_col4\" class=\"data row2 col4\" >76.140452</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_cdc8c_level0_row3\" class=\"row_heading level0 row3\" >Gaussian Naive Bayes with word2vec</th>\n",
+       "      <td id=\"T_cdc8c_row3_col0\" class=\"data row3 col0\" >47.184685</td>\n",
+       "      <td id=\"T_cdc8c_row3_col1\" class=\"data row3 col1\" >48.012003</td>\n",
+       "      <td id=\"T_cdc8c_row3_col2\" class=\"data row3 col2\" >48.012003</td>\n",
+       "      <td id=\"T_cdc8c_row3_col3\" class=\"data row3 col3\" >50.995666</td>\n",
+       "      <td id=\"T_cdc8c_row3_col4\" class=\"data row3 col4\" >48.054367</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x26e8c826460>"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_list = [gnb_bow,gnb_tf,gnb_hash,gnb_w2v]\n",
+    "name_list = [\"Gaussian Naive Bayes with bow\",\"Gaussian Naive Bayes with tf_idf\", \"Gaussian Naive Bayes with hash\",\"Gaussian Naive Bayes with word2vec\"]\n",
+    "y_data = [[y_bow_train,y_bow_test], [y_tf_train,y_tf_test], [y_hash_train,y_hash_test],[y_w2v_train,y_w2v_test]]\n",
+    "X_data = [[X_bow_train,X_bow_test], [X_tf_train,X_tf_test], [X_hash_train,X_hash_test],[X_w2v_train,X_w2v_test]]\n",
+    "matric_table(model_list, name_list, y_data, X_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee368178",
+   "metadata": {},
+   "source": [
+    "### In conclusion, KNN with bage of words dataset is the winner among all classifers with highest score on test accuracy, precision, recall and F1 scores, The Random Forest is best on the Training accuracy. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "06f5d0bc",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_d67ec_row0_col0, #T_d67ec_row3_col1, #T_d67ec_row3_col2, #T_d67ec_row3_col3, #T_d67ec_row3_col4 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_d67ec_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >Training Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Testing Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col2\" >Testing precision %</th>\n",
+       "      <th class=\"col_heading level0 col3\" >Testing recall %</th>\n",
+       "      <th class=\"col_heading level0 col4\" >Testing f1_score %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Model</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d67ec_level0_row0\" class=\"row_heading level0 row0\" >Random Forest with w2v</th>\n",
+       "      <td id=\"T_d67ec_row0_col0\" class=\"data row0 col0\" >99.774775</td>\n",
+       "      <td id=\"T_d67ec_row0_col1\" class=\"data row0 col1\" >99.549887</td>\n",
+       "      <td id=\"T_d67ec_row0_col2\" class=\"data row0 col2\" >99.549887</td>\n",
+       "      <td id=\"T_d67ec_row0_col3\" class=\"data row0 col3\" >99.574932</td>\n",
+       "      <td id=\"T_d67ec_row0_col4\" class=\"data row0 col4\" >99.549645</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d67ec_level0_row1\" class=\"row_heading level0 row1\" >Logistic Regression with bow</th>\n",
+       "      <td id=\"T_d67ec_row1_col0\" class=\"data row1 col0\" >99.568318</td>\n",
+       "      <td id=\"T_d67ec_row1_col1\" class=\"data row1 col1\" >99.549887</td>\n",
+       "      <td id=\"T_d67ec_row1_col2\" class=\"data row1 col2\" >99.549887</td>\n",
+       "      <td id=\"T_d67ec_row1_col3\" class=\"data row1 col3\" >99.574027</td>\n",
+       "      <td id=\"T_d67ec_row1_col4\" class=\"data row1 col4\" >99.549153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d67ec_level0_row2\" class=\"row_heading level0 row2\" >SVC with tf</th>\n",
+       "      <td id=\"T_d67ec_row2_col0\" class=\"data row2 col0\" >99.605856</td>\n",
+       "      <td id=\"T_d67ec_row2_col1\" class=\"data row2 col1\" >99.549887</td>\n",
+       "      <td id=\"T_d67ec_row2_col2\" class=\"data row2 col2\" >99.549887</td>\n",
+       "      <td id=\"T_d67ec_row2_col3\" class=\"data row2 col3\" >99.574027</td>\n",
+       "      <td id=\"T_d67ec_row2_col4\" class=\"data row2 col4\" >99.549153</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d67ec_level0_row3\" class=\"row_heading level0 row3\" >KNN withbow</th>\n",
+       "      <td id=\"T_d67ec_row3_col0\" class=\"data row3 col0\" >99.699700</td>\n",
+       "      <td id=\"T_d67ec_row3_col1\" class=\"data row3 col1\" >99.699925</td>\n",
+       "      <td id=\"T_d67ec_row3_col2\" class=\"data row3 col2\" >99.699925</td>\n",
+       "      <td id=\"T_d67ec_row3_col3\" class=\"data row3 col3\" >99.708187</td>\n",
+       "      <td id=\"T_d67ec_row3_col4\" class=\"data row3 col4\" >99.699770</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_d67ec_level0_row4\" class=\"row_heading level0 row4\" >gaussian Naive Bayes with tf</th>\n",
+       "      <td id=\"T_d67ec_row4_col0\" class=\"data row4 col0\" >90.878378</td>\n",
+       "      <td id=\"T_d67ec_row4_col1\" class=\"data row4 col1\" >87.921980</td>\n",
+       "      <td id=\"T_d67ec_row4_col2\" class=\"data row4 col2\" >87.921980</td>\n",
+       "      <td id=\"T_d67ec_row4_col3\" class=\"data row4 col3\" >90.570234</td>\n",
+       "      <td id=\"T_d67ec_row4_col4\" class=\"data row4 col4\" >88.415025</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x26ec7562610>"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "### Find the best classifer among all classifers\n",
+    "model_list = [rf_w2v,lr_bow,svc_tf,knn_bow,gnb_tf]\n",
+    "name_list = [\"Random Forest with w2v\",\"Logistic Regression with bow\", \"SVC with tf\",\"KNN withbow\",\"gaussian Naive Bayes with tf\"]\n",
+    "y_data = [[y_w2v_train,y_w2v_test], [y_bow_train,y_bow_test], [y_tf_train,y_tf_test],[y_bow_train,y_bow_test],[y_tf_train,y_tf_test]]\n",
+    "X_data = [[X_w2v_train,X_w2v_test], [X_bow_train,X_bow_test], [X_tf_train,X_tf_test],[X_bow_train,X_bow_test],[X_tf_train,X_tf_test]]\n",
+    "matric_table(model_list, name_list, y_data, X_data)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "id": "f19f59d9",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(3, 'uniform', 1, 0.9969992498124531)\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Further tuning the KNN  classifer with bow\n",
+    "best_models = []\n",
+    "n_neighbors = [3,5,7,9]\n",
+    "weights = ['uniform','distance']\n",
+    "ps = [1,2]\n",
+    "\n",
+    "def KNN_clf(n_neighbors, weight, p):\n",
+    "    knn = KNeighborsClassifier(n_neighbors = n_neighbors, weights = weight,p = p)\n",
+    "    knn.fit(X_bow_train, y_bow_train)\n",
+    "    y_pred = knn.predict(X_bow_test)\n",
+    "    n = accuracy_score(y_bow_test,y_pred)\n",
+    "    best_models.append((n_neighbors, weight, p ,n))\n",
+    "\n",
+    "for c in n_neighbors:\n",
+    "    for w in weights:\n",
+    "        for p in ps:\n",
+    "            KNN_clf(c, w, p)\n",
+    "\n",
+    "print(max(best_models,key=lambda item:item[3]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "2a08cf65",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('gini', 13, 1, 50, 0.9969992498124531)\n"
+     ]
+    }
+   ],
+   "source": [
+    "##Further tuning the random forest classifer with w2v\n",
+    "best_models = []\n",
+    "crit = ['gini', 'entropy']\n",
+    "max_d = range(1,20,4)\n",
+    "min_s_leaf = range(1,20,4)\n",
+    "n_est = [50, 100, 200]\n",
+    "\n",
+    "def RF_clf(crit, max_d, min_s_leaf, n_est):\n",
+    "    forest = RandomForestClassifier(criterion=crit, max_depth=max_d, min_samples_leaf=min_s_leaf, n_estimators=n_est, random_state=1)\n",
+    "    forest.fit(X_w2v_train, y_w2v_train)\n",
+    "    y_pred = forest.predict(X_w2v_test)\n",
+    "    n = accuracy_score(y_w2v_test,y_pred)\n",
+    "    best_models.append((crit,max_d,min_s_leaf,n_est,n))\n",
+    "\n",
+    "\n",
+    "for c in crit:\n",
+    "    for md in max_d:\n",
+    "        for msl in min_s_leaf:\n",
+    "            for n_e in n_est:\n",
+    "                RF_clf(c, md, msl, n_e)\n",
+    "\n",
+    "print(max(best_models,key=lambda item:item[4]))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "965d3205",
+   "metadata": {},
+   "source": [
+    "### After tuning the Random Forest and KNN, Random Forest indeed is the best performance classifer"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "ad815863",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style type=\"text/css\">\n",
+       "#T_607af_row0_col1, #T_607af_row0_col2, #T_607af_row0_col3, #T_607af_row0_col4, #T_607af_row1_col0, #T_607af_row1_col1, #T_607af_row1_col2, #T_607af_row1_col3, #T_607af_row1_col4 {\n",
+       "  background-color: lightgreen;\n",
+       "}\n",
+       "</style>\n",
+       "<table id=\"T_607af_\">\n",
+       "  <thead>\n",
+       "    <tr>\n",
+       "      <th class=\"blank level0\" >&nbsp;</th>\n",
+       "      <th class=\"col_heading level0 col0\" >Training Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col1\" >Testing Accuracy %</th>\n",
+       "      <th class=\"col_heading level0 col2\" >Testing precision %</th>\n",
+       "      <th class=\"col_heading level0 col3\" >Testing recall %</th>\n",
+       "      <th class=\"col_heading level0 col4\" >Testing f1_score %</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th class=\"index_name level0\" >Model</th>\n",
+       "      <th class=\"blank col0\" >&nbsp;</th>\n",
+       "      <th class=\"blank col1\" >&nbsp;</th>\n",
+       "      <th class=\"blank col2\" >&nbsp;</th>\n",
+       "      <th class=\"blank col3\" >&nbsp;</th>\n",
+       "      <th class=\"blank col4\" >&nbsp;</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th id=\"T_607af_level0_row0\" class=\"row_heading level0 row0\" >Tuned KNN</th>\n",
+       "      <td id=\"T_607af_row0_col0\" class=\"data row0 col0\" >99.699700</td>\n",
+       "      <td id=\"T_607af_row0_col1\" class=\"data row0 col1\" >99.699925</td>\n",
+       "      <td id=\"T_607af_row0_col2\" class=\"data row0 col2\" >99.699925</td>\n",
+       "      <td id=\"T_607af_row0_col3\" class=\"data row0 col3\" >99.708048</td>\n",
+       "      <td id=\"T_607af_row0_col4\" class=\"data row0 col4\" >99.699734</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th id=\"T_607af_level0_row1\" class=\"row_heading level0 row1\" >Tuned Randome Forest</th>\n",
+       "      <td id=\"T_607af_row1_col0\" class=\"data row1 col0\" >99.774775</td>\n",
+       "      <td id=\"T_607af_row1_col1\" class=\"data row1 col1\" >99.699925</td>\n",
+       "      <td id=\"T_607af_row1_col2\" class=\"data row1 col2\" >99.699925</td>\n",
+       "      <td id=\"T_607af_row1_col3\" class=\"data row1 col3\" >99.708048</td>\n",
+       "      <td id=\"T_607af_row1_col4\" class=\"data row1 col4\" >99.699734</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n"
+      ],
+      "text/plain": [
+       "<pandas.io.formats.style.Styler at 0x26ee38be7f0>"
+      ]
+     },
+     "execution_count": 35,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "Knn_best = KNeighborsClassifier(n_neighbors = 3, weights = 'uniform' ,p = 1)\n",
+    "Rf_best = RandomForestClassifier(criterion='gini', max_depth=13, min_samples_leaf=1, n_estimators=50, random_state=1)\n",
+    "Knn_best.fit(X_bow_train, y_bow_train)\n",
+    "Rf_best.fit(X_w2v_train, y_w2v_train)\n",
+    "\n",
+    "model_list = [Knn_best, Rf_best]\n",
+    "name_list = [\"Tuned KNN\", 'Tuned Randome Forest']\n",
+    "y_data = [[y_bow_train,y_bow_test], [y_w2v_train,y_w2v_test] ]\n",
+    "X_data = [[X_bow_train,X_bow_test], [X_w2v_train,X_w2v_test]]\n",
+    "matric_table(model_list, name_list, y_data, X_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "349ca9c6",
+   "metadata": {},
+   "source": [
+    "## Draw The ROC and Recall-Precision Graphs to compare"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "d0553c0d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# too perfect, maybe we don't need graph just use our table to explain\n",
+    "import scikitplot as skplt\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "skplt.metrics.plot_roc(y_w2v_test, Rf_best.predict_proba(X_w2v_test)\n",
+    "                                          ,text_fontsize = 'small'\n",
+    "                                          ,title = ' ROC for best model'\n",
+    "                                          ,figsize = (12,8))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "id": "005ff0bb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 864x576 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "skplt.metrics.plot_precision_recall_curve(y_w2v_test, Rf_best.predict_proba(X_w2v_test),\n",
+    "                                          text_fontsize = 'small'\n",
+    "                                          ,title = 'PR Curve for best model'\n",
+    "                                          ,figsize = (12,8))\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cbb69977",
+   "metadata": {},
+   "source": [
+    "## Deployment: building a pipeline to automatically preprose the text and make classification"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "abb72553",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#save required model for deployment\n",
+    "pickle.dump(Rf_best, open('bestmodel.pkl','wb'))\n",
+    "pickle.dump(w2v_model, open('w2v_model.pkl','wb'))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "c54f2a3f",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "I have cut my finger because of playing football and I have to apply pain relief cream but it does not help\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array(['Injury from sports'], dtype=object)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "raw_input = input()\n",
+    "\n",
+    "def input_process(data):\n",
+    "    input_clean = phrase_cleanse(data)\n",
+    "    w2v_model = pickle.load(open('w2v_model.pkl', 'rb'))\n",
+    "    input_clean = [input_clean.split(\" \")]\n",
+    "    processed_input = word_avg_vect(input_clean, w2v_model, 100)\n",
+    "    pca_model = pickle.load(open('word2vec.pkl', 'rb')) \n",
+    "    test = pca_model.transform(processed_input)\n",
+    "    return test\n",
+    "\n",
+    "def pred(data):\n",
+    "    test = input_process(data)\n",
+    "    model = pickle.load(open('bestmodel.pkl', 'rb'))\n",
+    "    prediction = model.predict(test)\n",
+    "    return prediction\n",
+    "\n",
+    "pred(raw_input)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6315d091",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}