model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('fivethirtyeight')\n",
    "from sklearn.svm import OneClassSVM \n",
    "from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('predicted_review.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['True(1)/Deceptive(0)'] = (data['True(1)/Deceptive(0)'] == 'True').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.rename(columns={'True(1)/Deceptive(0)':'True'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "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>Review</th>\n",
       "      <th>True</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>This place is horrible, we were so excited to ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>For being fairly \"fast\" food.. Pei Wei (pronou...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>I decided to try it out, I'm celiac and theref...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>I'm not saying Pei Wei is the best asian food ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Sometimes the food is spot on and delicious an...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Review  True\n",
       "0  This place is horrible, we were so excited to ...     1\n",
       "1  For being fairly \"fast\" food.. Pei Wei (pronou...     1\n",
       "2  I decided to try it out, I'm celiac and theref...     1\n",
       "3  I'm not saying Pei Wei is the best asian food ...     1\n",
       "4  Sometimes the food is spot on and delicious an...     1"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 423516.,       0.,       0.,       0.,       0.,       0.,\n",
       "              0.,       0.,       0., 1554986.]),\n",
       " array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots(figsize = (10,7))\n",
    "plt.hist(data['True'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "true = data[data['True'] == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1554986 1978502\n"
     ]
    }
   ],
   "source": [
    "print(len(true),len(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "423516\n"
     ]
    }
   ],
   "source": [
    "print(len(data[data['True'] == 0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "true = true.sample(50000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove accents\n",
    "import unicodedata\n",
    "\n",
    "def remove_accents(input_str):\n",
    "    nfkd_form = unicodedata.normalize('NFKD', input_str)\n",
    "    only_ascii = nfkd_form.encode('ASCII', 'ignore')\n",
    "    return only_ascii.decode()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "true['Review'] = true['Review'].apply(remove_accents)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "#lower case\n",
    "true['Review'] = true['Review'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NLTK word tokenizer\n",
    "from nltk.tokenize import sent_tokenize, word_tokenize\n",
    "true['Tokenized'] = true['Review'].apply(word_tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
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       "      <th>1295857</th>\n",
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       "                                                    Review  True  \\\n",
       "377107   my family and i had a great time... service wa...     1   \n",
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       "795485   my friend that lives there took me there on we...     1   \n",
       "1295857  they make pierogi's from scratch, and so many ...     1   \n",
       "1083598  best trendy hot spot in glendale. i can't beli...     1   \n",
       "\n",
       "                                                 Tokenized  \n",
       "377107   [my, family, and, i, had, a, great, time, ...,...  \n",
       "696302   [well, ,, i, like, this, cranky, ole, building...  \n",
       "795485   [my, friend, that, lives, there, took, me, the...  \n",
       "1295857  [they, make, pierogi, 's, from, scratch, ,, an...  \n",
       "1083598  [best, trendy, hot, spot, in, glendale, ., i, ...  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://www.datacamp.com/community/tutorials/text-analytics-beginners-nltk\n",
    "# from nltk.probability import FreqDist\n",
    "# fdist = FreqDist(tokenized_word)\n",
    "# print(fdist)\n",
    "\n",
    "# fdist.most_common(2)\n",
    "\n",
    "# import matplotlib.pyplot as plt\n",
    "# fdist.plot(30,cumulative=False)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "#remove puncs and stopwords\n",
    "#NEED TO EXPAND stopwords for MAKEUP site\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "\n",
    "stopwords_ = set(stopwords.words('english'))\n",
    "punctuations_ = set(string.punctuation)\n",
    "\n",
    "true['Tokenized2'] = true['Tokenized'].apply(lambda x: [item for item in x if item not in stopwords_  and not item in punctuations_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#NEED TO REMOVE NUMBERS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>well, i like this cranky ole building and the ...</td>\n",
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       "      <th>1295857</th>\n",
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       "      <td>[make, pierogi, 's, scratch, many, varieties, ...</td>\n",
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       "      <th>...</th>\n",
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       "      <th>742358</th>\n",
       "      <td>the cabo wabo cantina is another one of those ...</td>\n",
       "      <td>1</td>\n",
       "      <td>[the, cabo, wabo, cantina, is, another, one, o...</td>\n",
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       "      <td>food was delish! we got the sampler with mac &amp;...</td>\n",
       "      <td>1</td>\n",
       "      <td>[food, was, delish, !, we, got, the, sampler, ...</td>\n",
       "      <td>[food, delish, got, sampler, mac, cheese, croq...</td>\n",
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       "    <tr>\n",
       "      <th>651741</th>\n",
       "      <td>been going here since the 90s. the building ha...</td>\n",
       "      <td>1</td>\n",
       "      <td>[been, going, here, since, the, 90s, ., the, b...</td>\n",
       "      <td>[going, since, 90s, building, changed, food, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>682319</th>\n",
       "      <td>i like chili's, i know what i am getting so i ...</td>\n",
       "      <td>1</td>\n",
       "      <td>[i, like, chili, 's, ,, i, know, what, i, am, ...</td>\n",
       "      <td>[like, chili, 's, know, getting, like, food, s...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1130462</th>\n",
       "      <td>had a really great early dinner here. the crep...</td>\n",
       "      <td>1</td>\n",
       "      <td>[had, a, really, great, early, dinner, here, ....</td>\n",
       "      <td>[really, great, early, dinner, crepes, marseil...</td>\n",
       "    </tr>\n",
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       "<p>50000 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Review  True  \\\n",
       "377107   my family and i had a great time... service wa...     1   \n",
       "696302   well, i like this cranky ole building and the ...     1   \n",
       "795485   my friend that lives there took me there on we...     1   \n",
       "1295857  they make pierogi's from scratch, and so many ...     1   \n",
       "1083598  best trendy hot spot in glendale. i can't beli...     1   \n",
       "...                                                    ...   ...   \n",
       "742358   the cabo wabo cantina is another one of those ...     1   \n",
       "296348   food was delish! we got the sampler with mac &...     1   \n",
       "651741   been going here since the 90s. the building ha...     1   \n",
       "682319   i like chili's, i know what i am getting so i ...     1   \n",
       "1130462  had a really great early dinner here. the crep...     1   \n",
       "\n",
       "                                                 Tokenized  \\\n",
       "377107   [my, family, and, i, had, a, great, time, ...,...   \n",
       "696302   [well, ,, i, like, this, cranky, ole, building...   \n",
       "795485   [my, friend, that, lives, there, took, me, the...   \n",
       "1295857  [they, make, pierogi, 's, from, scratch, ,, an...   \n",
       "1083598  [best, trendy, hot, spot, in, glendale, ., i, ...   \n",
       "...                                                    ...   \n",
       "742358   [the, cabo, wabo, cantina, is, another, one, o...   \n",
       "296348   [food, was, delish, !, we, got, the, sampler, ...   \n",
       "651741   [been, going, here, since, the, 90s, ., the, b...   \n",
       "682319   [i, like, chili, 's, ,, i, know, what, i, am, ...   \n",
       "1130462  [had, a, really, great, early, dinner, here, ....   \n",
       "\n",
       "                                                Tokenized2  \n",
       "377107   [family, great, time, ..., service, fast, frie...  \n",
       "696302   [well, like, cranky, ole, building, menu, cour...  \n",
       "795485   [friend, lives, took, wednesday, labor, day, w...  \n",
       "1295857  [make, pierogi, 's, scratch, many, varieties, ...  \n",
       "1083598  [best, trendy, hot, spot, glendale, ca, n't, b...  \n",
       "...                                                    ...  \n",
       "742358   [cabo, wabo, cantina, another, one, tourist, t...  \n",
       "296348   [food, delish, got, sampler, mac, cheese, croq...  \n",
       "651741   [going, since, 90s, building, changed, food, p...  \n",
       "682319   [like, chili, 's, know, getting, like, food, s...  \n",
       "1130462  [really, great, early, dinner, crepes, marseil...  \n",
       "\n",
       "[50000 rows x 4 columns]"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "#stemming/ lemmatization\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "#using snowball to preserve some words ex: 'better' (so it does not become 'good')\n",
    "stemmer = SnowballStemmer(\"english\")\n",
    "true['Stemmed'] = true['Tokenized2'].apply(lambda x: [stemmer.stem(y) for y in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#n-gram, might do in the future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #detokenize before vectorize\n",
    "# from nltk.tokenize.treebank import TreebankWordDetokenizer \n",
    "# detokenizer = TreebankWordDetokenizer()\n",
    "# true['Detokenized'] = true['Stemmed'].apply(lambda x: [detokenizer.detokenize(y) for y in x ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "true['Detokenized'] = true['Stemmed'].apply(lambda x: ' '.join([y for y in x ]))\n",
    "#Detokenized "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# true['Count'] = true['Detokenized'].apply(lambda x: (true['Detokenized'].value_counts()).loc[x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
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       "      <td>[best, trendi, hot, spot, glendal, ca, n't, be...</td>\n",
       "      <td>best trendi hot spot glendal ca n't believ ve ...</td>\n",
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      "text/plain": [
       "                                                    Review  True  \\\n",
       "377107   my family and i had a great time... service wa...     1   \n",
       "696302   well, i like this cranky ole building and the ...     1   \n",
       "795485   my friend that lives there took me there on we...     1   \n",
       "1295857  they make pierogi's from scratch, and so many ...     1   \n",
       "1083598  best trendy hot spot in glendale. i can't beli...     1   \n",
       "\n",
       "                                                 Tokenized  \\\n",
       "377107   [my, family, and, i, had, a, great, time, ...,...   \n",
       "696302   [well, ,, i, like, this, cranky, ole, building...   \n",
       "795485   [my, friend, that, lives, there, took, me, the...   \n",
       "1295857  [they, make, pierogi, 's, from, scratch, ,, an...   \n",
       "1083598  [best, trendy, hot, spot, in, glendale, ., i, ...   \n",
       "\n",
       "                                                Tokenized2  \\\n",
       "377107   [family, great, time, ..., service, fast, frie...   \n",
       "696302   [well, like, cranky, ole, building, menu, cour...   \n",
       "795485   [friend, lives, took, wednesday, labor, day, w...   \n",
       "1295857  [make, pierogi, 's, scratch, many, varieties, ...   \n",
       "1083598  [best, trendy, hot, spot, glendale, ca, n't, b...   \n",
       "\n",
       "                                                   Stemmed  \\\n",
       "377107   [famili, great, time, ..., servic, fast, frien...   \n",
       "696302   [well, like, cranki, ole, build, menu, cours, ...   \n",
       "795485   [friend, live, took, wednesday, labor, day, we...   \n",
       "1295857  [make, pierogi, 's, scratch, mani, varieti, fi...   \n",
       "1083598  [best, trendi, hot, spot, glendal, ca, n't, be...   \n",
       "\n",
       "                                               Detokenized  \n",
       "377107   famili great time ... servic fast friend food ...  \n",
       "696302   well like cranki ole build menu cours 's direc...  \n",
       "795485   friend live took wednesday labor day weekend l...  \n",
       "1295857  make pierogi 's scratch mani varieti find tast...  \n",
       "1083598  best trendi hot spot glendal ca n't believ ve ...  "
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Start with one review:\n",
    "text = true['Detokenized'][377107]\n",
    "\n",
    "# Create and generate a word cloud image:\n",
    "wordcloud = WordCloud().generate(text)\n",
    "\n",
    "# Display the generated image:\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 18278890 words in the combination of all review.\n"
     ]
    }
   ],
   "source": [
    "text = \" \".join(review for review in true.Detokenized)\n",
    "print (\"There are {} words in the combination of all review.\".format(len(text)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create and generate a word cloud image:\n",
    "wordcloud = WordCloud().generate(text)\n",
    "\n",
    "# Display the generated image:\n",
    "plt.imshow(wordcloud, interpolation='bilinear')\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "tfidf = TfidfVectorizer(max_features= 300)\n",
    "doc_tfidf_matrix = tfidf.fit_transform(true['Detokenized']).todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector = pd.DataFrame(doc_tfidf_matrix, columns = tfidf.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>10</th>\n",
       "      <th>20</th>\n",
       "      <th>actual</th>\n",
       "      <th>almost</th>\n",
       "      <th>also</th>\n",
       "      <th>alway</th>\n",
       "      <th>amaz</th>\n",
       "      <th>anoth</th>\n",
       "      <th>anyth</th>\n",
       "      <th>appet</th>\n",
       "      <th>...</th>\n",
       "      <th>wife</th>\n",
       "      <th>wine</th>\n",
       "      <th>wing</th>\n",
       "      <th>without</th>\n",
       "      <th>wo</th>\n",
       "      <th>wonder</th>\n",
       "      <th>work</th>\n",
       "      <th>worth</th>\n",
       "      <th>would</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.158052</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</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>49995</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.116582</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.054328</td>\n",
       "      <td>0.060086</td>\n",
       "      <td>0.0</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>0.053525</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.037058</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49996</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.247307</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.206587</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49997</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.172575</td>\n",
       "      <td>0.200177</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49998</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.317752</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49999</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.309514</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</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>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50000 rows × 300 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        10   20    actual  almost      also     alway      amaz     anoth  \\\n",
       "0      0.0  0.0  0.000000     0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "1      0.0  0.0  0.000000     0.0  0.114399  0.000000  0.000000  0.000000   \n",
       "2      0.0  0.0  0.000000     0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "3      0.0  0.0  0.000000     0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "4      0.0  0.0  0.000000     0.0  0.000000  0.000000  0.000000  0.000000   \n",
       "...    ...  ...       ...     ...       ...       ...       ...       ...   \n",
       "49995  0.0  0.0  0.116582     0.0  0.000000  0.000000  0.000000  0.054328   \n",
       "49996  0.0  0.0  0.247307     0.0  0.000000  0.000000  0.206587  0.000000   \n",
       "49997  0.0  0.0  0.000000     0.0  0.172575  0.200177  0.000000  0.000000   \n",
       "49998  0.0  0.0  0.000000     0.0  0.000000  0.317752  0.000000  0.000000   \n",
       "49999  0.0  0.0  0.000000     0.0  0.309514  0.000000  0.000000  0.000000   \n",
       "\n",
       "          anyth  appet  ...  wife  wine  wing  without   wo  wonder      work  \\\n",
       "0      0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "1      0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "2      0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "3      0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "4      0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "...         ...    ...  ...   ...   ...   ...      ...  ...     ...       ...   \n",
       "49995  0.060086    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.053525   \n",
       "49996  0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "49997  0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "49998  0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "49999  0.000000    0.0  ...   0.0   0.0   0.0      0.0  0.0     0.0  0.000000   \n",
       "\n",
       "       worth     would      year  \n",
       "0        0.0  0.000000  0.000000  \n",
       "1        0.0  0.000000  0.165103  \n",
       "2        0.0  0.000000  0.000000  \n",
       "3        0.0  0.158052  0.000000  \n",
       "4        0.0  0.000000  0.000000  \n",
       "...      ...       ...       ...  \n",
       "49995    0.0  0.037058  0.000000  \n",
       "49996    0.0  0.000000  0.000000  \n",
       "49997    0.0  0.000000  0.000000  \n",
       "49998    0.0  0.000000  0.000000  \n",
       "49999    0.0  0.000000  0.000000  \n",
       "\n",
       "[50000 rows x 300 columns]"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['10', '20', 'actual', 'almost', 'also', 'alway', 'amaz', 'anoth',\n",
       "       'anyth', 'appet',\n",
       "       ...\n",
       "       'wife', 'wine', 'wing', 'without', 'wo', 'wonder', 'work', 'worth',\n",
       "       'would', 'year'],\n",
       "      dtype='object', length=300)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import matplotlib.pyplot as plt\n",
    "\n",
    "# fig,ax = plt.subplots(figsize = (10,7))\n",
    "# plt.scatter(vector.iloc[0,:], vector.columns)\n",
    "# plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import svm\n",
    "\n",
    "X_train, X_test = train_test_split(vector, test_size=.2) \n",
    "estimator= svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\n",
    "estimator.fit(X_train)\n",
    "y_pred_test = estimator.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test = np.ones(shape=(10000,))\n",
    "y_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 977.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,    0.,\n",
       "        9023.]),\n",
       " array([-1. , -0.8, -0.6, -0.4, -0.2,  0. ,  0.2,  0.4,  0.6,  0.8,  1. ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.hist(y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 977, 9023])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(y_pred_test, return_counts=True)\n",
    "\n",
    "dict(zip(unique, counts))\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.bar(['not correct','correct'], counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score, precision_score, recall_score, f1_score, roc_auc_score, log_loss, confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/suchaya/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n        -1.0       0.00      0.00      0.00         0\\n         1.0       1.00      0.90      0.95     10000\\n\\n    accuracy                           0.90     10000\\n   macro avg       0.50      0.45      0.47     10000\\nweighted avg       1.00      0.90      0.95     10000\\n'"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9023"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    0],\n",
       "       [ 977, 9023]])"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test, y_pred_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**try testing using only fake reviews with the model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "# try testing only fake reviews with the model\n",
    "false = data[data['True'] == 0].sample(50000)\n",
    "false['Review'] = false['Review'].apply(remove_accents)\n",
    "false['Review'] = false['Review'].str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [],
   "source": [
    "false['Tokenized'] = false['Review'].apply(word_tokenize)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "false['Tokenized2'] = false['Tokenized'].apply(lambda x: [item for item in x if item not in stopwords_  and not item in punctuations_])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer(\"english\")\n",
    "false['Stemmed'] = false['Tokenized2'].apply(lambda x: [stemmer.stem(y) for y in x])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "false['Detokenized'] = false['Stemmed'].apply(lambda x: ' '.join([y for y in x ]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "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>Review</th>\n",
       "      <th>True</th>\n",
       "      <th>Tokenized</th>\n",
       "      <th>Tokenized2</th>\n",
       "      <th>Stemmed</th>\n",
       "      <th>Detokenized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>524720</th>\n",
       "      <td>casanova's is much more than an awesome pizza ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[casanova, 's, is, much, more, than, an, aweso...</td>\n",
       "      <td>[casanova, 's, much, awesome, pizza, joint, 's...</td>\n",
       "      <td>[casanova, 's, much, awesom, pizza, joint, 's,...</td>\n",
       "      <td>casanova 's much awesom pizza joint 's awesom ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>477348</th>\n",
       "      <td>we randomly tried this restaurant while walkin...</td>\n",
       "      <td>0</td>\n",
       "      <td>[we, randomly, tried, this, restaurant, while,...</td>\n",
       "      <td>[randomly, tried, restaurant, walking, around,...</td>\n",
       "      <td>[random, tri, restaur, walk, around, venetian,...</td>\n",
       "      <td>random tri restaur walk around venetian wonder...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032307</th>\n",
       "      <td>pizza is very good and a great price, i wish t...</td>\n",
       "      <td>0</td>\n",
       "      <td>[pizza, is, very, good, and, a, great, price, ...</td>\n",
       "      <td>[pizza, good, great, price, wish, bigger, pizz...</td>\n",
       "      <td>[pizza, good, great, price, wish, bigger, pizz...</td>\n",
       "      <td>pizza good great price wish bigger pizza diffe...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492848</th>\n",
       "      <td>always good food. great for my wife who likes ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[always, good, food, ., great, for, my, wife, ...</td>\n",
       "      <td>[always, good, food, great, wife, likes, fried...</td>\n",
       "      <td>[alway, good, food, great, wife, like, fri, fo...</td>\n",
       "      <td>alway good food great wife like fri food ca n'...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23408</th>\n",
       "      <td>great atmosphere, good food and good service. ...</td>\n",
       "      <td>0</td>\n",
       "      <td>[great, atmosphere, ,, good, food, and, good, ...</td>\n",
       "      <td>[great, atmosphere, good, food, good, service,...</td>\n",
       "      <td>[great, atmospher, good, food, good, servic, b...</td>\n",
       "      <td>great atmospher good food good servic belmont ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Review  True  \\\n",
       "524720   casanova's is much more than an awesome pizza ...     0   \n",
       "477348   we randomly tried this restaurant while walkin...     0   \n",
       "1032307  pizza is very good and a great price, i wish t...     0   \n",
       "492848   always good food. great for my wife who likes ...     0   \n",
       "23408    great atmosphere, good food and good service. ...     0   \n",
       "\n",
       "                                                 Tokenized  \\\n",
       "524720   [casanova, 's, is, much, more, than, an, aweso...   \n",
       "477348   [we, randomly, tried, this, restaurant, while,...   \n",
       "1032307  [pizza, is, very, good, and, a, great, price, ...   \n",
       "492848   [always, good, food, ., great, for, my, wife, ...   \n",
       "23408    [great, atmosphere, ,, good, food, and, good, ...   \n",
       "\n",
       "                                                Tokenized2  \\\n",
       "524720   [casanova, 's, much, awesome, pizza, joint, 's...   \n",
       "477348   [randomly, tried, restaurant, walking, around,...   \n",
       "1032307  [pizza, good, great, price, wish, bigger, pizz...   \n",
       "492848   [always, good, food, great, wife, likes, fried...   \n",
       "23408    [great, atmosphere, good, food, good, service,...   \n",
       "\n",
       "                                                   Stemmed  \\\n",
       "524720   [casanova, 's, much, awesom, pizza, joint, 's,...   \n",
       "477348   [random, tri, restaur, walk, around, venetian,...   \n",
       "1032307  [pizza, good, great, price, wish, bigger, pizz...   \n",
       "492848   [alway, good, food, great, wife, like, fri, fo...   \n",
       "23408    [great, atmospher, good, food, good, servic, b...   \n",
       "\n",
       "                                               Detokenized  \n",
       "524720   casanova 's much awesom pizza joint 's awesom ...  \n",
       "477348   random tri restaur walk around venetian wonder...  \n",
       "1032307  pizza good great price wish bigger pizza diffe...  \n",
       "492848   alway good food great wife like fri food ca n'...  \n",
       "23408    great atmospher good food good servic belmont ...  "
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "false.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf2 = TfidfVectorizer(max_features= 300)\n",
    "doc_tfidf_matrix2 = tfidf2.fit_transform(false['Detokenized']).todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector2 = pd.DataFrame(doc_tfidf_matrix2, columns = tfidf2.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_2, X_test_2 = train_test_split(vector2, test_size=.2) \n",
    "# estimator= svm.OneClassSVM(nu=0.1, kernel=\"rbf\", gamma=0.1)\n",
    "# estimator.fit(X_train)\n",
    "y_pred_test_2 = estimator.predict(X_test_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_2 = np.negative(np.ones(shape=(10000,)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -1., -1., ..., -1., -1., -1.])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  1,  1, ...,  1, -1,  1])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred_test_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{-1: 4257, 1: 5743}"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unique, counts = np.unique(y_pred_test_2, return_counts=True)\n",
    "\n",
    "dict(zip(unique, counts))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 2 artists>"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+zkiSutiwYUOG95f71twW4NjQ/lSrSZLWqOUOosxRcxYkSWvYumX+eVPAtqH9rcDx2Z1XT9ckSavfcs+IjgATSS5Jcj5wM7B/mfsgSXoNWdYgqqpXgN8DHgWOAvuq6qnl7IN+Isk7kvxzkpeT/FHv/kjLIcmeJCeTfKd3XzRjWVfN6bUlyYXALwM3Aqeq6i87d0kauyS/CvwAeKCqfqV3f+STFda0qjpZVUeA/+ndF2m5VNVXgene/dBPGESSpK4MIklSVwbRGpNkV5In2uuXevdHkpb7/4jUWVV9gpnn/UnSa4Kr5tawJL8IfAN4E/BjZlYSXVpV3+/aMWmMknwOuAp4C/ACsLuq7u/aqTXOIJIkdeV3RJKkrgwiSVJXBpEkqSuDSJLUlUEkSerKIJIkdWUQSZK6MogkSV39L65e1K2FjBTaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "plt.bar(['-1','1'], counts)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4257"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test_2, y_pred_test_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4257, 5743],\n",
       "       [   0,    0]])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test_2, y_pred_test_2) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/suchaya/anaconda3/lib/python3.7/site-packages/sklearn/metrics/_classification.py:1268: UndefinedMetricWarning: Recall and F-score are ill-defined and being set to 0.0 in labels with no true samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'              precision    recall  f1-score   support\\n\\n        -1.0       1.00      0.43      0.60     10000\\n         1.0       0.00      0.00      0.00         0\\n\\n    accuracy                           0.43     10000\\n   macro avg       0.50      0.21      0.30     10000\\nweighted avg       1.00      0.43      0.60     10000\\n'"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classification_report(y_test_2, y_pred_test_2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**try testing half fake, half true reviews**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [],
   "source": [
    "mix = pd.concat([true.Detokenized.sample(25000), false.Detokenized.sample(25000)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'to_frame'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-186-45773f87219d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mmix\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmix\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_frame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5177\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5178\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5179\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5180\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5181\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'to_frame'"
     ]
    }
   ],
   "source": [
    "mix = mix.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "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>Detokenized</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>880175</th>\n",
       "      <td>greg f said still worst qdoba charlott portion...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374581</th>\n",
       "      <td>took wife mother mother 's day cortez room gol...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1906371</th>\n",
       "      <td>bewar food disgust ... ..nasti red cheap egg r...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>870862</th>\n",
       "      <td>waffl die belgium say almost delici bit pricey...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>312894</th>\n",
       "      <td>great place ... sold new owner epic fail</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               Detokenized\n",
       "880175   greg f said still worst qdoba charlott portion...\n",
       "1374581  took wife mother mother 's day cortez room gol...\n",
       "1906371  bewar food disgust ... ..nasti red cheap egg r...\n",
       "870862   waffl die belgium say almost delici bit pricey...\n",
       "312894            great place ... sold new owner epic fail"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mix.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 1, 1, 1, 1, -1, -1, -1, -1, -1]"
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "([1]*5)+([-1]*5)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [],
   "source": [
    "mix['y'] = ([1]*25000)+([-1]*25000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [
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       "                                               Detokenized  y\n",
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     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf3 = TfidfVectorizer(max_features =300)\n",
    "doc_tfidf_matrix3 = tfidf3.fit_transform(mix['Detokenized']).todense()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector3 = pd.DataFrame(doc_tfidf_matrix3, columns = tfidf3.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "vector3['label'] = ([1]*25000)+([-1]*25000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
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      ],
      "text/plain": [
       "        10   20  absolut    actual    almost      also  alway  amaz     anoth  \\\n",
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       "49999  0.0  0.0      0.0  0.000000  0.000000  0.000000    0.0   0.0  0.000000   \n",
       "\n",
       "       anyth  ...  wine      wing  without   wo  wonder  work     worth  \\\n",
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       "\n",
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       "\n",
       "[50000 rows x 301 columns]"
      ]
     },
     "execution_count": 237,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vector3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vector3.iloc[:,:-1]\n",
    "y = vector3.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_3, X_test_3, y_train_3, y_test_3 = train_test_split(X,y, test_size=.2) \n",
    "y_pred_test_3 = estimator.predict(X_test_3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5564"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(y_test_3, y_pred_test_3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1673, 3316],\n",
       "       [1120, 3891]])"
      ]
     },
     "execution_count": 241,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "confusion_matrix(y_test_3, y_pred_test_3) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sephora - use to test the model on my real data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "sephora = pd.read_csv('Sephora_review.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 11 columns):\n",
      "product_id           1000 non-null object\n",
      "review_title         901 non-null object\n",
      "review_text          1000 non-null object\n",
      "rating               1000 non-null int64\n",
      "age_range            0 non-null float64\n",
      "skin_type            417 non-null object\n",
      "skin_tone            112 non-null object\n",
      "eye_color            0 non-null float64\n",
      "reviewer_username    1000 non-null object\n",
      "tags                 447 non-null object\n",
      "review_id            1000 non-null object\n",
      "dtypes: float64(2), int64(1), object(8)\n",
      "memory usage: 86.1+ KB\n"
     ]
    }
   ],
   "source": [
    "sephora.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>product_id</th>\n",
       "      <th>review_title</th>\n",
       "      <th>review_text</th>\n",
       "      <th>rating</th>\n",
       "      <th>age_range</th>\n",
       "      <th>skin_type</th>\n",
       "      <th>skin_tone</th>\n",
       "      <th>eye_color</th>\n",
       "      <th>reviewer_username</th>\n",
       "      <th>tags</th>\n",
       "      <th>review_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>P38217</td>\n",
       "      <td>Worth the money</td>\n",
       "      <td>Sometimes I stray from this cleanser, but I al...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>normal</td>\n",
       "      <td>light</td>\n",
       "      <td>NaN</td>\n",
       "      <td>katechatte</td>\n",
       "      <td>{foamy,exfoliating}</td>\n",
       "      <td>6611717f-2636-4756-bf36-66c81cc267a7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>P38217</td>\n",
       "      <td>Great</td>\n",
       "      <td>I am a 41 year old African American woman with...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>combination</td>\n",
       "      <td>deep</td>\n",
       "      <td>NaN</td>\n",
       "      <td>snook41</td>\n",
       "      <td>{foamy}</td>\n",
       "      <td>e7d3307e-02ff-45a1-8fc3-6bd628bedd86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>P38217</td>\n",
       "      <td>Great Product</td>\n",
       "      <td>I'm really enjoying this product. Received a s...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>combination</td>\n",
       "      <td>olive</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wahinewarrior</td>\n",
       "      <td>{foamy,milky,exfoliating}</td>\n",
       "      <td>4188d728-fde6-4d06-984e-164cca2b8781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>P38217</td>\n",
       "      <td>Nice, but not great for combination skin</td>\n",
       "      <td>I tried this cleanser at a friends house, and ...</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>combination</td>\n",
       "      <td>fair</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jenlines22</td>\n",
       "      <td>{hydrating,creamy}</td>\n",
       "      <td>248c904c-6e30-4929-8228-87b03ad7a921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>P38217</td>\n",
       "      <td>great moisturizer</td>\n",
       "      <td>leaves the skin feeling fresh and revived... j...</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dry</td>\n",
       "      <td>light</td>\n",
       "      <td>NaN</td>\n",
       "      <td>jessea</td>\n",
       "      <td>{exfoliating}</td>\n",
       "      <td>654bdb99-9371-4440-a540-0dd2a73da339</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  product_id                              review_title  \\\n",
       "0     P38217                           Worth the money   \n",
       "1     P38217                                     Great   \n",
       "2     P38217                             Great Product   \n",
       "3     P38217  Nice, but not great for combination skin   \n",
       "4     P38217                         great moisturizer   \n",
       "\n",
       "                                         review_text  rating  age_range  \\\n",
       "0  Sometimes I stray from this cleanser, but I al...       5        NaN   \n",
       "1  I am a 41 year old African American woman with...       5        NaN   \n",
       "2  I'm really enjoying this product. Received a s...       5        NaN   \n",
       "3  I tried this cleanser at a friends house, and ...       3        NaN   \n",
       "4  leaves the skin feeling fresh and revived... j...       5        NaN   \n",
       "\n",
       "     skin_type skin_tone  eye_color reviewer_username  \\\n",
       "0       normal     light        NaN        katechatte   \n",
       "1  combination      deep        NaN           snook41   \n",
       "2  combination     olive        NaN     wahinewarrior   \n",
       "3  combination      fair        NaN        jenlines22   \n",
       "4          dry     light        NaN            jessea   \n",
       "\n",
       "                        tags                             review_id  \n",
       "0        {foamy,exfoliating}  6611717f-2636-4756-bf36-66c81cc267a7  \n",
       "1                    {foamy}  e7d3307e-02ff-45a1-8fc3-6bd628bedd86  \n",
       "2  {foamy,milky,exfoliating}  4188d728-fde6-4d06-984e-164cca2b8781  \n",
       "3         {hydrating,creamy}  248c904c-6e30-4929-8228-87b03ad7a921  \n",
       "4              {exfoliating}  654bdb99-9371-4440-a540-0dd2a73da339  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sephora.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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