final_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "23QBjT0fzJDh"
   },
   "source": [
    "# **DATA LOADING**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "id": "o_k1mhYwm4GD"
   },
   "outputs": [],
   "source": [
    "import json\n",
    "import csv\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "CrmC_39umBMc",
    "outputId": "b94dddb1-91e9-4514-caa9-9dc8b133d75e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'rating': 5.0,\n",
       "  'title': 'Nice',\n",
       "  'text': 'If i had a dollar for how many times I have played this cd and how many times I have asked Alexa to play it, I would be rich. Love this singer along with the Black Pumas. Finding a lot of new music that I like a lot on amazon. Try new things.',\n",
       "  'images': [],\n",
       "  'asin': 'B004RQ2IRG',\n",
       "  'parent_asin': 'B004RQ2IRG',\n",
       "  'user_id': 'AFUOYIZBU3MTBOLYKOJE5Z35MBDA',\n",
       "  'timestamp': 1618972613292,\n",
       "  'helpful_vote': 0,\n",
       "  'verified_purchase': True},\n",
       " {'rating': 5.0,\n",
       "  'title': 'Excellent',\n",
       "  'text': 'awesome sound - cant wait to see them in person - always miss them when they are in town !',\n",
       "  'images': [],\n",
       "  'asin': 'B0026UZEI0',\n",
       "  'parent_asin': 'B0026UZEI0',\n",
       "  'user_id': 'AHGAOIZVODNHYMNCBV4DECZH42UQ',\n",
       "  'timestamp': 1308167525000,\n",
       "  'helpful_vote': 0,\n",
       "  'verified_purchase': True},\n",
       " {'rating': 5.0,\n",
       "  'title': 'Great service',\n",
       "  'text': 'This is a great cd. Good music and plays well. Seller responded back very quicky and  received it within 3 days',\n",
       "  'images': [],\n",
       "  'asin': 'B0055JSYHC',\n",
       "  'parent_asin': 'B0055JSYHC',\n",
       "  'user_id': 'AFGEM6BXCYHUILEOA3P2ZYBEF2TA',\n",
       "  'timestamp': 1615838793006,\n",
       "  'helpful_vote': 0,\n",
       "  'verified_purchase': True},\n",
       " {'rating': 1.0,\n",
       "  'title': 'No good',\n",
       "  'text': 'These are not real German singers, they have accents. It is nothing what they advertised it. Music stinks.',\n",
       "  'images': [],\n",
       "  'asin': 'B000F9SMUQ',\n",
       "  'parent_asin': 'B000F9SMUQ',\n",
       "  'user_id': 'AH3OG6QD6EDJGZRVCFKV4B66VWNQ',\n",
       "  'timestamp': 1405219741000,\n",
       "  'helpful_vote': 0,\n",
       "  'verified_purchase': True},\n",
       " {'rating': 3.0,\n",
       "  'title': 'Cool concept, so-so execution...',\n",
       "  'text': 'I first heard this playing in a Nagoya shop and fell in love with the remix of Ke$ha\\'s \"Tik Tok\" (Ke$ha and Lady Gaga are EVERYWHERE in Japan), which then morphed into several other recent pop hits. When the salesgirl handed me the CD, I was pleased to see that it actually included thirty pop songs by the likes of Lady Gaga, Usher, La Roux, Katy Perry, Black-Eyed Peas, Ke$ha, etc. I researched more after I got home, and discovered that this Japanese label (Manhattan Records) exists solely to put out rerecorded and remixed dance CDs based on recent pop hits, the key word being rerecorded. I guessed as much as I was listening to the CD in-store; several of the soundalikes do a great job, others are just so-so. It\\'s great for workouts, though, since the tracks fade one into another with no downtime (the bad side is that if you\\'ve got in in shuffle, the beginning of the next song, which is often in a different key, is kind of an awkward fade-out when not played in sequence).',\n",
       "  'images': [],\n",
       "  'asin': 'B0049D1WVK',\n",
       "  'parent_asin': 'B0049D1WVK',\n",
       "  'user_id': 'AFW2PDT3AMT4X3PYQG7FJZH5FXFA',\n",
       "  'timestamp': 1309029595000,\n",
       "  'helpful_vote': 0,\n",
       "  'verified_purchase': False}]"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load in data from a jsonl file, print first 5 elements\n",
    "\n",
    "file_path = 'Digital_Music.jsonl'\n",
    "with open(file_path, 'r') as file:\n",
    "    data = [json.loads(line) for line in file]\n",
    "\n",
    "data[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "GSUFygZFmIKn",
    "outputId": "3350657e-b3b0-4560-880f-93740cc628b2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data has been successfully converted to CSV.\n"
     ]
    }
   ],
   "source": [
    "# convert file to csv\n",
    "csv_file_path = 'Digital_Music.csv'\n",
    "\n",
    "fieldnames = data[0].keys()\n",
    "with open(csv_file_path, 'w', newline='') as csvfile:\n",
    "    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "    writer.writeheader()\n",
    "    writer.writerows(data)\n",
    "\n",
    "print(\"Data has been successfully converted to CSV.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 293
    },
    "id": "GjkhKr1gm0Y5",
    "outputId": "39ea0741-03e9-48e0-df8e-6672dbb44a92"
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
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       "    .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>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>images</th>\n",
       "      <th>asin</th>\n",
       "      <th>parent_asin</th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>helpful_vote</th>\n",
       "      <th>verified_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Nice</td>\n",
       "      <td>If i had a dollar for how many times I have pl...</td>\n",
       "      <td>[]</td>\n",
       "      <td>B004RQ2IRG</td>\n",
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       "      <th>1</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Excellent</td>\n",
       "      <td>awesome sound - cant wait to see them in perso...</td>\n",
       "      <td>[]</td>\n",
       "      <td>B0026UZEI0</td>\n",
       "      <td>B0026UZEI0</td>\n",
       "      <td>AHGAOIZVODNHYMNCBV4DECZH42UQ</td>\n",
       "      <td>1308167525000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Great service</td>\n",
       "      <td>This is a great cd. Good music and plays well....</td>\n",
       "      <td>[]</td>\n",
       "      <td>B0055JSYHC</td>\n",
       "      <td>B0055JSYHC</td>\n",
       "      <td>AFGEM6BXCYHUILEOA3P2ZYBEF2TA</td>\n",
       "      <td>1615838793006</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "      <td>No good</td>\n",
       "      <td>These are not real German singers, they have a...</td>\n",
       "      <td>[]</td>\n",
       "      <td>B000F9SMUQ</td>\n",
       "      <td>B000F9SMUQ</td>\n",
       "      <td>AH3OG6QD6EDJGZRVCFKV4B66VWNQ</td>\n",
       "      <td>1405219741000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Cool concept, so-so execution...</td>\n",
       "      <td>I first heard this playing in a Nagoya shop an...</td>\n",
       "      <td>[]</td>\n",
       "      <td>B0049D1WVK</td>\n",
       "      <td>B0049D1WVK</td>\n",
       "      <td>AFW2PDT3AMT4X3PYQG7FJZH5FXFA</td>\n",
       "      <td>1309029595000</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   rating                             title  \\\n",
       "0     5.0                              Nice   \n",
       "1     5.0                         Excellent   \n",
       "2     5.0                     Great service   \n",
       "3     1.0                           No good   \n",
       "4     3.0  Cool concept, so-so execution...   \n",
       "\n",
       "                                                text images        asin  \\\n",
       "0  If i had a dollar for how many times I have pl...     []  B004RQ2IRG   \n",
       "1  awesome sound - cant wait to see them in perso...     []  B0026UZEI0   \n",
       "2  This is a great cd. Good music and plays well....     []  B0055JSYHC   \n",
       "3  These are not real German singers, they have a...     []  B000F9SMUQ   \n",
       "4  I first heard this playing in a Nagoya shop an...     []  B0049D1WVK   \n",
       "\n",
       "  parent_asin                       user_id      timestamp  helpful_vote  \\\n",
       "0  B004RQ2IRG  AFUOYIZBU3MTBOLYKOJE5Z35MBDA  1618972613292             0   \n",
       "1  B0026UZEI0  AHGAOIZVODNHYMNCBV4DECZH42UQ  1308167525000             0   \n",
       "2  B0055JSYHC  AFGEM6BXCYHUILEOA3P2ZYBEF2TA  1615838793006             0   \n",
       "3  B000F9SMUQ  AH3OG6QD6EDJGZRVCFKV4B66VWNQ  1405219741000             0   \n",
       "4  B0049D1WVK  AFW2PDT3AMT4X3PYQG7FJZH5FXFA  1309029595000             0   \n",
       "\n",
       "   verified_purchase  \n",
       "0               True  \n",
       "1               True  \n",
       "2               True  \n",
       "3               True  \n",
       "4              False  "
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load the csv file\n",
    "df = pd.read_csv(\"Digital_Music.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "35GjOd4azRsn"
   },
   "source": [
    "# **DATA EXPLORATION**\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 394
    },
    "id": "-MeQ-SPWnwQN",
    "outputId": "a816747f-e816-4ebe-9b84-e65895b656da"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\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>rating</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>images</th>\n",
       "      <th>asin</th>\n",
       "      <th>parent_asin</th>\n",
       "      <th>user_id</th>\n",
       "      <th>timestamp</th>\n",
       "      <th>helpful_vote</th>\n",
       "      <th>verified_purchase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>130434.000000</td>\n",
       "      <td>130406</td>\n",
       "      <td>130399</td>\n",
       "      <td>130434</td>\n",
       "      <td>130434</td>\n",
       "      <td>130434</td>\n",
       "      <td>130434</td>\n",
       "      <td>1.304340e+05</td>\n",
       "      <td>130434.000000</td>\n",
       "      <td>130434</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>86009</td>\n",
       "      <td>118640</td>\n",
       "      <td>3269</td>\n",
       "      <td>70519</td>\n",
       "      <td>70511</td>\n",
       "      <td>100952</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>Great</td>\n",
       "      <td>[]</td>\n",
       "      <td>B00003CXKT</td>\n",
       "      <td>B00003CXKT</td>\n",
       "      <td>AGAFM74L2RIJ5O36NNYH4Z5ISQNQ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17294</td>\n",
       "      <td>400</td>\n",
       "      <td>127100</td>\n",
       "      <td>398</td>\n",
       "      <td>398</td>\n",
       "      <td>341</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>4.532837</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.469880e+12</td>\n",
       "      <td>1.045050</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.027994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.369730e+11</td>\n",
       "      <td>3.485248</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.745575e+11</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.400349e+12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.475705e+12</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.573689e+12</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.694040e+12</td>\n",
       "      <td>259.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               rating       title    text  images        asin parent_asin  \\\n",
       "count   130434.000000      130406  130399  130434      130434      130434   \n",
       "unique            NaN       86009  118640    3269       70519       70511   \n",
       "top               NaN  Five Stars   Great      []  B00003CXKT  B00003CXKT   \n",
       "freq              NaN       17294     400  127100         398         398   \n",
       "mean         4.532837         NaN     NaN     NaN         NaN         NaN   \n",
       "std          1.027994         NaN     NaN     NaN         NaN         NaN   \n",
       "min          1.000000         NaN     NaN     NaN         NaN         NaN   \n",
       "25%          5.000000         NaN     NaN     NaN         NaN         NaN   \n",
       "50%          5.000000         NaN     NaN     NaN         NaN         NaN   \n",
       "75%          5.000000         NaN     NaN     NaN         NaN         NaN   \n",
       "max          5.000000         NaN     NaN     NaN         NaN         NaN   \n",
       "\n",
       "                             user_id     timestamp   helpful_vote  \\\n",
       "count                         130434  1.304340e+05  130434.000000   \n",
       "unique                        100952           NaN            NaN   \n",
       "top     AGAFM74L2RIJ5O36NNYH4Z5ISQNQ           NaN            NaN   \n",
       "freq                             341           NaN            NaN   \n",
       "mean                             NaN  1.469880e+12       1.045050   \n",
       "std                              NaN  1.369730e+11       3.485248   \n",
       "min                              NaN  8.745575e+11       0.000000   \n",
       "25%                              NaN  1.400349e+12       0.000000   \n",
       "50%                              NaN  1.475705e+12       0.000000   \n",
       "75%                              NaN  1.573689e+12       1.000000   \n",
       "max                              NaN  1.694040e+12     259.000000   \n",
       "\n",
       "       verified_purchase  \n",
       "count             130434  \n",
       "unique                 2  \n",
       "top                 True  \n",
       "freq               96033  \n",
       "mean                 NaN  \n",
       "std                  NaN  \n",
       "min                  NaN  \n",
       "25%                  NaN  \n",
       "50%                  NaN  \n",
       "75%                  NaN  \n",
       "max                  NaN  "
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# number of observations and summary statistics\n",
    "num_observations = df.shape[0]\n",
    "df.describe(include='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398
    },
    "id": "tOwbWcQr3lRo",
    "outputId": "a46d126e-82d7-49e7-ff83-cfb6237dcab0"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating                0\n",
       "title                28\n",
       "text                 35\n",
       "images                0\n",
       "asin                  0\n",
       "parent_asin           0\n",
       "user_id               0\n",
       "timestamp             0\n",
       "helpful_vote          0\n",
       "verified_purchase     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check for missing data\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 398
    },
    "id": "hrsiadS-45kM",
    "outputId": "78bdf23c-b55e-497a-b6d0-81e5fee0142a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "rating               float64\n",
       "title                 object\n",
       "text                  object\n",
       "images                object\n",
       "asin                  object\n",
       "parent_asin           object\n",
       "user_id               object\n",
       "timestamp              int64\n",
       "helpful_vote           int64\n",
       "verified_purchase       bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "f6qvvaNB48_c",
    "outputId": "01536894-116c-4870-faf2-7a5344bf624c"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'rating' has 5 unique values.\n",
      "Column 'title' has 86009 unique values.\n",
      "Column 'text' has 118640 unique values.\n",
      "Column 'images' has 3269 unique values.\n",
      "Column 'asin' has 70519 unique values.\n",
      "Column 'parent_asin' has 70511 unique values.\n",
      "Column 'user_id' has 100952 unique values.\n",
      "Column 'timestamp' has 128745 unique values.\n",
      "Column 'helpful_vote' has 96 unique values.\n",
      "Column 'verified_purchase' has 2 unique values.\n"
     ]
    }
   ],
   "source": [
    "unique_values = df.nunique()\n",
    "\n",
    "for column, unique_count in unique_values.items():\n",
    "    print(f\"Column '{column}' has {unique_count} unique values.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "verified_purchase\n",
       "True     96033\n",
       "False    34401\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# count number of verified purchases vs. not verified purchases\n",
    "df['verified_purchase'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "id": "3wOK5xgJ9J4h",
    "outputId": "7732ba62-3dd8-4fc2-fdf0-d8ed361e1a7a"
   },
   "outputs": [
    {
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       "      <td>B00EJWT9UW</td>\n",
       "      <td>B00EJWT9UW</td>\n",
       "      <td>AG2TFOAXNIQB3AZ6TDTXNHE7AA5A</td>\n",
       "      <td>1563101693691</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83789</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great</td>\n",
       "      <td>[]</td>\n",
       "      <td>B09GRY9XW3</td>\n",
       "      <td>B09GRY9XW3</td>\n",
       "      <td>AHW73OTACSMAOOOTRUCAH7LWYC4Q</td>\n",
       "      <td>1681286521411</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88734</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B00PUUGZNQ</td>\n",
       "      <td>B00PUUGZNQ</td>\n",
       "      <td>AGEOITIPPYF3LUOAF3W4H2XJGATA</td>\n",
       "      <td>1639451738271</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89366</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Anything with the late great Tommy López is a ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'small_image_url': 'https://images-na.ssl-im...</td>\n",
       "      <td>B01EZE2W48</td>\n",
       "      <td>B01EZE2W48</td>\n",
       "      <td>AEAQYUIECUQF36ZLPMOFY32LU3GQ</td>\n",
       "      <td>1510015720335</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91292</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Great band great music. This family truly can ...</td>\n",
       "      <td>[]</td>\n",
       "      <td>B07L9XQL3R</td>\n",
       "      <td>B07L9XQL3R</td>\n",
       "      <td>AELWC576LKQTFBYYAW4H746KQQKQ</td>\n",
       "      <td>1603135881388</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91464</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Like it</td>\n",
       "      <td>[]</td>\n",
       "      <td>B00YM8K164</td>\n",
       "      <td>B00YM8K164</td>\n",
       "      <td>AEMEIGHR45R7RFJ2QLWXIT47HS6Q</td>\n",
       "      <td>1639007657088</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91940</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Good product</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B082QNJ9RT</td>\n",
       "      <td>B082QNJ9RT</td>\n",
       "      <td>AG2PS3F2FFYKKV4YQV7Z4TQSA7JQ</td>\n",
       "      <td>1675369325343</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92762</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wanted only one song #11.</td>\n",
       "      <td>[]</td>\n",
       "      <td>B00FY3VN9K</td>\n",
       "      <td>B00FY3VN9K</td>\n",
       "      <td>AGZ7QNTF5765SXB3IN6D6AI7ITKA</td>\n",
       "      <td>1590785812572</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93519</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B08TG5L2VW</td>\n",
       "      <td>B08TG5L2VW</td>\n",
       "      <td>AHUJPI7HG5NNWP62LEHHBIVFK2HA</td>\n",
       "      <td>1671492004859</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95052</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B0B57VBBHZ</td>\n",
       "      <td>B0B57VBBHZ</td>\n",
       "      <td>AGYPKIBSKZ44O67F5DW6ZWKQAEVQ</td>\n",
       "      <td>1684315690957</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100698</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Four Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'small_image_url': 'https://images-na.ssl-im...</td>\n",
       "      <td>B014I7B8GK</td>\n",
       "      <td>B014I7B8GK</td>\n",
       "      <td>AEOB3RX6P2E46WQCXAIOXXH5NQ3A</td>\n",
       "      <td>1523434428165</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101750</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B0012GNG70</td>\n",
       "      <td>B0012GNG70</td>\n",
       "      <td>AG5WX46AOZD7ZEDBQJLK2SNXSCOA</td>\n",
       "      <td>1458176043000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103395</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Music I like.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B08479PC93</td>\n",
       "      <td>B08479PC93</td>\n",
       "      <td>AF77XZ67BUR4MPEXHCOOPIHNL4ZQ</td>\n",
       "      <td>1661885523185</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>106782</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Good</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B00Y301X9A</td>\n",
       "      <td>B00Y301X9A</td>\n",
       "      <td>AGLVIZVQAJD6ICBKFRRL77HRM6SA</td>\n",
       "      <td>1581106715635</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107256</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B092H9LB22</td>\n",
       "      <td>B092H9LB22</td>\n",
       "      <td>AEIKU62NBNI4O2X2ASROVJDIPXPA</td>\n",
       "      <td>1683516429615</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>117245</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>This not a review, Only way can update the sel...</td>\n",
       "      <td>[]</td>\n",
       "      <td>B000BDHB30</td>\n",
       "      <td>B000BDHB30</td>\n",
       "      <td>AFRBE6S2HYU2GUKRJIX4QTKVK3SA</td>\n",
       "      <td>1539086314717</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119246</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'small_image_url': 'https://images-na.ssl-im...</td>\n",
       "      <td>B01G557XSA</td>\n",
       "      <td>B01G557XSA</td>\n",
       "      <td>AEKIRATB6BFHSDA36VP3AEDLYA2A</td>\n",
       "      <td>1492711768000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119533</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Four Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'small_image_url': 'https://images-na.ssl-im...</td>\n",
       "      <td>B07719ZGRY</td>\n",
       "      <td>B07719ZGRY</td>\n",
       "      <td>AHVBT26GIJVZQY6OTWC3LGPQVVNQ</td>\n",
       "      <td>1510869188243</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>119736</th>\n",
       "      <td>5.0</td>\n",
       "      <td>The CD arrived in time for the usesge Inplanne...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B01GUGYLT8</td>\n",
       "      <td>B01GUGYLT8</td>\n",
       "      <td>AFYYJTDAGXST7DN3ORNSAD2PPGKA</td>\n",
       "      <td>1629073389511</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122287</th>\n",
       "      <td>4.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Interesting music.</td>\n",
       "      <td>[]</td>\n",
       "      <td>B00KAG6SVO</td>\n",
       "      <td>B00KAG6SVO</td>\n",
       "      <td>AG5ECJ5HE5SAYRC5RCSLJIBSFBOQ</td>\n",
       "      <td>1419871147000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122288</th>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Not his best.</td>\n",
       "      <td>[]</td>\n",
       "      <td>B00JDSWVBG</td>\n",
       "      <td>B00JDSWVBG</td>\n",
       "      <td>AG5ECJ5HE5SAYRC5RCSLJIBSFBOQ</td>\n",
       "      <td>1419871062000</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122689</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>It's a CD - what is there to report?</td>\n",
       "      <td>[]</td>\n",
       "      <td>B082TKPTZG</td>\n",
       "      <td>B082TKPTZG</td>\n",
       "      <td>AEHW64Y4VHXNNFNIBDWFSPVZZTAQ</td>\n",
       "      <td>1663120148087</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124544</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B019GLMNVM</td>\n",
       "      <td>B019GLMNVM</td>\n",
       "      <td>AHYFXMCRLG7AKHVXBXQGOCADHEAQ</td>\n",
       "      <td>1609186115103</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124818</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Nice compilation of Coltrane's compositions</td>\n",
       "      <td>[]</td>\n",
       "      <td>B013Q6TAAU</td>\n",
       "      <td>B013Q6TAAU</td>\n",
       "      <td>AFUYYEFFJNTSZW54DCCFC53T6ZEQ</td>\n",
       "      <td>1556575185170</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124980</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Five Stars</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'small_image_url': 'https://images-na.ssl-im...</td>\n",
       "      <td>B006T41X7U</td>\n",
       "      <td>B006T41X7U</td>\n",
       "      <td>AHIEHEZ66YNQJDIPDJYLUF4LSA5A</td>\n",
       "      <td>1492373902000</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125948</th>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Good</td>\n",
       "      <td>[]</td>\n",
       "      <td>B001O6SIBC</td>\n",
       "      <td>B001O6SIBC</td>\n",
       "      <td>AEREOYAQCHJF7PBXVD6SBPPNFJKA</td>\n",
       "      <td>1536802444241</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128451</th>\n",
       "      <td>5.0</td>\n",
       "      <td>STAR POWER!</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[{'small_image_url': 'https://images-na.ssl-im...</td>\n",
       "      <td>B016P3QHWA</td>\n",
       "      <td>B016P3QHWA</td>\n",
       "      <td>AFE6JIM26ZIBSKRHD6M7BBQFYGAQ</td>\n",
       "      <td>1494672957000</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129968</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Awesome</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>B09WQ2CM15</td>\n",
       "      <td>B09WQ2CM15</td>\n",
       "      <td>AHQ3NJU3MSRSPBSPPDGZUM7US2IQ</td>\n",
       "      <td>1663157494166</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        rating                                              title  \\\n",
       "3514       5.0                                                NaN   \n",
       "11585      5.0                                                NaN   \n",
       "11586      5.0                                                NaN   \n",
       "12676      3.0                                          I like it   \n",
       "12677      3.0                                          I like it   \n",
       "15588      5.0                                         Five Stars   \n",
       "20784      5.0                                             Rating   \n",
       "22838      5.0                                                NaN   \n",
       "25309      5.0                    the whole CD is great listening   \n",
       "27022      5.0                                         Five Stars   \n",
       "27623      5.0                                                NaN   \n",
       "31001      5.0                                         Five Stars   \n",
       "35245      5.0                                                NaN   \n",
       "36252      5.0                                          Excellent   \n",
       "50472      4.0                                         Four Stars   \n",
       "51745      3.0                                                NaN   \n",
       "59212      5.0                                         Five Stars   \n",
       "62618      5.0                                                NaN   \n",
       "63355      5.0                                                NaN   \n",
       "64410      5.0                                                NaN   \n",
       "70825      5.0                                               GOOD   \n",
       "72543      5.0                                                NaN   \n",
       "75169      4.0                                         Four Stars   \n",
       "75170      4.0                                         Four Stars   \n",
       "75171      4.0                                         Four Stars   \n",
       "75172      4.0                                         Four Stars   \n",
       "77334      5.0                                         Five Stars   \n",
       "82168      5.0                                                NaN   \n",
       "82470      5.0                               CD in good condition   \n",
       "83347      5.0                                                NaN   \n",
       "83789      5.0                                                NaN   \n",
       "88734      5.0                                                NaN   \n",
       "89366      5.0  Anything with the late great Tommy López is a ...   \n",
       "91292      3.0                                                NaN   \n",
       "91464      5.0                                                NaN   \n",
       "91940      5.0                                       Good product   \n",
       "92762      3.0                                                NaN   \n",
       "93519      5.0                                                NaN   \n",
       "95052      5.0                                                NaN   \n",
       "100698     4.0                                         Four Stars   \n",
       "101750     5.0                                         Five Stars   \n",
       "103395     5.0                                      Music I like.   \n",
       "106782     5.0                                               Good   \n",
       "107256     5.0                                                NaN   \n",
       "117245     5.0                                                NaN   \n",
       "119246     5.0                                         Five Stars   \n",
       "119533     4.0                                         Four Stars   \n",
       "119736     5.0  The CD arrived in time for the usesge Inplanne...   \n",
       "122287     4.0                                                NaN   \n",
       "122288     3.0                                                NaN   \n",
       "122689     5.0                                                NaN   \n",
       "124544     5.0                                                NaN   \n",
       "124818     5.0                                                NaN   \n",
       "124980     5.0                                         Five Stars   \n",
       "125948     5.0                                                NaN   \n",
       "128451     5.0                                        STAR POWER!   \n",
       "129968     5.0                                            Awesome   \n",
       "\n",
       "                                                     text  \\\n",
       "3514                                       Beautiful song   \n",
       "11585                                          listen too   \n",
       "11586                                        listening to   \n",
       "12676                                                 NaN   \n",
       "12677                                                 NaN   \n",
       "15588                                                 NaN   \n",
       "20784                                                 NaN   \n",
       "22838                     I am really enjoying my new cd.   \n",
       "25309                                                 NaN   \n",
       "27022                                                 NaN   \n",
       "27623                                     LOVE his music!   \n",
       "31001                                                 NaN   \n",
       "35245                                                 NaN   \n",
       "36252                                                 NaN   \n",
       "50472                                                 NaN   \n",
       "51745   It was ok , pretty music but not as big of fan...   \n",
       "59212                                                 NaN   \n",
       "62618                                         Great songs   \n",
       "63355                                             Nothing   \n",
       "64410            Easy listening with soft lights and wine   \n",
       "70825                                                 NaN   \n",
       "72543                                                Good   \n",
       "75169                                                 NaN   \n",
       "75170                                                 NaN   \n",
       "75171                                                 NaN   \n",
       "75172                                                 NaN   \n",
       "77334                                                 NaN   \n",
       "82168   Very good music to the ears. This is the compl...   \n",
       "82470                                                 NaN   \n",
       "83347                                             Good CD   \n",
       "83789                                               Great   \n",
       "88734                                                 NaN   \n",
       "89366                                                 NaN   \n",
       "91292   Great band great music. This family truly can ...   \n",
       "91464                                             Like it   \n",
       "91940                                                 NaN   \n",
       "92762                           Wanted only one song #11.   \n",
       "93519                                                 NaN   \n",
       "95052                                                 NaN   \n",
       "100698                                                NaN   \n",
       "101750                                                NaN   \n",
       "103395                                                NaN   \n",
       "106782                                                NaN   \n",
       "107256                                                NaN   \n",
       "117245  This not a review, Only way can update the sel...   \n",
       "119246                                                NaN   \n",
       "119533                                                NaN   \n",
       "119736                                                NaN   \n",
       "122287                                 Interesting music.   \n",
       "122288                                      Not his best.   \n",
       "122689               It's a CD - what is there to report?   \n",
       "124544                                                NaN   \n",
       "124818        Nice compilation of Coltrane's compositions   \n",
       "124980                                                NaN   \n",
       "125948                                               Good   \n",
       "128451                                                NaN   \n",
       "129968                                                NaN   \n",
       "\n",
       "                                                   images        asin  \\\n",
       "3514                                                   []  B07KC42LCV   \n",
       "11585                                                  []  B07ZGL8MP6   \n",
       "11586                                                  []  B07QPHD1FH   \n",
       "12676                                                  []  B0008JFQ8O   \n",
       "12677                                                  []  B001E4CZGI   \n",
       "15588                                                  []  B00PTBZQ6S   \n",
       "20784                                                  []  B09PGTMG8T   \n",
       "22838                                                  []  B000EY5FAM   \n",
       "25309                                                  []  B01JT71BVQ   \n",
       "27022                                                  []  B00PDNS8F8   \n",
       "27623                                                  []  B07BN2LT8N   \n",
       "31001   [{'small_image_url': 'https://images-na.ssl-im...  B01E0PAKDC   \n",
       "35245                                                  []  B003ZZUC0K   \n",
       "36252                                                  []  B01HMN3M7U   \n",
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       "62618                                                  []  B07QPC7RR5   \n",
       "63355                                                  []  B0898XWBRW   \n",
       "64410                                                  []  B000MGMFLE   \n",
       "70825                                                  []  B07D22TZ78   \n",
       "72543                                                  []  B08L718W4P   \n",
       "75169                                                  []  B000FCDBKY   \n",
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       "75172                                                  []  B000S2TE8E   \n",
       "77334   [{'small_image_url': 'https://images-na.ssl-im...  B002UDYIAI   \n",
       "82168                                                  []  B0190M9VQ2   \n",
       "82470                                                  []  B008LCV98Q   \n",
       "83347                                                  []  B00EJWT9UW   \n",
       "83789                                                  []  B09GRY9XW3   \n",
       "88734                                                  []  B00PUUGZNQ   \n",
       "89366   [{'small_image_url': 'https://images-na.ssl-im...  B01EZE2W48   \n",
       "91292                                                  []  B07L9XQL3R   \n",
       "91464                                                  []  B00YM8K164   \n",
       "91940                                                  []  B082QNJ9RT   \n",
       "92762                                                  []  B00FY3VN9K   \n",
       "93519                                                  []  B08TG5L2VW   \n",
       "95052                                                  []  B0B57VBBHZ   \n",
       "100698  [{'small_image_url': 'https://images-na.ssl-im...  B014I7B8GK   \n",
       "101750                                                 []  B0012GNG70   \n",
       "103395                                                 []  B08479PC93   \n",
       "106782                                                 []  B00Y301X9A   \n",
       "107256                                                 []  B092H9LB22   \n",
       "117245                                                 []  B000BDHB30   \n",
       "119246  [{'small_image_url': 'https://images-na.ssl-im...  B01G557XSA   \n",
       "119533  [{'small_image_url': 'https://images-na.ssl-im...  B07719ZGRY   \n",
       "119736                                                 []  B01GUGYLT8   \n",
       "122287                                                 []  B00KAG6SVO   \n",
       "122288                                                 []  B00JDSWVBG   \n",
       "122689                                                 []  B082TKPTZG   \n",
       "124544                                                 []  B019GLMNVM   \n",
       "124818                                                 []  B013Q6TAAU   \n",
       "124980  [{'small_image_url': 'https://images-na.ssl-im...  B006T41X7U   \n",
       "125948                                                 []  B001O6SIBC   \n",
       "128451  [{'small_image_url': 'https://images-na.ssl-im...  B016P3QHWA   \n",
       "129968                                                 []  B09WQ2CM15   \n",
       "\n",
       "       parent_asin                       user_id      timestamp  helpful_vote  \\\n",
       "3514    B07KC42LCV  AG57MN3I7PVZP2GCQSVG77KHYSCA  1558879851627             0   \n",
       "11585   B07ZGL8MP6  AGJATVTSGTPHR5SYDZZV3TKQTXBQ  1597784991788             0   \n",
       "11586   B07QPHD1FH  AGJATVTSGTPHR5SYDZZV3TKQTXBQ  1597503726341             0   \n",
       "12676   B0008JFQ8O  AGCJI4FXNAQJDCZS5FISCP6NKXRA  1627849943672             0   \n",
       "12677   B001E4CZGI  AGCJI4FXNAQJDCZS5FISCP6NKXRA  1627849896522             0   \n",
       "15588   B00PTBZQ6S  AEUQYMN7IVESRXCIZM4Q5QVNVHEA  1487107665000             0   \n",
       "20784   B09PGTMG8T  AG23W5SN5G4MDVFNEULOCQ2REUQA  1671891381419             0   \n",
       "22838   B000EY5FAM  AFUWOIDOULJLDGQQ33ZHQRDYYD2Q  1543353219723             0   \n",
       "25309   B01JT71BVQ  AGBOXS57LTAWM6B7UVIHSMMABQJQ  1601750267637             0   \n",
       "27022   B00PDNS8F8  AH2BVFJD6XD22W3STSD2W2B3ZJZQ  1495002483000             0   \n",
       "27623   B07BN2LT8N  AG4ABNZBKHDQBTC56VSXZFKICJHQ  1577563864446             1   \n",
       "31001   B01E0PAKDC  AE2ZIEOK5G237ARLMGPZDBP4TQCQ  1470745661000             1   \n",
       "35245   B003ZZUC0K  AFNPDNBXZQZKMPBN3KRVSYVCB7YA  1670772634826             0   \n",
       "36252   B01HMN3M7U  AHDQZRM565ZUDHZNS6YOAPMPTRQQ  1648641285034             0   \n",
       "50472   B0194436HG  AEVZMSUTXSVXKGAJJ7TI3ZMUYJ3Q  1465008866000             0   \n",
       "51745   B07JNCFRHP  AFEEZYGJTSPXZULSGYF7PIY2JGEQ  1553971331382             0   \n",
       "59212   B00OOO2W2C  AG6RAEYW2M7VH4FLVQPVL5DNHWGQ  1483627589000             0   \n",
       "62618   B07QPC7RR5  AE73IYD45HN5WW6RHGM7YNANCCWA  1606946741730             2   \n",
       "63355   B0898XWBRW  AHCLIMFY73XW7V324AJWEABOACVQ  1627337535385             0   \n",
       "64410   B000MGMFLE  AHNVWE54PILUX5VC4E7AAIX2UJWQ  1483402147000             0   \n",
       "70825   B07D22TZ78  AGBQ6VB267Q4OYWWSN5VYOQIFHSA  1623674397735             0   \n",
       "72543   B08L718W4P  AETAWHCYC2HCOC6L2CSDDHJWYLRQ  1673525759792             0   \n",
       "75169   B000FCDBKY  AHOPAABTJDRGBWN37HFGYJ5XPQAQ  1427651968000             0   \n",
       "75170   B000HCNE90  AHOPAABTJDRGBWN37HFGYJ5XPQAQ  1427651952000             0   \n",
       "75171   B000GTBWW0  AHOPAABTJDRGBWN37HFGYJ5XPQAQ  1427651931000             0   \n",
       "75172   B000S2TE8E  AHOPAABTJDRGBWN37HFGYJ5XPQAQ  1427651910000             0   \n",
       "77334   B002UDYIAI  AGGLFTPFEX6QWPKQLKTABHIHPC4Q  1496374982000             0   \n",
       "82168   B0190M9VQ2  AEEVYIDQHGFMJM2WPH3CETB5GY4A  1657280673375             0   \n",
       "82470   B008LCV98Q  AENSNZNYJEY6WOVB7FT3E2VP6Q2Q  1566790296960             0   \n",
       "83347   B00EJWT9UW  AG2TFOAXNIQB3AZ6TDTXNHE7AA5A  1563101693691             0   \n",
       "83789   B09GRY9XW3  AHW73OTACSMAOOOTRUCAH7LWYC4Q  1681286521411             0   \n",
       "88734   B00PUUGZNQ  AGEOITIPPYF3LUOAF3W4H2XJGATA  1639451738271             0   \n",
       "89366   B01EZE2W48  AEAQYUIECUQF36ZLPMOFY32LU3GQ  1510015720335             0   \n",
       "91292   B07L9XQL3R  AELWC576LKQTFBYYAW4H746KQQKQ  1603135881388             0   \n",
       "91464   B00YM8K164  AEMEIGHR45R7RFJ2QLWXIT47HS6Q  1639007657088             1   \n",
       "91940   B082QNJ9RT  AG2PS3F2FFYKKV4YQV7Z4TQSA7JQ  1675369325343             0   \n",
       "92762   B00FY3VN9K  AGZ7QNTF5765SXB3IN6D6AI7ITKA  1590785812572             0   \n",
       "93519   B08TG5L2VW  AHUJPI7HG5NNWP62LEHHBIVFK2HA  1671492004859             0   \n",
       "95052   B0B57VBBHZ  AGYPKIBSKZ44O67F5DW6ZWKQAEVQ  1684315690957             0   \n",
       "100698  B014I7B8GK  AEOB3RX6P2E46WQCXAIOXXH5NQ3A  1523434428165             0   \n",
       "101750  B0012GNG70  AG5WX46AOZD7ZEDBQJLK2SNXSCOA  1458176043000             0   \n",
       "103395  B08479PC93  AF77XZ67BUR4MPEXHCOOPIHNL4ZQ  1661885523185             0   \n",
       "106782  B00Y301X9A  AGLVIZVQAJD6ICBKFRRL77HRM6SA  1581106715635             0   \n",
       "107256  B092H9LB22  AEIKU62NBNI4O2X2ASROVJDIPXPA  1683516429615             0   \n",
       "117245  B000BDHB30  AFRBE6S2HYU2GUKRJIX4QTKVK3SA  1539086314717             0   \n",
       "119246  B01G557XSA  AEKIRATB6BFHSDA36VP3AEDLYA2A  1492711768000             0   \n",
       "119533  B07719ZGRY  AHVBT26GIJVZQY6OTWC3LGPQVVNQ  1510869188243             0   \n",
       "119736  B01GUGYLT8  AFYYJTDAGXST7DN3ORNSAD2PPGKA  1629073389511             0   \n",
       "122287  B00KAG6SVO  AG5ECJ5HE5SAYRC5RCSLJIBSFBOQ  1419871147000             0   \n",
       "122288  B00JDSWVBG  AG5ECJ5HE5SAYRC5RCSLJIBSFBOQ  1419871062000             0   \n",
       "122689  B082TKPTZG  AEHW64Y4VHXNNFNIBDWFSPVZZTAQ  1663120148087             0   \n",
       "124544  B019GLMNVM  AHYFXMCRLG7AKHVXBXQGOCADHEAQ  1609186115103             0   \n",
       "124818  B013Q6TAAU  AFUYYEFFJNTSZW54DCCFC53T6ZEQ  1556575185170             0   \n",
       "124980  B006T41X7U  AHIEHEZ66YNQJDIPDJYLUF4LSA5A  1492373902000             0   \n",
       "125948  B001O6SIBC  AEREOYAQCHJF7PBXVD6SBPPNFJKA  1536802444241             0   \n",
       "128451  B016P3QHWA  AFE6JIM26ZIBSKRHD6M7BBQFYGAQ  1494672957000             1   \n",
       "129968  B09WQ2CM15  AHQ3NJU3MSRSPBSPPDGZUM7US2IQ  1663157494166             0   \n",
       "\n",
       "        verified_purchase  \n",
       "3514                 True  \n",
       "11585                True  \n",
       "11586                True  \n",
       "12676                True  \n",
       "12677                True  \n",
       "15588                True  \n",
       "20784                True  \n",
       "22838                True  \n",
       "25309                True  \n",
       "27022                True  \n",
       "27623                True  \n",
       "31001                True  \n",
       "35245                True  \n",
       "36252                True  \n",
       "50472                True  \n",
       "51745                True  \n",
       "59212                True  \n",
       "62618                True  \n",
       "63355                True  \n",
       "64410                True  \n",
       "70825                True  \n",
       "72543                True  \n",
       "75169                True  \n",
       "75170                True  \n",
       "75171                True  \n",
       "75172                True  \n",
       "77334                True  \n",
       "82168                True  \n",
       "82470                True  \n",
       "83347                True  \n",
       "83789                True  \n",
       "88734                True  \n",
       "89366                True  \n",
       "91292                True  \n",
       "91464                True  \n",
       "91940                True  \n",
       "92762                True  \n",
       "93519                True  \n",
       "95052                True  \n",
       "100698              False  \n",
       "101750               True  \n",
       "103395               True  \n",
       "106782               True  \n",
       "107256               True  \n",
       "117245               True  \n",
       "119246               True  \n",
       "119533               True  \n",
       "119736               True  \n",
       "122287               True  \n",
       "122288               True  \n",
       "122689               True  \n",
       "124544               True  \n",
       "124818               True  \n",
       "124980              False  \n",
       "125948               True  \n",
       "128451               True  \n",
       "129968               True  "
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see observations with missing title OR missing text\n",
    "\n",
    "empty_title_or_text = df[(df['title'].isnull()) | (df['text'].isnull())]\n",
    "empty_title_or_text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 758
    },
    "id": "DlkS0oXq_5WG",
    "outputId": "d30181c9-5bda-43e6-b0af-19012a4fd625"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numerical_boolean_columns = df.select_dtypes(include=['number', 'bool']).columns\n",
    "sns.pairplot(df[numerical_boolean_columns])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Data Preprocessing & First Model**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.metrics import classification_report, confusion_matrix, log_loss\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from imblearn.over_sampling import RandomOverSampler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "import re\n",
    "import html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "id": "T7yZMDiNBRxF"
   },
   "outputs": [],
   "source": [
    "\"\"\"clean text by removing HTML entities, emojis, and extra spaces.\"\"\"\n",
    "def clean_text(text):\n",
    "    if pd.isna(text):\n",
    "        return text\n",
    "    \n",
    "    text = str(text)\n",
    "    \n",
    "    # Remove any thml entities\n",
    "    text = html.unescape(text)\n",
    "    \n",
    "    # Remove an html tags\n",
    "    text = re.sub(r'<[^>]+>', '', text)\n",
    "    \n",
    "    # Remove emojis\n",
    "    emoji_pattern = re.compile(\"[\"\n",
    "        u\"\\U0001F600-\\U0001F64F\"\n",
    "        u\"\\U0001F300-\\U0001F5FF\"\n",
    "        u\"\\U0001F680-\\U0001F6FF\"\n",
    "        u\"\\U0001F1E0-\\U0001F1FF\"\n",
    "        \"]+\", flags=re.UNICODE)\n",
    "    text = emoji_pattern.sub(r'', text)\n",
    "    \n",
    "    # Convert to lowercase\n",
    "    text = text.lower()\n",
    "    \n",
    "    # Remove extra spaces\n",
    "    text = ' '.join(text.split())\n",
    "    \n",
    "    return text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"data preprocessing function on the dataframe\"\"\"\n",
    "def preprocess_data(df):\n",
    "    # first we filter the dataframe to be verified purchases only\n",
    "    df = df[df['verified_purchase'] == True].copy()\n",
    "    \n",
    "    df = df[['rating', 'title', 'text']].copy()\n",
    "    \n",
    "    # fill missing titles with text and vice versa\n",
    "    df['title'] = df.apply(lambda x: x['text'] if pd.isna(x['title']) and not pd.isna(x['text']) else x['title'], axis=1)\n",
    "    df['text'] = df.apply(lambda x: x['title'] if pd.isna(x['text']) and not pd.isna(x['title']) else x['text'], axis=1)\n",
    "    \n",
    "    # if both title and text are missing fill based on rating\n",
    "    missing_mask = df['title'].isna() & df['text'].isna()\n",
    "    df.loc[missing_mask & (df['rating'].isin([4, 5])), ['title', 'text']] = 'very good'\n",
    "    df.loc[missing_mask & (df['rating'] == 3), ['title', 'text']] = 'good'\n",
    "    df.loc[missing_mask & (df['rating'].isin([1, 2])), ['title', 'text']] = 'bad'\n",
    "    \n",
    "    # clean the titles and text\n",
    "    df['title'] = df['title'].apply(clean_text)\n",
    "    df['text'] = df['text'].apply(clean_text)\n",
    "    \n",
    "    # setup columns with features we want\n",
    "    df['title_exclamations'] = df['title'].apply(lambda x: x.count('!') if pd.notna(x) else 0)\n",
    "    df['title_questions'] = df['title'].apply(lambda x: x.count('?') if pd.notna(x) else 0)\n",
    "    df['title_word_count'] = df['title'].apply(lambda x: len(x.split()) if pd.notna(x) else 0)\n",
    "    \n",
    "    df['text_exclamations'] = df['text'].apply(lambda x: x.count('!') if pd.notna(x) else 0)\n",
    "    df['text_questions'] = df['text'].apply(lambda x: x.count('?') if pd.notna(x) else 0)\n",
    "    df['text_word_count'] = df['text'].apply(lambda x: len(x.split()) if pd.notna(x) else 0)\n",
    "    \n",
    "    # >= 3 is positive rating, otherwise negative\n",
    "    df['positive_review'] = (df['rating'] >= 3).astype(int)\n",
    "    \n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"Prepare features for modeling.\"\"\"\n",
    "def prepare_features(df):\n",
    "    features = [\n",
    "        'title_exclamations', 'title_questions', 'title_word_count',\n",
    "        'text_exclamations', 'text_questions', 'text_word_count'\n",
    "    ]\n",
    "    \n",
    "    X = df[features]\n",
    "    y = df['positive_review']\n",
    "    \n",
    "    return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_df = preprocess_data(df)\n",
    "X, y = prepare_features(processed_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.2, random_state=42, stratify=y\n",
    ")\n",
    "\n",
    "# featuer scaling with standard scaler\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = pd.DataFrame(scaler.fit_transform(X_train), columns=X_train.columns)\n",
    "X_test_scaled = pd.DataFrame(scaler.transform(X_test), columns=X_test.columns)\n",
    "\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "X_train_balanced, y_train_balanced = ros.fit_resample(X_train_scaled, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original class distribution:\n",
      "positive_review\n",
      "1    0.93421\n",
      "0    0.06579\n",
      "Name: proportion, dtype: float64\n",
      "\n",
      "Resampled class distribution:\n",
      "positive_review\n",
      "1    0.5\n",
      "0    0.5\n",
      "Name: proportion, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Original class distribution:\")\n",
    "print(pd.Series(y).value_counts(normalize=True))\n",
    "print(\"\\nResampled class distribution:\")\n",
    "print(pd.Series(y_train_balanced).value_counts(normalize=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression(max_iter=1000, random_state=42)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "LogisticRegression(max_iter=1000, random_state=42)"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = LogisticRegression(random_state=42, max_iter=1000)\n",
    "model.fit(X_train_balanced, y_train_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_pred = model.predict(X_train_scaled)\n",
    "test_pred = model.predict(X_test_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training Performance:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.09      0.46      0.16      5054\n",
      "           1       0.95      0.69      0.80     71772\n",
      "\n",
      "    accuracy                           0.67     76826\n",
      "   macro avg       0.52      0.57      0.48     76826\n",
      "weighted avg       0.89      0.67      0.75     76826\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classifiction report on training\n",
    "print(\"Training Performance:\")\n",
    "print(classification_report(y_train, train_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Test Performance:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.09      0.47      0.16      1264\n",
      "           1       0.95      0.68      0.79     17943\n",
      "\n",
      "    accuracy                           0.67     19207\n",
      "   macro avg       0.52      0.58      0.48     19207\n",
      "weighted avg       0.89      0.67      0.75     19207\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# classifiction report on testing\n",
    "print(\"\\nTest Performance:\")\n",
    "print(classification_report(y_test, test_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Fitting Curve\n",
    "\n",
    "complexity_values = np.logspace(-3, 3, 20)  # from simple to very complex\n",
    "train_errors = []\n",
    "test_errors = []\n",
    "\n",
    "for C in complexity_values:\n",
    "    model = LogisticRegression(random_state=42, max_iter=1000, C=C)\n",
    "    model.fit(X_train_balanced, y_train_balanced)\n",
    "    \n",
    "    train_probs = model.predict_proba(X_train_scaled)\n",
    "    test_probs = model.predict_proba(X_test_scaled)\n",
    "    \n",
    "    train_loss = log_loss(y_train, train_probs)\n",
    "    test_loss = log_loss(y_test, test_probs)\n",
    "    \n",
    "    train_errors.append(train_loss)\n",
    "    test_errors.append(test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(complexity_values, train_errors, label=\"Error on Training Data\", color='red')\n",
    "plt.plot(complexity_values, test_errors, label=\"Error on Test Data\", color='green')\n",
    "plt.xscale('log')  # logarithmic scale for complexity\n",
    "plt.xlabel(\"Model Complexity\")\n",
    "plt.ylabel(\"Predictive Error (Log Loss)\")\n",
    "plt.title(\"Fitting Graph: Training vs Test Error\")\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First Model Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Current Model Performance:\n",
    "\n",
    "Training vs Testing performance is very similar, which indicates the model is not overfitting:\n",
    "- Training accuracy: 0.67\n",
    "- Testing accuracy: 0.67\n",
    "\n",
    "Both also show similar metrics across precision, recall, and F1-score.\n",
    "\n",
    "Another thing to note is that the F1-score for the negative class is 0.16 both training and testing, whereas the F1-score for the positive class is much higher at around 0.79-0.80. This tell us that the imbalance within the dataset is still an issue, and we need to take better measures to reduce this issue.\n",
    "\n",
    "### Fitting Assessment:\n",
    "Based on the fitting graph, there might be slight underfitting as model complexity increases. The training error goes down as the number of\n",
    "degrees increases (so as model complexity increases) and so does the testing error too.\n",
    "\n",
    "### Possible Improvements:\n",
    "\n",
    "We could focus on better feature engineering. For example, creating new interaction features between our original columns and/or considering polynomial features. Additionally, we could try better techniques to account for the class imbalances such as using SMOTE instead of random oversampling and/or combining oversampling with undersampling (SMOTETomek or SMOTEENN).\n",
    "In addition, we could try using specific words in the texts as columns, like whether it contains \"good\" or \"bad\".\n",
    "\n",
    "\n",
    "### Next Models to Try (in order):\n",
    "Our final conclusion is this logistic regression model we have made is not sufficient in predicting whether a rating is good or bad from the title & text. Next, we look to apply better feature engineering and class imbalance techniques with more powerful models that can capture non-linear relationships such as decision trees. Decision trees would be especially helpful when considering the words in specific reviews, so we could add columns in the data for whether they contain certain keywords."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Second Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "processed_df['combined_text'] = processed_df['title'] + \" \" + processed_df['text']\n",
    "y = processed_df['positive_review']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from xgboost import XGBClassifier\n",
    "from sklearn.metrics import classification_report\n",
    "\n",
    "\n",
    "# TF-IDF vectorization\n",
    "tfidf = TfidfVectorizer(max_features=5000, ngram_range=(1, 2))\n",
    "X = tfidf.fit_transform(processed_df['combined_text'])\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, stratify=y, random_state=42)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Class distribution before SMOTE: positive_review\n",
      "1    71772\n",
      "0     5054\n",
      "Name: count, dtype: int64\n",
      "Class distribution after SMOTE: positive_review\n",
      "1    71772\n",
      "0    71772\n",
      "Name: count, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "from imblearn.over_sampling import SMOTE\n",
    "\n",
    "smote = SMOTE(random_state=42)\n",
    "X_train_balanced, y_train_balanced = smote.fit_resample(X_train, y_train)\n",
    "\n",
    "print(\"Class distribution before SMOTE:\", pd.Series(y_train).value_counts())\n",
    "print(\"Class distribution after SMOTE:\", pd.Series(y_train_balanced).value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Edward\\anaconda3\\envs\\151a_project\\lib\\site-packages\\xgboost\\core.py:158: UserWarning: [13:53:55] WARNING: C:\\buildkite-agent\\builds\\buildkite-windows-cpu-autoscaling-group-i-0c55ff5f71b100e98-1\\xgboost\\xgboost-ci-windows\\src\\learner.cc:740: \n",
      "Parameters: { \"use_label_encoder\" } are not used.\n",
      "\n",
      "  warnings.warn(smsg, UserWarning)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;mlogloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
       "              n_jobs=None, num_class=2, num_parallel_tree=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBClassifier<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric=&#x27;mlogloss&#x27;,\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
       "              n_jobs=None, num_class=2, num_parallel_tree=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
       "              colsample_bylevel=None, colsample_bynode=None,\n",
       "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "              enable_categorical=False, eval_metric='mlogloss',\n",
       "              feature_types=None, gamma=None, grow_policy=None,\n",
       "              importance_type=None, interaction_constraints=None,\n",
       "              learning_rate=None, max_bin=None, max_cat_threshold=None,\n",
       "              max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "              max_leaves=None, min_child_weight=None, missing=nan,\n",
       "              monotone_constraints=None, multi_strategy=None, n_estimators=None,\n",
       "              n_jobs=None, num_class=2, num_parallel_tree=None, ...)"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xgb = XGBClassifier(\n",
    "    objective='multi:softmax',\n",
    "    num_class=len(set(y)), \n",
    "    eval_metric='mlogloss',\n",
    "    use_label_encoder=False\n",
    ")\n",
    "\n",
    "xgb.fit(X_train_balanced, y_train_balanced)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.65      0.89      0.75      5054\n",
      "           1       0.99      0.97      0.98     71772\n",
      "\n",
      "    accuracy                           0.96     76826\n",
      "   macro avg       0.82      0.93      0.87     76826\n",
      "weighted avg       0.97      0.96      0.96     76826\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, xgb.predict(X_train)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.53      0.74      0.62      1264\n",
      "           1       0.98      0.95      0.97     17943\n",
      "\n",
      "    accuracy                           0.94     19207\n",
      "   macro avg       0.76      0.85      0.79     19207\n",
      "weighted avg       0.95      0.94      0.94     19207\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, xgb.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Fitting Curve\n",
    "max_depth_values = range(1, 12)  # From 1 (simple) to 12 (complex)\n",
    "train_errors = []\n",
    "test_errors = []\n",
    "\n",
    "for max_depth in max_depth_values:\n",
    "    xgb = XGBClassifier(\n",
    "        objective='multi:softmax',\n",
    "        num_class=len(set(y_train_balanced)),  \n",
    "        eval_metric='mlogloss',\n",
    "        max_depth=max_depth  \n",
    "    )\n",
    "    \n",
    "    xgb.fit(X_train_balanced, y_train_balanced)\n",
    "    \n",
    "    train_probs = xgb.predict_proba(X_train)\n",
    "    test_probs = xgb.predict_proba(X_test)\n",
    "    \n",
    "    train_loss = log_loss(y_train, train_probs)\n",
    "    test_loss = log_loss(y_test, test_probs)\n",
    "    \n",
    "    train_errors.append(train_loss)\n",
    "    test_errors.append(test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the learning curve\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(max_depth_values, train_errors, label='Training Loss')\n",
    "plt.plot(max_depth_values, test_errors, label='Test Loss')\n",
    "plt.xlabel('Complexity')\n",
    "plt.ylabel('Loss')\n",
    "plt.title('XGBoost Fitting Curve')\n",
    "plt.legend()\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True Positives (TP): 17377\n",
      "False Positives (FP): 389\n",
      "False Negatives (FN): 566\n",
      "True Negatives (TN): 875\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "tn, fp, fn, tp = confusion_matrix(y_test, xgb.predict(X_test)).ravel()\n",
    "\n",
    "print(f\"True Positives (TP): {tp}\")\n",
    "print(f\"False Positives (FP): {fp}\")\n",
    "print(f\"False Negatives (FN): {fn}\")\n",
    "print(f\"True Negatives (TN): {tn}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Current Model Performance:\n",
    "While precision, recall, and F1-score for **Class 1** (the majority class) are very high and consistent across both training and testing, the performance for **Class 0** (the minority class) drops notably in the test set. Specifically, recall and F1-score for **Class 0** are much higher during training than testing, indicating the model is better at identifying **Class 0** in the training data but struggles with generalization. Overall accuracy is higher in the training set (96%) than the test set (94%), and both the macro and weighted average F1-scores are also higher during training. These results suggest that the model is overly adapted to the training data and may benefit from techniques like regularization, resampling, or tuning to improve generalization and address the class imbalance.\n",
    "\n",
    "Overall though, it performs much better than the first model we made.\n",
    "\n",
    "### Fitting Assessment:\n",
    "Based on the fitting graph, there might be slight underfitting. The training error and test error are both decreasing as the number of iterations increases, but the train error is getting lower than the test error.\n",
    "\n",
    "### Possible Improvements:\n",
    "1. Fine-tuning Hyperparameters:\n",
    "    - Fine-tuning the learning rate (also known as eta in XGBoost) can help balance model performance. \n",
    "    - Max Depth: Experiment with different tree depths to control the complexity of the trees. Shallow trees might underfit, while too deep trees can overfit.\n",
    "\n",
    "2. Further SMOTE Improvements\n",
    "    - SMOTE Variants: Besides basic SMOTE, consider using variants like [Borderline-SMOTE or ADASYN](https://towardsdatascience.com/class-imbalance-smote-borderline-smote-adasyn-6e36c78d804), which generate synthetic samples based on boundary regions or difficult-to-classify examples.\n",
    "    \n",
    "3. Cross-Validation\n",
    "    - Stratified Cross-Validation: Ensure that we're using stratified K-fold cross-validation to maintain the class distribution in each fold, which is crucial for imbalanced datasets.\n",
    "\n",
    "### Next Models to Try:\n",
    "We are considering using ensemble methods as a next step. For example, we can first, use a classifier (like XGBoost) to predict whether the instance belongs to the minority or majority class. Then, for the minority class predictions, we can apply a more specialized classifier that focuses on fine-tuning the model's performance for those cases. Additionally, we are also thinking of using deep neural network models (like BERT-based) for better sentiment understanding in the text."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Final Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = processed_df['combined_text']\n",
    "y = processed_df['positive_review']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from tensorflow import keras\n",
    "\n",
    "max_tokens = 1000\n",
    "max_length = 1000\n",
    "\n",
    "vectorizer = keras.layers.TextVectorization(max_tokens=max_tokens, output_mode='int', output_sequence_length=max_length)\n",
    "\n",
    "vectorizer.adapt(np.array(X))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = vectorizer(np.array(X))\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X.numpy(), y, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "smote = SMOTE()\n",
    "X_train_resampled, y_train_resampled = smote.fit_resample(X_train, y_train)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.Sequential() \n",
    "\n",
    "model.add(keras.layers.Embedding(max_tokens, 32, input_length=max_length)) \n",
    "\n",
    "model.add(keras.layers.Conv1D(32, 7, activation='relu'))\n",
    "\n",
    "model.add(keras.layers.MaxPooling1D(5))\n",
    "\n",
    "model.add(keras.layers.Conv1D(32, 7, activation='relu'))\n",
    "\n",
    "model.add(keras.layers.GlobalMaxPooling1D())\n",
    "\n",
    "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer=keras.optimizers.AdamW(), metrics=['accuracy']) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_6\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding_5 (Embedding)     (None, 1000, 32)          32000     \n",
      "                                                                 \n",
      " conv1d_10 (Conv1D)          (None, 994, 32)           7200      \n",
      "                                                                 \n",
      " max_pooling1d_5 (MaxPoolin  (None, 198, 32)           0         \n",
      " g1D)                                                            \n",
      "                                                                 \n",
      " conv1d_11 (Conv1D)          (None, 192, 32)           7200      \n",
      "                                                                 \n",
      " global_max_pooling1d_5 (Gl  (None, 32)                0         \n",
      " obalMaxPooling1D)                                               \n",
      "                                                                 \n",
      " dense_5 (Dense)             (None, 1)                 33        \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 46433 (181.38 KB)\n",
      "Trainable params: 46433 (181.38 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/5\n",
      "5745/5745 [==============================] - 125s 22ms/step - loss: 0.1789 - accuracy: 0.9281 - val_loss: 0.1211 - val_accuracy: 0.9473\n",
      "Epoch 2/5\n",
      "5745/5745 [==============================] - 127s 22ms/step - loss: 0.1100 - accuracy: 0.9589 - val_loss: 0.0479 - val_accuracy: 0.9814\n",
      "Epoch 3/5\n",
      "5745/5745 [==============================] - 133s 23ms/step - loss: 0.0896 - accuracy: 0.9664 - val_loss: 0.0880 - val_accuracy: 0.9627\n",
      "Epoch 4/5\n",
      "5745/5745 [==============================] - 123s 21ms/step - loss: 0.0740 - accuracy: 0.9719 - val_loss: 0.0818 - val_accuracy: 0.9658\n",
      "Epoch 5/5\n",
      "5745/5745 [==============================] - 124s 22ms/step - loss: 0.0615 - accuracy: 0.9769 - val_loss: 0.0610 - val_accuracy: 0.9747\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.History at 0x1bd65e7e460>"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train_resampled, y_train_resampled, validation_split=0.2, epochs=5, batch_size=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2401/2401 [==============================] - 14s 6ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred_prob = model.predict(X_train)\n",
    "\n",
    "y_pred = (y_pred_prob > 0.5).astype(\"int32\").flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.85      0.90      0.87      5020\n",
      "           1       0.99      0.99      0.99     71806\n",
      "\n",
      "    accuracy                           0.98     76826\n",
      "   macro avg       0.92      0.94      0.93     76826\n",
      "weighted avg       0.98      0.98      0.98     76826\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_train, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "601/601 [==============================] - 4s 6ms/step\n"
     ]
    }
   ],
   "source": [
    "y_pred_prob = model.predict(X_test)\n",
    "\n",
    "y_pred = (y_pred_prob > 0.5).astype(\"int32\").flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.67      0.64      1298\n",
      "           1       0.98      0.97      0.97     17909\n",
      "\n",
      "    accuracy                           0.95     19207\n",
      "   macro avg       0.79      0.82      0.80     19207\n",
      "weighted avg       0.95      0.95      0.95     19207\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  }
 ],
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