movie-rec-system-fo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Load the datasets\n",
    "movies = pd.read_csv('datasets/rotten_tomatoes_movies.csv')\n",
    "reviews = pd.read_csv('datasets/rotten_tomatoes_movie_reviews.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 143258 entries, 0 to 143257\n",
      "Data columns (total 16 columns):\n",
      " #   Column                Non-Null Count   Dtype  \n",
      "---  ------                --------------   -----  \n",
      " 0   id                    143258 non-null  object \n",
      " 1   title                 142891 non-null  object \n",
      " 2   audienceScore         73248 non-null   float64\n",
      " 3   tomatoMeter           33877 non-null   float64\n",
      " 4   rating                13991 non-null   object \n",
      " 5   ratingContents        13991 non-null   object \n",
      " 6   releaseDateTheaters   30773 non-null   object \n",
      " 7   releaseDateStreaming  79420 non-null   object \n",
      " 8   runtimeMinutes        129431 non-null  float64\n",
      " 9   genre                 132175 non-null  object \n",
      " 10  originalLanguage      129400 non-null  object \n",
      " 11  director              139041 non-null  object \n",
      " 12  writer                90116 non-null   object \n",
      " 13  boxOffice             14743 non-null   object \n",
      " 14  distributor           23001 non-null   object \n",
      " 15  soundMix              15917 non-null   object \n",
      "dtypes: float64(3), object(13)\n",
      "memory usage: 17.5+ MB\n",
      "None\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1444963 entries, 0 to 1444962\n",
      "Data columns (total 11 columns):\n",
      " #   Column          Non-Null Count    Dtype \n",
      "---  ------          --------------    ----- \n",
      " 0   id              1444963 non-null  object\n",
      " 1   reviewId        1444963 non-null  int64 \n",
      " 2   creationDate    1444963 non-null  object\n",
      " 3   criticName      1444963 non-null  object\n",
      " 4   isTopCritic     1444963 non-null  bool  \n",
      " 5   originalScore   1009745 non-null  object\n",
      " 6   reviewState     1444963 non-null  object\n",
      " 7   publicatioName  1444963 non-null  object\n",
      " 8   reviewText      1375738 non-null  object\n",
      " 9   scoreSentiment  1444963 non-null  object\n",
      " 10  reviewUrl       1234038 non-null  object\n",
      "dtypes: bool(1), int64(1), object(9)\n",
      "memory usage: 111.6+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Display basic information\n",
    "print(movies.info())\n",
    "print(reviews.info())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>reviewId</th>\n",
       "      <th>creationDate</th>\n",
       "      <th>criticName</th>\n",
       "      <th>isTopCritic</th>\n",
       "      <th>originalScore</th>\n",
       "      <th>reviewState</th>\n",
       "      <th>publicatioName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>scoreSentiment</th>\n",
       "      <th>reviewUrl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beavers</td>\n",
       "      <td>1145982</td>\n",
       "      <td>2003-05-23</td>\n",
       "      <td>Ivan M. Lincoln</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Deseret News (Salt Lake City)</td>\n",
       "      <td>Timed to be just long enough for most youngste...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.deseretnews.com/article/700003233/B...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blood_mask</td>\n",
       "      <td>1636744</td>\n",
       "      <td>2007-06-02</td>\n",
       "      <td>The Foywonder</td>\n",
       "      <td>False</td>\n",
       "      <td>1/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Dread Central</td>\n",
       "      <td>It doesn't matter if a movie costs 300 million...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>http://www.dreadcentral.com/index.php?name=Rev...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>city_hunter_shinjuku_private_eyes</td>\n",
       "      <td>2590987</td>\n",
       "      <td>2019-05-28</td>\n",
       "      <td>Reuben Baron</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fresh</td>\n",
       "      <td>CBR</td>\n",
       "      <td>The choreography is so precise and lifelike at...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.cbr.com/city-hunter-shinjuku-priva...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city_hunter_shinjuku_private_eyes</td>\n",
       "      <td>2558908</td>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>Matt Schley</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Japan Times</td>\n",
       "      <td>The film's out-of-touch attempts at humor may ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.japantimes.co.jp/culture/2019/02/0...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2504681</td>\n",
       "      <td>2018-08-29</td>\n",
       "      <td>Pat Padua</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fresh</td>\n",
       "      <td>DCist</td>\n",
       "      <td>Its clumsy determination is endearing and some...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://dcist.com/2015/11/out_of_frame_dangerou...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444958</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706151</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Christie Cronan</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Raising Whasians</td>\n",
       "      <td>Solid but not totally sold&amp;#44; Thor&amp;#58; Ragn...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://raisingwhasians.com/thor-love-and-thun...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444959</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706150</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Ian Sandwell</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Digital Spy</td>\n",
       "      <td>Thor&amp;#58; Love and Thunder is the most enterta...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.digitalspy.com/movies/a40496050/th...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444960</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706149</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Lauren LaMagna</td>\n",
       "      <td>False</td>\n",
       "      <td>8/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Next Best Picture</td>\n",
       "      <td>&amp;quot;Thor&amp;#58; Love and Thunder&amp;quot; is a st...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.nextbestpicture.com/thor-love-and-...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444961</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706148</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Jake Cole</td>\n",
       "      <td>True</td>\n",
       "      <td>1/4</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Slant Magazine</td>\n",
       "      <td>Across Taika Waititi&amp;#8217;s film&amp;#44; a war a...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.slantmagazine.com/film/thor-love-a...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444962</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706147</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Roger Moore</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Movie Nation</td>\n",
       "      <td>It&amp;#8217;s the jokes that make it&amp;#44; with th...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://rogersmovienation.com/2022/07/05/movie...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1444963 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id   reviewId creationDate  \\\n",
       "0                                  beavers    1145982   2003-05-23   \n",
       "1                               blood_mask    1636744   2007-06-02   \n",
       "2        city_hunter_shinjuku_private_eyes    2590987   2019-05-28   \n",
       "3        city_hunter_shinjuku_private_eyes    2558908   2019-02-14   \n",
       "4                       dangerous_men_2015    2504681   2018-08-29   \n",
       "...                                    ...        ...          ...   \n",
       "1444958              thor_love_and_thunder  102706151   2022-07-05   \n",
       "1444959              thor_love_and_thunder  102706150   2022-07-05   \n",
       "1444960              thor_love_and_thunder  102706149   2022-07-05   \n",
       "1444961              thor_love_and_thunder  102706148   2022-07-05   \n",
       "1444962              thor_love_and_thunder  102706147   2022-07-05   \n",
       "\n",
       "              criticName  isTopCritic originalScore reviewState  \\\n",
       "0        Ivan M. Lincoln        False         3.5/4       fresh   \n",
       "1          The Foywonder        False           1/5      rotten   \n",
       "2           Reuben Baron        False           NaN       fresh   \n",
       "3            Matt Schley        False         2.5/5      rotten   \n",
       "4              Pat Padua        False           NaN       fresh   \n",
       "...                  ...          ...           ...         ...   \n",
       "1444958  Christie Cronan        False          7/10       fresh   \n",
       "1444959     Ian Sandwell        False           4/5       fresh   \n",
       "1444960   Lauren LaMagna        False          8/10       fresh   \n",
       "1444961        Jake Cole         True           1/4      rotten   \n",
       "1444962      Roger Moore        False         2.5/4       fresh   \n",
       "\n",
       "                        publicatioName  \\\n",
       "0        Deseret News (Salt Lake City)   \n",
       "1                        Dread Central   \n",
       "2                                  CBR   \n",
       "3                          Japan Times   \n",
       "4                                DCist   \n",
       "...                                ...   \n",
       "1444958               Raising Whasians   \n",
       "1444959                    Digital Spy   \n",
       "1444960              Next Best Picture   \n",
       "1444961                 Slant Magazine   \n",
       "1444962                   Movie Nation   \n",
       "\n",
       "                                                reviewText scoreSentiment  \\\n",
       "0        Timed to be just long enough for most youngste...       POSITIVE   \n",
       "1        It doesn't matter if a movie costs 300 million...       NEGATIVE   \n",
       "2        The choreography is so precise and lifelike at...       POSITIVE   \n",
       "3        The film's out-of-touch attempts at humor may ...       NEGATIVE   \n",
       "4        Its clumsy determination is endearing and some...       POSITIVE   \n",
       "...                                                    ...            ...   \n",
       "1444958  Solid but not totally sold&#44; Thor&#58; Ragn...       POSITIVE   \n",
       "1444959  Thor&#58; Love and Thunder is the most enterta...       POSITIVE   \n",
       "1444960  &quot;Thor&#58; Love and Thunder&quot; is a st...       POSITIVE   \n",
       "1444961  Across Taika Waititi&#8217;s film&#44; a war a...       NEGATIVE   \n",
       "1444962  It&#8217;s the jokes that make it&#44; with th...       POSITIVE   \n",
       "\n",
       "                                                 reviewUrl  \n",
       "0        http://www.deseretnews.com/article/700003233/B...  \n",
       "1        http://www.dreadcentral.com/index.php?name=Rev...  \n",
       "2        https://www.cbr.com/city-hunter-shinjuku-priva...  \n",
       "3        https://www.japantimes.co.jp/culture/2019/02/0...  \n",
       "4        http://dcist.com/2015/11/out_of_frame_dangerou...  \n",
       "...                                                    ...  \n",
       "1444958  https://raisingwhasians.com/thor-love-and-thun...  \n",
       "1444959  https://www.digitalspy.com/movies/a40496050/th...  \n",
       "1444960  https://www.nextbestpicture.com/thor-love-and-...  \n",
       "1444961  https://www.slantmagazine.com/film/thor-love-a...  \n",
       "1444962  https://rogersmovienation.com/2022/07/05/movie...  \n",
       "\n",
       "[1444963 rows x 11 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# movies_df = movies_df.dropna(subset=['originalLanguage', 'originalScore'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "def normalize_score(score):\n",
    "    try:\n",
    "        if isinstance(score, str):\n",
    "            if '/' in score:  # Handle scores like '8/10'\n",
    "                score = score.strip(\"'\")  # Remove single quotes if present\n",
    "                value, max_value = map(float, score.split('/'))\n",
    "                if max_value == 0:\n",
    "                    raise ValueError(\"max_value cannot be zero\")\n",
    "                return (value / max_value) * 10\n",
    "            elif score.replace('.', '').isdigit():  # Handle numeric values with or without decimals\n",
    "                return float(score)\n",
    "            elif score.replace('.', '').replace('/', '').isdigit():  # Handle fractions\n",
    "                score = score.strip(\"'\")  # Remove single quotes if present\n",
    "                value, max_value = map(float, score.split('/'))\n",
    "                if max_value == 0:\n",
    "                    raise ValueError(\"max_value cannot be zero\")\n",
    "                return (value / max_value) * 10\n",
    "            else:  # Handle other formats\n",
    "                grade_to_score = {'A+': 10, 'A': 9, 'A-': 8, 'B+': 7, 'B': 6, 'B-': 5, 'C+': 4, 'C': 3, 'C-': 2, 'D+': 1, 'D': 1, 'D-': 1}\n",
    "                return grade_to_score.get(score.upper(), np.nan)\n",
    "        elif isinstance(score, (int, float)):\n",
    "            return score\n",
    "        else:\n",
    "            return np.nan\n",
    "    except (ValueError, AttributeError, ZeroDivisionError) as e:\n",
    "        print(f\"Error processing score: {score} - {e}\")\n",
    "        return np.nan\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Error processing score: ***1/2 - could not convert string to float: '***1'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 0/0 - max_value cannot be zero\n",
      "Error processing score: 2.1/2` - could not convert string to float: '2`'\n",
      "Error processing score: 1.5.4 - could not convert string to float: '1.5.4'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 4.5.5 - could not convert string to float: '4.5.5'\n",
      "Error processing score: 3/0 - max_value cannot be zero\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 1/2 star - could not convert string to float: '2 star'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 2.5.5 - could not convert string to float: '2.5.5'\n",
      "Error processing score: 3.5.4 - could not convert string to float: '3.5.4'\n",
      "Error processing score: 3 1/2/4 - could not convert string to float: '3 1'\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 0/0 - max_value cannot be zero\n",
      "Error processing score: 2 1/2 - could not convert string to float: '2 1'\n",
      "Error processing score: 1.5.5 - could not convert string to float: '1.5.5'\n",
      "Error processing score: 7/0 - max_value cannot be zero\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 1.5.4 - could not convert string to float: '1.5.4'\n",
      "Error processing score: 1.5.5 - could not convert string to float: '1.5.5'\n",
      "Error processing score: **1/2 - could not convert string to float: '**1'\n",
      "Error processing score: **1/2 - could not convert string to float: '**1'\n",
      "Error processing score: 2/4/ - could not convert string to float: ''\n",
      "Error processing score: 2.5.5 - could not convert string to float: '2.5.5'\n",
      "Error processing score: 3/5/5 - too many values to unpack (expected 2)\n",
      "Error processing score: 3 1/2 - could not convert string to float: '3 1'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 2.5/ - could not convert string to float: ''\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 4 1/2/5 - could not convert string to float: '4 1'\n",
      "Error processing score: 2.5/5Too tepid and too predictable of a picture to - could not convert string to float: '5Too tepid and too predictable of a picture to'\n",
      "Error processing score: 2/5/4 - too many values to unpack (expected 2)\n",
      "Error processing score: 1/8/5 - too many values to unpack (expected 2)\n",
      "Error processing score: 0/0 - max_value cannot be zero\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 3/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 7.5.10 - could not convert string to float: '7.5.10'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 4 1/2 stars - could not convert string to float: '4 1'\n",
      "Error processing score: 1/4ANNAPOL - could not convert string to float: '4ANNAPOL'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: **1/2 - could not convert string to float: '**1'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 1.5.4 - could not convert string to float: '1.5.4'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 2.5.4 - could not convert string to float: '2.5.4'\n",
      "Error processing score: 2.5.4 - could not convert string to float: '2.5.4'\n",
      "Error processing score: 1.5.4 - could not convert string to float: '1.5.4'\n",
      "Error processing score: 2.5.4 - could not convert string to float: '2.5.4'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: B-/C+ - could not convert string to float: 'B-'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 3.5.4 - could not convert string to float: '3.5.4'\n",
      "Error processing score: 1/4 stars - could not convert string to float: '4 stars'\n",
      "Error processing score: 3.5/ - could not convert string to float: ''\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 2'/4 - could not convert string to float: \"2'\"\n",
      "Error processing score: 3 1/2/4 - could not convert string to float: '3 1'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 4.5.5 - could not convert string to float: '4.5.5'\n",
      "Error processing score: 4.5.5 - could not convert string to float: '4.5.5'\n",
      "Error processing score: ***1/2 - could not convert string to float: '***1'\n",
      "Error processing score: 1.5/ - could not convert string to float: ''\n",
      "Error processing score: 2.1/2` - could not convert string to float: '2`'\n",
      "Error processing score: 2/4 stars - could not convert string to float: '4 stars'\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: * 1/2 - could not convert string to float: '* 1'\n",
      "Error processing score: 2.5.4 - could not convert string to float: '2.5.4'\n",
      "Error processing score: 21/2 pink ponies out of five - could not convert string to float: '2 pink ponies out of five'\n",
      "Error processing score: 2/4 stars - could not convert string to float: '4 stars'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 3. 5/5 - could not convert string to float: '3. 5'\n",
      "Error processing score: 1/2/5 - too many values to unpack (expected 2)\n",
      "Error processing score: 4.5.5 - could not convert string to float: '4.5.5'\n",
      "Error processing score: ****1/2 - could not convert string to float: '****1'\n",
      "Error processing score: 3/4 stars - could not convert string to float: '4 stars'\n",
      "Error processing score: ****1/2 - could not convert string to float: '****1'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 3 1/2/4 - could not convert string to float: '3 1'\n",
      "Error processing score: 5/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 3/4f - could not convert string to float: '4f'\n",
      "Error processing score: 1.5.5 - could not convert string to float: '1.5.5'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 4/5http://www.bbc.co.uk/films/2007/08/27/a_throw_o - could not convert string to float: '5http:'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 61/00 - max_value cannot be zero\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: w.5/4 - could not convert string to float: 'w.5'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: B/B- - could not convert string to float: 'B'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 2.5/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 0/0 - max_value cannot be zero\n",
      "Error processing score: 5/10- - could not convert string to float: '10-'\n",
      "Error processing score: 2.5.4 - could not convert string to float: '2.5.4'\n",
      "Error processing score: ***1/2 - could not convert string to float: '***1'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 2/4The best that can be said about the movie is th - could not convert string to float: '4The best that can be said about the movie is th'\n",
      "Error processing score: 1/5/4 - too many values to unpack (expected 2)\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 3 1/2  out of 4 - could not convert string to float: '3 1'\n",
      "Error processing score: 2.5.5 - could not convert string to float: '2.5.5'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: * 1/2 - could not convert string to float: '* 1'\n",
      "Error processing score: 3/5/4 - too many values to unpack (expected 2)\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 4/5ao - could not convert string to float: '5ao'\n",
      "Error processing score: 3.5.4 - could not convert string to float: '3.5.4'\n",
      "Error processing score: 3.5/4A - could not convert string to float: '4A'\n",
      "Error processing score: 2.5./4 - could not convert string to float: '2.5.'\n",
      "Error processing score: 2.5/'5 - could not convert string to float: \"'5\"\n",
      "Error processing score: 3/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 3.0/.0 - max_value cannot be zero\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 1.5.4 - could not convert string to float: '1.5.4'\n",
      "Error processing score: 2.5.4 - could not convert string to float: '2.5.4'\n",
      "Error processing score: 4.5.5 - could not convert string to float: '4.5.5'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 4/5w - could not convert string to float: '5w'\n",
      "Error processing score: 4/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 21/2/4 - too many values to unpack (expected 2)\n",
      "Error processing score: 0/0 - max_value cannot be zero\n",
      "Error processing score: 3.5.5 - could not convert string to float: '3.5.5'\n",
      "Error processing score: 3.5./4 - could not convert string to float: '3.5.'\n",
      "Error processing score: ***1/2 - could not convert string to float: '***1'\n",
      "Error processing score: 2/ - could not convert string to float: ''\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 4/0 - max_value cannot be zero\n",
      "Error processing score: 2 1/2 stars - could not convert string to float: '2 1'\n",
      "Error processing score: 3.5.4.0 - could not convert string to float: '3.5.4.0'\n",
      "Error processing score: 2.5/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 4 1/2 out of 5 stars - could not convert string to float: '4 1'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 2 1/2 - could not convert string to float: '2 1'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 4..5/5 - could not convert string to float: '4..5'\n",
      "Error processing score: 3 1/2 stars - could not convert string to float: '3 1'\n",
      "Error processing score: 3/5 stars - could not convert string to float: '5 stars'\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 2. 5 / 5 - could not convert string to float: '2. 5 '\n",
      "Error processing score: 1/2/5 - too many values to unpack (expected 2)\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Apply the normalization\n",
    "reviews['normalizedScore'] = reviews['originalScore'].apply(normalize_score)\n",
    "\n",
    "# Remove NaN values\n",
    "reviews_cleaned = reviews.dropna(subset=['normalizedScore'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = reviews_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>reviewId</th>\n",
       "      <th>creationDate</th>\n",
       "      <th>criticName</th>\n",
       "      <th>isTopCritic</th>\n",
       "      <th>originalScore</th>\n",
       "      <th>reviewState</th>\n",
       "      <th>publicatioName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>scoreSentiment</th>\n",
       "      <th>reviewUrl</th>\n",
       "      <th>normalizedScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beavers</td>\n",
       "      <td>1145982</td>\n",
       "      <td>2003-05-23</td>\n",
       "      <td>Ivan M. Lincoln</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Deseret News (Salt Lake City)</td>\n",
       "      <td>Timed to be just long enough for most youngste...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.deseretnews.com/article/700003233/B...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blood_mask</td>\n",
       "      <td>1636744</td>\n",
       "      <td>2007-06-02</td>\n",
       "      <td>The Foywonder</td>\n",
       "      <td>False</td>\n",
       "      <td>1/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Dread Central</td>\n",
       "      <td>It doesn't matter if a movie costs 300 million...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>http://www.dreadcentral.com/index.php?name=Rev...</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city_hunter_shinjuku_private_eyes</td>\n",
       "      <td>2558908</td>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>Matt Schley</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Japan Times</td>\n",
       "      <td>The film's out-of-touch attempts at humor may ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.japantimes.co.jp/culture/2019/02/0...</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2299284</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>Eric Melin</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Lawrence.com</td>\n",
       "      <td>With every new minute, there's another head-sc...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.lawrence.com/weblogs/scenestealers/...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2295858</td>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>Matt Donato</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>We Got This Covered</td>\n",
       "      <td>Emotionless reaction shots, zero characterizat...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://wegotthiscovered.com/movies/dangerous-m...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                  id  reviewId creationDate       criticName  \\\n",
       "0                            beavers   1145982   2003-05-23  Ivan M. Lincoln   \n",
       "1                         blood_mask   1636744   2007-06-02    The Foywonder   \n",
       "3  city_hunter_shinjuku_private_eyes   2558908   2019-02-14      Matt Schley   \n",
       "5                 dangerous_men_2015   2299284   2015-12-13       Eric Melin   \n",
       "6                 dangerous_men_2015   2295858   2015-11-22      Matt Donato   \n",
       "\n",
       "   isTopCritic originalScore reviewState                 publicatioName  \\\n",
       "0        False         3.5/4       fresh  Deseret News (Salt Lake City)   \n",
       "1        False           1/5      rotten                  Dread Central   \n",
       "3        False         2.5/5      rotten                    Japan Times   \n",
       "5        False           4/5       fresh                   Lawrence.com   \n",
       "6        False          7/10       fresh            We Got This Covered   \n",
       "\n",
       "                                          reviewText scoreSentiment  \\\n",
       "0  Timed to be just long enough for most youngste...       POSITIVE   \n",
       "1  It doesn't matter if a movie costs 300 million...       NEGATIVE   \n",
       "3  The film's out-of-touch attempts at humor may ...       NEGATIVE   \n",
       "5  With every new minute, there's another head-sc...       POSITIVE   \n",
       "6  Emotionless reaction shots, zero characterizat...       POSITIVE   \n",
       "\n",
       "                                           reviewUrl  normalizedScore  \n",
       "0  http://www.deseretnews.com/article/700003233/B...             8.75  \n",
       "1  http://www.dreadcentral.com/index.php?name=Rev...             2.00  \n",
       "3  https://www.japantimes.co.jp/culture/2019/02/0...             5.00  \n",
       "5  http://www.lawrence.com/weblogs/scenestealers/...             8.00  \n",
       "6  http://wegotthiscovered.com/movies/dangerous-m...             7.00  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1004642, 12)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                           0\n",
      "title                      367\n",
      "audienceScore            70010\n",
      "tomatoMeter             109381\n",
      "rating                  129267\n",
      "ratingContents          129267\n",
      "releaseDateTheaters     112485\n",
      "releaseDateStreaming     63838\n",
      "runtimeMinutes           13827\n",
      "genre                    11083\n",
      "originalLanguage         13858\n",
      "director                  4217\n",
      "writer                   53142\n",
      "boxOffice               128515\n",
      "distributor             120257\n",
      "soundMix                127341\n",
      "dtype: int64\n",
      "id                      0\n",
      "reviewId                0\n",
      "creationDate            0\n",
      "criticName              0\n",
      "isTopCritic             0\n",
      "originalScore           0\n",
      "reviewState             0\n",
      "publicatioName          0\n",
      "reviewText          68619\n",
      "scoreSentiment          0\n",
      "reviewUrl          163070\n",
      "normalizedScore         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Check for missing values\n",
    "print(movies.isnull().sum())\n",
    "print(reviews.isnull().sum())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                           0\n",
      "title                      367\n",
      "audienceScore            70010\n",
      "tomatoMeter             109381\n",
      "releaseDateTheaters     112485\n",
      "releaseDateStreaming     63838\n",
      "runtimeMinutes           13827\n",
      "genre                    11083\n",
      "originalLanguage         13858\n",
      "director                  4217\n",
      "writer                   53142\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "movies.drop(columns=['rating', 'ratingContents', 'boxOffice', 'distributor', 'soundMix'], inplace=True)\n",
    "print(movies.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "movies['title'].fillna('Unknown', inplace=True)\n",
    "movies['audienceScore'].fillna(movies['audienceScore'].median(), inplace=True)\n",
    "movies['tomatoMeter'].fillna(movies['tomatoMeter'].median(), inplace=True)\n",
    "movies['releaseDateTheaters'].fillna(movies['releaseDateTheaters'].mode()[0], inplace=True)\n",
    "movies['releaseDateStreaming'].fillna(movies['releaseDateStreaming'].mode()[0], inplace=True)\n",
    "movies['runtimeMinutes'].fillna(movies['runtimeMinutes'].median(), inplace=True)\n",
    "movies['genre'].fillna('Unknown', inplace=True)\n",
    "movies['originalLanguage'].fillna('Unknown', inplace=True)\n",
    "movies['director'].fillna('Unknown', inplace=True)\n",
    "movies['writer'].fillna('Unknown', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                      0\n",
      "title                   0\n",
      "audienceScore           0\n",
      "tomatoMeter             0\n",
      "releaseDateTheaters     0\n",
      "releaseDateStreaming    0\n",
      "runtimeMinutes          0\n",
      "genre                   0\n",
      "originalLanguage        0\n",
      "director                0\n",
      "writer                  0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(movies.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>title</th>\n",
       "      <th>audienceScore</th>\n",
       "      <th>tomatoMeter</th>\n",
       "      <th>releaseDateTheaters</th>\n",
       "      <th>releaseDateStreaming</th>\n",
       "      <th>runtimeMinutes</th>\n",
       "      <th>genre</th>\n",
       "      <th>originalLanguage</th>\n",
       "      <th>director</th>\n",
       "      <th>writer</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>space-zombie-bingo</td>\n",
       "      <td>Space Zombie Bingo!</td>\n",
       "      <td>50.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2018-08-25</td>\n",
       "      <td>75.0</td>\n",
       "      <td>Comedy, Horror, Sci-fi</td>\n",
       "      <td>English</td>\n",
       "      <td>George Ormrod</td>\n",
       "      <td>George Ormrod,John Sabotta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>the_green_grass</td>\n",
       "      <td>The Green Grass</td>\n",
       "      <td>57.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2020-02-11</td>\n",
       "      <td>114.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>Tiffany Edwards</td>\n",
       "      <td>Tiffany Edwards</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>love_lies</td>\n",
       "      <td>Love, Lies</td>\n",
       "      <td>43.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>120.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>Korean</td>\n",
       "      <td>Park Heung-Sik,Heung-Sik Park</td>\n",
       "      <td>Ha Young-Joon,Jeon Yun-su,Song Hye-jin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>the_sore_losers_1997</td>\n",
       "      <td>Sore Losers</td>\n",
       "      <td>60.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2020-10-23</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Action, Mystery &amp; thriller</td>\n",
       "      <td>English</td>\n",
       "      <td>John Michael McCarthy</td>\n",
       "      <td>John Michael McCarthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dinosaur_island_2002</td>\n",
       "      <td>Dinosaur Island</td>\n",
       "      <td>70.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2017-03-27</td>\n",
       "      <td>80.0</td>\n",
       "      <td>Fantasy, Adventure, Animation</td>\n",
       "      <td>English</td>\n",
       "      <td>Will Meugniot</td>\n",
       "      <td>John Loy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143253</th>\n",
       "      <td>nadia_the_secret_of_blue_water_the_motion_pict...</td>\n",
       "      <td>Nadia: The Secret of Blue Water: The Motion Pi...</td>\n",
       "      <td>14.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2002-08-27</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Action, Adventure, Anime</td>\n",
       "      <td>Japanese</td>\n",
       "      <td>Sho Aono</td>\n",
       "      <td>Kaoru Umeno</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143254</th>\n",
       "      <td>everyone_i_knew_and_loved</td>\n",
       "      <td>Everyone I Knew and Loved</td>\n",
       "      <td>57.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>99.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>Andrew Behringer</td>\n",
       "      <td>Erika Heidewald</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143255</th>\n",
       "      <td>the-human-body</td>\n",
       "      <td>The Human Body</td>\n",
       "      <td>71.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>43.0</td>\n",
       "      <td>Documentary</td>\n",
       "      <td>English</td>\n",
       "      <td>Peter Georgi</td>\n",
       "      <td>Richard Dale</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143256</th>\n",
       "      <td>flying_fists</td>\n",
       "      <td>Flying Fists</td>\n",
       "      <td>57.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2006-11-21</td>\n",
       "      <td>63.0</td>\n",
       "      <td>Drama</td>\n",
       "      <td>English</td>\n",
       "      <td>Robert F. Hill</td>\n",
       "      <td>Robert F. Hill,Basil Dickey</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143257</th>\n",
       "      <td>awakening-2005</td>\n",
       "      <td>Awakening</td>\n",
       "      <td>57.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>2018-09-14</td>\n",
       "      <td>2017-05-22</td>\n",
       "      <td>90.0</td>\n",
       "      <td>Horror</td>\n",
       "      <td>English</td>\n",
       "      <td>David J. Francis</td>\n",
       "      <td>David J. Francis,Mike Masters</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>143258 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                       id  \\\n",
       "0                                      space-zombie-bingo   \n",
       "1                                         the_green_grass   \n",
       "2                                               love_lies   \n",
       "3                                    the_sore_losers_1997   \n",
       "4                                    dinosaur_island_2002   \n",
       "...                                                   ...   \n",
       "143253  nadia_the_secret_of_blue_water_the_motion_pict...   \n",
       "143254                          everyone_i_knew_and_loved   \n",
       "143255                                     the-human-body   \n",
       "143256                                       flying_fists   \n",
       "143257                                     awakening-2005   \n",
       "\n",
       "                                                    title  audienceScore  \\\n",
       "0                                     Space Zombie Bingo!           50.0   \n",
       "1                                         The Green Grass           57.0   \n",
       "2                                              Love, Lies           43.0   \n",
       "3                                             Sore Losers           60.0   \n",
       "4                                         Dinosaur Island           70.0   \n",
       "...                                                   ...            ...   \n",
       "143253  Nadia: The Secret of Blue Water: The Motion Pi...           14.0   \n",
       "143254                          Everyone I Knew and Loved           57.0   \n",
       "143255                                     The Human Body           71.0   \n",
       "143256                                       Flying Fists           57.0   \n",
       "143257                                          Awakening           57.0   \n",
       "\n",
       "        tomatoMeter releaseDateTheaters releaseDateStreaming  runtimeMinutes  \\\n",
       "0              73.0          2018-09-14           2018-08-25            75.0   \n",
       "1              73.0          2018-09-14           2020-02-11           114.0   \n",
       "2              73.0          2018-09-14           2017-05-22           120.0   \n",
       "3              73.0          2018-09-14           2020-10-23            90.0   \n",
       "4              73.0          2018-09-14           2017-03-27            80.0   \n",
       "...             ...                 ...                  ...             ...   \n",
       "143253         73.0          2002-08-27           2017-05-22            90.0   \n",
       "143254         73.0          2018-09-14           2017-05-22            99.0   \n",
       "143255         89.0          2018-09-14           2017-05-22            43.0   \n",
       "143256         73.0          2018-09-14           2006-11-21            63.0   \n",
       "143257         73.0          2018-09-14           2017-05-22            90.0   \n",
       "\n",
       "                                genre originalLanguage  \\\n",
       "0              Comedy, Horror, Sci-fi          English   \n",
       "1                               Drama          English   \n",
       "2                               Drama           Korean   \n",
       "3          Action, Mystery & thriller          English   \n",
       "4       Fantasy, Adventure, Animation          English   \n",
       "...                               ...              ...   \n",
       "143253       Action, Adventure, Anime         Japanese   \n",
       "143254                          Drama          English   \n",
       "143255                    Documentary          English   \n",
       "143256                          Drama          English   \n",
       "143257                         Horror          English   \n",
       "\n",
       "                             director                                  writer  \n",
       "0                       George Ormrod              George Ormrod,John Sabotta  \n",
       "1                     Tiffany Edwards                         Tiffany Edwards  \n",
       "2       Park Heung-Sik,Heung-Sik Park  Ha Young-Joon,Jeon Yun-su,Song Hye-jin  \n",
       "3               John Michael McCarthy                   John Michael McCarthy  \n",
       "4                       Will Meugniot                                John Loy  \n",
       "...                               ...                                     ...  \n",
       "143253                       Sho Aono                             Kaoru Umeno  \n",
       "143254               Andrew Behringer                         Erika Heidewald  \n",
       "143255                   Peter Georgi                            Richard Dale  \n",
       "143256                 Robert F. Hill             Robert F. Hill,Basil Dickey  \n",
       "143257               David J. Francis           David J. Francis,Mike Masters  \n",
       "\n",
       "[143258 rows x 11 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>reviewId</th>\n",
       "      <th>creationDate</th>\n",
       "      <th>criticName</th>\n",
       "      <th>isTopCritic</th>\n",
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       "      <th>normalizedScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beavers</td>\n",
       "      <td>1145982</td>\n",
       "      <td>2003-05-23</td>\n",
       "      <td>Ivan M. Lincoln</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Deseret News (Salt Lake City)</td>\n",
       "      <td>Timed to be just long enough for most youngste...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.deseretnews.com/article/700003233/B...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blood_mask</td>\n",
       "      <td>1636744</td>\n",
       "      <td>2007-06-02</td>\n",
       "      <td>The Foywonder</td>\n",
       "      <td>False</td>\n",
       "      <td>1/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Dread Central</td>\n",
       "      <td>It doesn't matter if a movie costs 300 million...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>http://www.dreadcentral.com/index.php?name=Rev...</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city_hunter_shinjuku_private_eyes</td>\n",
       "      <td>2558908</td>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>Matt Schley</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Japan Times</td>\n",
       "      <td>The film's out-of-touch attempts at humor may ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.japantimes.co.jp/culture/2019/02/0...</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2299284</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>Eric Melin</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Lawrence.com</td>\n",
       "      <td>With every new minute, there's another head-sc...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.lawrence.com/weblogs/scenestealers/...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2295858</td>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>Matt Donato</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>We Got This Covered</td>\n",
       "      <td>Emotionless reaction shots, zero characterizat...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://wegotthiscovered.com/movies/dangerous-m...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>1444958</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706151</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Christie Cronan</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Raising Whasians</td>\n",
       "      <td>Solid but not totally sold&amp;#44; Thor&amp;#58; Ragn...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://raisingwhasians.com/thor-love-and-thun...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444959</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706150</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Ian Sandwell</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Digital Spy</td>\n",
       "      <td>Thor&amp;#58; Love and Thunder is the most enterta...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.digitalspy.com/movies/a40496050/th...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444960</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706149</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Lauren LaMagna</td>\n",
       "      <td>False</td>\n",
       "      <td>8/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Next Best Picture</td>\n",
       "      <td>&amp;quot;Thor&amp;#58; Love and Thunder&amp;quot; is a st...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.nextbestpicture.com/thor-love-and-...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444961</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706148</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Jake Cole</td>\n",
       "      <td>True</td>\n",
       "      <td>1/4</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Slant Magazine</td>\n",
       "      <td>Across Taika Waititi&amp;#8217;s film&amp;#44; a war a...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.slantmagazine.com/film/thor-love-a...</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444962</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706147</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Roger Moore</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Movie Nation</td>\n",
       "      <td>It&amp;#8217;s the jokes that make it&amp;#44; with th...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://rogersmovienation.com/2022/07/05/movie...</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004642 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id   reviewId creationDate  \\\n",
       "0                                  beavers    1145982   2003-05-23   \n",
       "1                               blood_mask    1636744   2007-06-02   \n",
       "3        city_hunter_shinjuku_private_eyes    2558908   2019-02-14   \n",
       "5                       dangerous_men_2015    2299284   2015-12-13   \n",
       "6                       dangerous_men_2015    2295858   2015-11-22   \n",
       "...                                    ...        ...          ...   \n",
       "1444958              thor_love_and_thunder  102706151   2022-07-05   \n",
       "1444959              thor_love_and_thunder  102706150   2022-07-05   \n",
       "1444960              thor_love_and_thunder  102706149   2022-07-05   \n",
       "1444961              thor_love_and_thunder  102706148   2022-07-05   \n",
       "1444962              thor_love_and_thunder  102706147   2022-07-05   \n",
       "\n",
       "              criticName  isTopCritic originalScore reviewState  \\\n",
       "0        Ivan M. Lincoln        False         3.5/4       fresh   \n",
       "1          The Foywonder        False           1/5      rotten   \n",
       "3            Matt Schley        False         2.5/5      rotten   \n",
       "5             Eric Melin        False           4/5       fresh   \n",
       "6            Matt Donato        False          7/10       fresh   \n",
       "...                  ...          ...           ...         ...   \n",
       "1444958  Christie Cronan        False          7/10       fresh   \n",
       "1444959     Ian Sandwell        False           4/5       fresh   \n",
       "1444960   Lauren LaMagna        False          8/10       fresh   \n",
       "1444961        Jake Cole         True           1/4      rotten   \n",
       "1444962      Roger Moore        False         2.5/4       fresh   \n",
       "\n",
       "                        publicatioName  \\\n",
       "0        Deseret News (Salt Lake City)   \n",
       "1                        Dread Central   \n",
       "3                          Japan Times   \n",
       "5                         Lawrence.com   \n",
       "6                  We Got This Covered   \n",
       "...                                ...   \n",
       "1444958               Raising Whasians   \n",
       "1444959                    Digital Spy   \n",
       "1444960              Next Best Picture   \n",
       "1444961                 Slant Magazine   \n",
       "1444962                   Movie Nation   \n",
       "\n",
       "                                                reviewText scoreSentiment  \\\n",
       "0        Timed to be just long enough for most youngste...       POSITIVE   \n",
       "1        It doesn't matter if a movie costs 300 million...       NEGATIVE   \n",
       "3        The film's out-of-touch attempts at humor may ...       NEGATIVE   \n",
       "5        With every new minute, there's another head-sc...       POSITIVE   \n",
       "6        Emotionless reaction shots, zero characterizat...       POSITIVE   \n",
       "...                                                    ...            ...   \n",
       "1444958  Solid but not totally sold&#44; Thor&#58; Ragn...       POSITIVE   \n",
       "1444959  Thor&#58; Love and Thunder is the most enterta...       POSITIVE   \n",
       "1444960  &quot;Thor&#58; Love and Thunder&quot; is a st...       POSITIVE   \n",
       "1444961  Across Taika Waititi&#8217;s film&#44; a war a...       NEGATIVE   \n",
       "1444962  It&#8217;s the jokes that make it&#44; with th...       POSITIVE   \n",
       "\n",
       "                                                 reviewUrl  normalizedScore  \n",
       "0        http://www.deseretnews.com/article/700003233/B...             8.75  \n",
       "1        http://www.dreadcentral.com/index.php?name=Rev...             2.00  \n",
       "3        https://www.japantimes.co.jp/culture/2019/02/0...             5.00  \n",
       "5        http://www.lawrence.com/weblogs/scenestealers/...             8.00  \n",
       "6        http://wegotthiscovered.com/movies/dangerous-m...             7.00  \n",
       "...                                                    ...              ...  \n",
       "1444958  https://raisingwhasians.com/thor-love-and-thun...             7.00  \n",
       "1444959  https://www.digitalspy.com/movies/a40496050/th...             8.00  \n",
       "1444960  https://www.nextbestpicture.com/thor-love-and-...             8.00  \n",
       "1444961  https://www.slantmagazine.com/film/thor-love-a...             2.50  \n",
       "1444962  https://rogersmovienation.com/2022/07/05/movie...             6.25  \n",
       "\n",
       "[1004642 rows x 12 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# reviews.drop(columns=[ 'reviewUrl'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                      0\n",
      "reviewId                0\n",
      "creationDate            0\n",
      "criticName              0\n",
      "isTopCritic             0\n",
      "originalScore           0\n",
      "reviewState             0\n",
      "publicatioName          0\n",
      "reviewText          68619\n",
      "scoreSentiment          0\n",
      "reviewUrl          163070\n",
      "normalizedScore         0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(reviews.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishn\\AppData\\Local\\Temp\\ipykernel_48804\\3770022209.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  reviews['creationDate'] = pd.to_datetime(reviews['creationDate'])\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Convert dates with multiple formats and coerce errors\n",
    "movies['releaseDateTheaters'] = pd.to_datetime(movies['releaseDateTheaters'], errors='coerce')\n",
    "movies['releaseDateStreaming'] = pd.to_datetime(movies['releaseDateStreaming'], errors='coerce')\n",
    "\n",
    "\n",
    "\n",
    "# Convert dates to datetime\n",
    "movies['releaseDateTheaters'] = pd.to_datetime(movies['releaseDateTheaters'])\n",
    "movies['releaseDateStreaming'] = pd.to_datetime(movies['releaseDateStreaming'])\n",
    "reviews['creationDate'] = pd.to_datetime(reviews['creationDate'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       audienceScore    tomatoMeter            releaseDateTheaters  \\\n",
      "count  143258.000000  143258.000000                         143258   \n",
      "mean       56.322509      71.290364  2013-09-15 05:31:25.666420224   \n",
      "min         0.000000       0.000000            1902-10-04 00:00:00   \n",
      "25%        57.000000      73.000000            2018-09-14 00:00:00   \n",
      "50%        57.000000      73.000000            2018-09-14 00:00:00   \n",
      "75%        58.000000      73.000000            2018-09-14 00:00:00   \n",
      "max       100.000000     100.000000            2032-04-21 00:00:00   \n",
      "std        17.569599      13.969153                            NaN   \n",
      "\n",
      "                releaseDateStreaming  runtimeMinutes  \n",
      "count                         143257   143258.000000  \n",
      "mean   2015-10-11 14:21:50.813433088       93.543669  \n",
      "min              1928-12-18 00:00:00        1.000000  \n",
      "25%              2016-05-23 00:00:00       85.000000  \n",
      "50%              2017-05-22 00:00:00       92.000000  \n",
      "75%              2017-05-22 00:00:00      101.000000  \n",
      "max              2023-12-22 00:00:00     2700.000000  \n",
      "std                              NaN       26.741998  \n",
      "           reviewId                   creationDate  normalizedScore\n",
      "count  1.004642e+06                        1004642     1.004642e+06\n",
      "mean   8.258072e+06  2012-08-04 05:41:39.404962304     3.937573e+01\n",
      "min    1.000000e+00            1800-01-01 00:00:00    -2.500000e+00\n",
      "25%    1.524789e+06            2006-07-03 00:00:00     5.000000e+00\n",
      "50%    2.121730e+06            2013-01-10 00:00:00     6.250000e+00\n",
      "75%    2.532462e+06            2018-11-17 00:00:00     8.000000e+00\n",
      "max    1.027962e+08            2023-04-08 00:00:00     3.333334e+07\n",
      "std    2.449186e+07                            NaN     3.325623e+04\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# Descriptive statistics\n",
    "print(movies.describe())\n",
    "print(reviews.describe())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\vishn\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\vishn\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Visualization of distributions\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(movies['audienceScore'].dropna(), bins=20, kde=True)\n",
    "plt.title('Distribution of Audience Scores')\n",
    "plt.show()\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.histplot(movies['tomatoMeter'].dropna(), bins=20, kde=True)\n",
    "plt.title('Distribution of Tomatometer Scores')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plotting the distribution of score sentiment\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "# Histogram of score sentiment\n",
    "sns.countplot(x='scoreSentiment', data=reviews, palette='viridis')\n",
    "plt.title('Distribution of Score Sentiment')\n",
    "plt.xlabel('Score Sentiment')\n",
    "plt.ylabel('Count')\n",
    "\n",
    "plt.xticks(rotation=45)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each sentiment\n",
    "sentiment_counts = reviews['scoreSentiment'].value_counts()\n",
    "\n",
    "# Plotting the pie chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.pie(sentiment_counts, labels=sentiment_counts.index, autopct='%1.1f%%', startangle=140, colors=sns.color_palette(\"viridis\", len(sentiment_counts)))\n",
    "\n",
    "plt.title('Distribution of Score Sentiment')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\vishn\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Frequency')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the distributions\n",
    "plt.figure(figsize=(14, 6))\n",
    "\n",
    "# Distribution of Audience Scores\n",
    "plt.subplot(1, 2, 1)\n",
    "sns.histplot(movies['audienceScore'], kde=True, bins=30, color='blue')\n",
    "plt.title('Distribution of Audience Scores')\n",
    "plt.xlabel('Audience Score')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.utils import resample\n",
    "# Separate the majority and minority classes\n",
    "sentiment_counts = reviews['scoreSentiment'].value_counts()\n",
    "majority_class = sentiment_counts.idxmax()\n",
    "minority_classes = sentiment_counts.index[sentiment_counts < sentiment_counts.max()]\n",
    "\n",
    "# Upsample minority classes\n",
    "reviews_upsampled = reviews[reviews['scoreSentiment'] == majority_class]\n",
    "for sentiment in minority_classes:\n",
    "    minority_class_data = reviews[reviews['scoreSentiment'] == sentiment]\n",
    "    minority_class_upsampled = resample(minority_class_data,\n",
    "                                        replace=True,  # sample with replacement\n",
    "                                        n_samples=sentiment_counts.max(),  # to match majority class\n",
    "                                        random_state=42)  # reproducible results\n",
    "    reviews_upsampled = pd.concat([reviews_upsampled, minority_class_upsampled])\n",
    "\n",
    "# Now reviews_upsampled has balanced class distribution\n",
    "upsampled_sentiment_counts = reviews_upsampled['scoreSentiment'].value_counts()\n",
    "\n",
    "# Plotting the pie chart\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.pie(upsampled_sentiment_counts, labels=upsampled_sentiment_counts.index, autopct='%1.1f%%', startangle=140, colors=sns.color_palette(\"viridis\", len(upsampled_sentiment_counts)))\n",
    "\n",
    "plt.title('Upsampled Distribution of Score Sentiment')\n",
    "plt.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Convert releaseDateTheaters to datetime\n",
    "movies['releaseDateTheaters'] = pd.to_datetime(movies['releaseDateTheaters'], errors='coerce')\n",
    "\n",
    "# Extract year from releaseDateTheaters\n",
    "movies['releaseYear'] = movies['releaseDateTheaters'].dt.year\n",
    "\n",
    "# Fill missing audienceScore values with the median for the purpose of this visualization\n",
    "movies['audienceScore'].fillna(movies['audienceScore'].median(), inplace=True)\n",
    "\n",
    "# Filter out movies without a release year\n",
    "movies = movies.dropna(subset=['releaseYear'])\n",
    "\n",
    "# Plotting the distribution of audience scores across the years\n",
    "plt.figure(figsize=(14, 7))\n",
    "\n",
    "# Boxplot of audience scores by year\n",
    "sns.boxplot(x='releaseYear', y='audienceScore', data=movies)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Distribution of Audience Scores Across the Years')\n",
    "plt.xlabel('Release Year')\n",
    "plt.ylabel('Audience Score')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\vishn\\anaconda3\\Lib\\site-packages\\seaborn\\_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n",
      "  with pd.option_context('mode.use_inf_as_na', True):\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Distribution of Critic Scores (Tomatometer)\n",
    "plt.subplot(1, 2, 2)\n",
    "sns.histplot(movies['tomatoMeter'], kde=True, bins=30, color='red')\n",
    "plt.title('Distribution of Critic Scores (Tomatometer)')\n",
    "plt.xlabel('Critic Score')\n",
    "plt.ylabel('Frequency')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numeric_columns = movies.select_dtypes(include=['number']).columns\n",
    "\n",
    "# Generate the correlation matrix\n",
    "correlation_matrix = movies[numeric_columns].corr()\n",
    "\n",
    "# Plot the correlation heatmap\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n",
    "plt.title('Correlation Heatmap')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# # Correlation heatmap\n",
    "# plt.figure(figsize=(10, 6))\n",
    "# sns.heatmap(movies.corr(), annot=True, cmap='coolwarm')\n",
    "# plt.title('Correlation Heatmap')\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "# Analysis of categorical features\n",
    "genre_counts = movies['genre'].value_counts().head(10)\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(x=genre_counts.index, y=genre_counts.values)\n",
    "plt.title('Top 10 Genres')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Could not interpret input 'originalScore'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[42], line 3\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Plotting boxplot for distribution of ratings across languages\u001b[39;00m\n\u001b[0;32m      2\u001b[0m plt\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m12\u001b[39m, \u001b[38;5;241m8\u001b[39m))\n\u001b[1;32m----> 3\u001b[0m sns\u001b[38;5;241m.\u001b[39mboxplot(data\u001b[38;5;241m=\u001b[39mmovies, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moriginalLanguage\u001b[39m\u001b[38;5;124m'\u001b[39m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124moriginalScore\u001b[39m\u001b[38;5;124m'\u001b[39m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mviridis\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      4\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mDistribution of Ratings Across Various Languages\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mOriginal Language\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
      "File \u001b[1;32mc:\\Users\\vishn\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:2231\u001b[0m, in \u001b[0;36mboxplot\u001b[1;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[0;32m   2224\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mboxplot\u001b[39m(\n\u001b[0;32m   2225\u001b[0m     data\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, \u001b[38;5;241m*\u001b[39m, x\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, y\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, hue\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, hue_order\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   2226\u001b[0m     orient\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, color\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, palette\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, saturation\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.75\u001b[39m, width\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m.8\u001b[39m,\n\u001b[0;32m   2227\u001b[0m     dodge\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m, fliersize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m5\u001b[39m, linewidth\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m, whis\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1.5\u001b[39m, ax\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[0;32m   2228\u001b[0m     \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs\n\u001b[0;32m   2229\u001b[0m ):\n\u001b[1;32m-> 2231\u001b[0m     plotter \u001b[38;5;241m=\u001b[39m _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0;32m   2232\u001b[0m                           orient, color, palette, saturation,\n\u001b[0;32m   2233\u001b[0m                           width, dodge, fliersize, linewidth)\n\u001b[0;32m   2235\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m   2236\u001b[0m         ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39mgca()\n",
      "File \u001b[1;32mc:\\Users\\vishn\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:785\u001b[0m, in \u001b[0;36m_BoxPlotter.__init__\u001b[1;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[0;32m    781\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__init__\u001b[39m(\u001b[38;5;28mself\u001b[39m, x, y, hue, data, order, hue_order,\n\u001b[0;32m    782\u001b[0m              orient, color, palette, saturation,\n\u001b[0;32m    783\u001b[0m              width, dodge, fliersize, linewidth):\n\u001b[1;32m--> 785\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_variables(x, y, hue, data, orient, order, hue_order)\n\u001b[0;32m    786\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mestablish_colors(color, palette, saturation)\n\u001b[0;32m    788\u001b[0m     \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdodge \u001b[38;5;241m=\u001b[39m dodge\n",
      "File \u001b[1;32mc:\\Users\\vishn\\anaconda3\\Lib\\site-packages\\seaborn\\categorical.py:541\u001b[0m, in \u001b[0;36m_CategoricalPlotter.establish_variables\u001b[1;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[0;32m    539\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(var, \u001b[38;5;28mstr\u001b[39m):\n\u001b[0;32m    540\u001b[0m         err \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCould not interpret input \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mvar\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m--> 541\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(err)\n\u001b[0;32m    543\u001b[0m \u001b[38;5;66;03m# Figure out the plotting orientation\u001b[39;00m\n\u001b[0;32m    544\u001b[0m orient \u001b[38;5;241m=\u001b[39m infer_orient(\n\u001b[0;32m    545\u001b[0m     x, y, orient, require_numeric\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrequire_numeric\n\u001b[0;32m    546\u001b[0m )\n",
      "\u001b[1;31mValueError\u001b[0m: Could not interpret input 'originalScore'"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x800 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Plotting boxplot for distribution of ratings across languages\n",
    "# plt.figure(figsize=(12, 8))\n",
    "# sns.boxplot(data=movies, x='originalLanguage', y='originalScore', palette='viridis')\n",
    "# plt.title('Distribution of Ratings Across Various Languages')\n",
    "# plt.xlabel('Original Language')\n",
    "# plt.ylabel('Original Score')\n",
    "# plt.xticks(rotation=45, ha='right')\n",
    "# plt.tight_layout()\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data for collaborative filtering using the Surprise library\n",
    "from surprise import Dataset, Reader, SVD\n",
    "from surprise.model_selection import cross_validate, train_test_split\n",
    "\n",
    "# reviews['userId'] = reviews['reviewId']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'reviewId', 'creationDate', 'criticName', 'isTopCritic',\n",
      "       'originalScore', 'reviewState', 'publicatioName', 'reviewText',\n",
      "       'scoreSentiment', 'reviewUrl', 'normalizedScore'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(reviews.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>id</th>\n",
       "      <th>reviewId</th>\n",
       "      <th>creationDate</th>\n",
       "      <th>criticName</th>\n",
       "      <th>isTopCritic</th>\n",
       "      <th>originalScore</th>\n",
       "      <th>reviewState</th>\n",
       "      <th>publicatioName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>scoreSentiment</th>\n",
       "      <th>reviewUrl</th>\n",
       "      <th>normalizedScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beavers</td>\n",
       "      <td>1145982</td>\n",
       "      <td>2003-05-23</td>\n",
       "      <td>Ivan M. Lincoln</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Deseret News (Salt Lake City)</td>\n",
       "      <td>Timed to be just long enough for most youngste...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.deseretnews.com/article/700003233/B...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blood_mask</td>\n",
       "      <td>1636744</td>\n",
       "      <td>2007-06-02</td>\n",
       "      <td>The Foywonder</td>\n",
       "      <td>False</td>\n",
       "      <td>1/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Dread Central</td>\n",
       "      <td>It doesn't matter if a movie costs 300 million...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>http://www.dreadcentral.com/index.php?name=Rev...</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city_hunter_shinjuku_private_eyes</td>\n",
       "      <td>2558908</td>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>Matt Schley</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Japan Times</td>\n",
       "      <td>The film's out-of-touch attempts at humor may ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.japantimes.co.jp/culture/2019/02/0...</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2299284</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>Eric Melin</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Lawrence.com</td>\n",
       "      <td>With every new minute, there's another head-sc...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.lawrence.com/weblogs/scenestealers/...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2295858</td>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>Matt Donato</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>We Got This Covered</td>\n",
       "      <td>Emotionless reaction shots, zero characterizat...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://wegotthiscovered.com/movies/dangerous-m...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444958</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706151</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Christie Cronan</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Raising Whasians</td>\n",
       "      <td>Solid but not totally sold&amp;#44; Thor&amp;#58; Ragn...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://raisingwhasians.com/thor-love-and-thun...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444959</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706150</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Ian Sandwell</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Digital Spy</td>\n",
       "      <td>Thor&amp;#58; Love and Thunder is the most enterta...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.digitalspy.com/movies/a40496050/th...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444960</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706149</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Lauren LaMagna</td>\n",
       "      <td>False</td>\n",
       "      <td>8/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Next Best Picture</td>\n",
       "      <td>&amp;quot;Thor&amp;#58; Love and Thunder&amp;quot; is a st...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.nextbestpicture.com/thor-love-and-...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444961</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706148</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Jake Cole</td>\n",
       "      <td>True</td>\n",
       "      <td>1/4</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Slant Magazine</td>\n",
       "      <td>Across Taika Waititi&amp;#8217;s film&amp;#44; a war a...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.slantmagazine.com/film/thor-love-a...</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444962</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706147</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Roger Moore</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Movie Nation</td>\n",
       "      <td>It&amp;#8217;s the jokes that make it&amp;#44; with th...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://rogersmovienation.com/2022/07/05/movie...</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004642 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id   reviewId creationDate  \\\n",
       "0                                  beavers    1145982   2003-05-23   \n",
       "1                               blood_mask    1636744   2007-06-02   \n",
       "3        city_hunter_shinjuku_private_eyes    2558908   2019-02-14   \n",
       "5                       dangerous_men_2015    2299284   2015-12-13   \n",
       "6                       dangerous_men_2015    2295858   2015-11-22   \n",
       "...                                    ...        ...          ...   \n",
       "1444958              thor_love_and_thunder  102706151   2022-07-05   \n",
       "1444959              thor_love_and_thunder  102706150   2022-07-05   \n",
       "1444960              thor_love_and_thunder  102706149   2022-07-05   \n",
       "1444961              thor_love_and_thunder  102706148   2022-07-05   \n",
       "1444962              thor_love_and_thunder  102706147   2022-07-05   \n",
       "\n",
       "              criticName  isTopCritic originalScore reviewState  \\\n",
       "0        Ivan M. Lincoln        False         3.5/4       fresh   \n",
       "1          The Foywonder        False           1/5      rotten   \n",
       "3            Matt Schley        False         2.5/5      rotten   \n",
       "5             Eric Melin        False           4/5       fresh   \n",
       "6            Matt Donato        False          7/10       fresh   \n",
       "...                  ...          ...           ...         ...   \n",
       "1444958  Christie Cronan        False          7/10       fresh   \n",
       "1444959     Ian Sandwell        False           4/5       fresh   \n",
       "1444960   Lauren LaMagna        False          8/10       fresh   \n",
       "1444961        Jake Cole         True           1/4      rotten   \n",
       "1444962      Roger Moore        False         2.5/4       fresh   \n",
       "\n",
       "                        publicatioName  \\\n",
       "0        Deseret News (Salt Lake City)   \n",
       "1                        Dread Central   \n",
       "3                          Japan Times   \n",
       "5                         Lawrence.com   \n",
       "6                  We Got This Covered   \n",
       "...                                ...   \n",
       "1444958               Raising Whasians   \n",
       "1444959                    Digital Spy   \n",
       "1444960              Next Best Picture   \n",
       "1444961                 Slant Magazine   \n",
       "1444962                   Movie Nation   \n",
       "\n",
       "                                                reviewText scoreSentiment  \\\n",
       "0        Timed to be just long enough for most youngste...       POSITIVE   \n",
       "1        It doesn't matter if a movie costs 300 million...       NEGATIVE   \n",
       "3        The film's out-of-touch attempts at humor may ...       NEGATIVE   \n",
       "5        With every new minute, there's another head-sc...       POSITIVE   \n",
       "6        Emotionless reaction shots, zero characterizat...       POSITIVE   \n",
       "...                                                    ...            ...   \n",
       "1444958  Solid but not totally sold&#44; Thor&#58; Ragn...       POSITIVE   \n",
       "1444959  Thor&#58; Love and Thunder is the most enterta...       POSITIVE   \n",
       "1444960  &quot;Thor&#58; Love and Thunder&quot; is a st...       POSITIVE   \n",
       "1444961  Across Taika Waititi&#8217;s film&#44; a war a...       NEGATIVE   \n",
       "1444962  It&#8217;s the jokes that make it&#44; with th...       POSITIVE   \n",
       "\n",
       "                                                 reviewUrl  normalizedScore  \n",
       "0        http://www.deseretnews.com/article/700003233/B...             8.75  \n",
       "1        http://www.dreadcentral.com/index.php?name=Rev...             2.00  \n",
       "3        https://www.japantimes.co.jp/culture/2019/02/0...             5.00  \n",
       "5        http://www.lawrence.com/weblogs/scenestealers/...             8.00  \n",
       "6        http://wegotthiscovered.com/movies/dangerous-m...             7.00  \n",
       "...                                                    ...              ...  \n",
       "1444958  https://raisingwhasians.com/thor-love-and-thun...             7.00  \n",
       "1444959  https://www.digitalspy.com/movies/a40496050/th...             8.00  \n",
       "1444960  https://www.nextbestpicture.com/thor-love-and-...             8.00  \n",
       "1444961  https://www.slantmagazine.com/film/thor-love-a...             2.50  \n",
       "1444962  https://rogersmovienation.com/2022/07/05/movie...             6.25  \n",
       "\n",
       "[1004642 rows x 12 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prepare data\n",
    "reader = Reader(rating_scale=(1, 10))\n",
    "data = Dataset.load_from_df(reviews[['reviewId', 'id', 'normalizedScore']], reader)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-test split\n",
    "trainset, testset = train_test_split(data, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Evaluating RMSE, MAE of algorithm SVD on 5 split(s).\n",
      "\n",
      "                  Fold 1  Fold 2  Fold 3  Fold 4  Fold 5  Mean    Std     \n",
      "RMSE (testset)    5.5879  4.7355  5.0391  74363.27204.5148  14876.629929743.3211\n",
      "MAE (testset)     3.7848  3.7781  3.7639  167.26483.7629  36.4709 65.3969 \n",
      "Fit time          17.40   19.93   18.09   17.85   19.36   18.53   0.95    \n",
      "Test time         0.89    0.95    0.97    0.92    0.94    0.94    0.03    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([5.58788470e+00, 4.73554162e+00, 5.03908157e+00, 7.43632720e+04,\n",
       "        4.51484009e+00]),\n",
       " 'test_mae': array([  3.78476379,   3.77808527,   3.76394781, 167.26476768,\n",
       "          3.76291566]),\n",
       " 'fit_time': (17.400973558425903,\n",
       "  19.925355672836304,\n",
       "  18.092084646224976,\n",
       "  17.852832794189453,\n",
       "  19.35742163658142),\n",
       " 'test_time': (0.8886408805847168,\n",
       "  0.9547252655029297,\n",
       "  0.9745724201202393,\n",
       "  0.9217076301574707,\n",
       "  0.9410078525543213)}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "algo = SVD()\n",
    "\n",
    "# Train the model\n",
    "algo.fit(trainset)\n",
    "\n",
    "# Evaluate the model\n",
    "cross_validate(algo, data, measures=['RMSE', 'MAE'], cv=5, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>reviewId</th>\n",
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       "      <th>criticName</th>\n",
       "      <th>isTopCritic</th>\n",
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       "      <th>reviewState</th>\n",
       "      <th>publicatioName</th>\n",
       "      <th>reviewText</th>\n",
       "      <th>scoreSentiment</th>\n",
       "      <th>reviewUrl</th>\n",
       "      <th>normalizedScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>beavers</td>\n",
       "      <td>1145982</td>\n",
       "      <td>2003-05-23</td>\n",
       "      <td>Ivan M. Lincoln</td>\n",
       "      <td>False</td>\n",
       "      <td>3.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Deseret News (Salt Lake City)</td>\n",
       "      <td>Timed to be just long enough for most youngste...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.deseretnews.com/article/700003233/B...</td>\n",
       "      <td>8.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>blood_mask</td>\n",
       "      <td>1636744</td>\n",
       "      <td>2007-06-02</td>\n",
       "      <td>The Foywonder</td>\n",
       "      <td>False</td>\n",
       "      <td>1/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Dread Central</td>\n",
       "      <td>It doesn't matter if a movie costs 300 million...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>http://www.dreadcentral.com/index.php?name=Rev...</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>city_hunter_shinjuku_private_eyes</td>\n",
       "      <td>2558908</td>\n",
       "      <td>2019-02-14</td>\n",
       "      <td>Matt Schley</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/5</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Japan Times</td>\n",
       "      <td>The film's out-of-touch attempts at humor may ...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.japantimes.co.jp/culture/2019/02/0...</td>\n",
       "      <td>5.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2299284</td>\n",
       "      <td>2015-12-13</td>\n",
       "      <td>Eric Melin</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Lawrence.com</td>\n",
       "      <td>With every new minute, there's another head-sc...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://www.lawrence.com/weblogs/scenestealers/...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>dangerous_men_2015</td>\n",
       "      <td>2295858</td>\n",
       "      <td>2015-11-22</td>\n",
       "      <td>Matt Donato</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>We Got This Covered</td>\n",
       "      <td>Emotionless reaction shots, zero characterizat...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>http://wegotthiscovered.com/movies/dangerous-m...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444958</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706151</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Christie Cronan</td>\n",
       "      <td>False</td>\n",
       "      <td>7/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Raising Whasians</td>\n",
       "      <td>Solid but not totally sold&amp;#44; Thor&amp;#58; Ragn...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://raisingwhasians.com/thor-love-and-thun...</td>\n",
       "      <td>7.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444959</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706150</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Ian Sandwell</td>\n",
       "      <td>False</td>\n",
       "      <td>4/5</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Digital Spy</td>\n",
       "      <td>Thor&amp;#58; Love and Thunder is the most enterta...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.digitalspy.com/movies/a40496050/th...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444960</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706149</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Lauren LaMagna</td>\n",
       "      <td>False</td>\n",
       "      <td>8/10</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Next Best Picture</td>\n",
       "      <td>&amp;quot;Thor&amp;#58; Love and Thunder&amp;quot; is a st...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://www.nextbestpicture.com/thor-love-and-...</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444961</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706148</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Jake Cole</td>\n",
       "      <td>True</td>\n",
       "      <td>1/4</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Slant Magazine</td>\n",
       "      <td>Across Taika Waititi&amp;#8217;s film&amp;#44; a war a...</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>https://www.slantmagazine.com/film/thor-love-a...</td>\n",
       "      <td>2.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1444962</th>\n",
       "      <td>thor_love_and_thunder</td>\n",
       "      <td>102706147</td>\n",
       "      <td>2022-07-05</td>\n",
       "      <td>Roger Moore</td>\n",
       "      <td>False</td>\n",
       "      <td>2.5/4</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Movie Nation</td>\n",
       "      <td>It&amp;#8217;s the jokes that make it&amp;#44; with th...</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>https://rogersmovienation.com/2022/07/05/movie...</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004642 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        id   reviewId creationDate  \\\n",
       "0                                  beavers    1145982   2003-05-23   \n",
       "1                               blood_mask    1636744   2007-06-02   \n",
       "3        city_hunter_shinjuku_private_eyes    2558908   2019-02-14   \n",
       "5                       dangerous_men_2015    2299284   2015-12-13   \n",
       "6                       dangerous_men_2015    2295858   2015-11-22   \n",
       "...                                    ...        ...          ...   \n",
       "1444958              thor_love_and_thunder  102706151   2022-07-05   \n",
       "1444959              thor_love_and_thunder  102706150   2022-07-05   \n",
       "1444960              thor_love_and_thunder  102706149   2022-07-05   \n",
       "1444961              thor_love_and_thunder  102706148   2022-07-05   \n",
       "1444962              thor_love_and_thunder  102706147   2022-07-05   \n",
       "\n",
       "              criticName  isTopCritic originalScore reviewState  \\\n",
       "0        Ivan M. Lincoln        False         3.5/4       fresh   \n",
       "1          The Foywonder        False           1/5      rotten   \n",
       "3            Matt Schley        False         2.5/5      rotten   \n",
       "5             Eric Melin        False           4/5       fresh   \n",
       "6            Matt Donato        False          7/10       fresh   \n",
       "...                  ...          ...           ...         ...   \n",
       "1444958  Christie Cronan        False          7/10       fresh   \n",
       "1444959     Ian Sandwell        False           4/5       fresh   \n",
       "1444960   Lauren LaMagna        False          8/10       fresh   \n",
       "1444961        Jake Cole         True           1/4      rotten   \n",
       "1444962      Roger Moore        False         2.5/4       fresh   \n",
       "\n",
       "                        publicatioName  \\\n",
       "0        Deseret News (Salt Lake City)   \n",
       "1                        Dread Central   \n",
       "3                          Japan Times   \n",
       "5                         Lawrence.com   \n",
       "6                  We Got This Covered   \n",
       "...                                ...   \n",
       "1444958               Raising Whasians   \n",
       "1444959                    Digital Spy   \n",
       "1444960              Next Best Picture   \n",
       "1444961                 Slant Magazine   \n",
       "1444962                   Movie Nation   \n",
       "\n",
       "                                                reviewText scoreSentiment  \\\n",
       "0        Timed to be just long enough for most youngste...       POSITIVE   \n",
       "1        It doesn't matter if a movie costs 300 million...       NEGATIVE   \n",
       "3        The film's out-of-touch attempts at humor may ...       NEGATIVE   \n",
       "5        With every new minute, there's another head-sc...       POSITIVE   \n",
       "6        Emotionless reaction shots, zero characterizat...       POSITIVE   \n",
       "...                                                    ...            ...   \n",
       "1444958  Solid but not totally sold&#44; Thor&#58; Ragn...       POSITIVE   \n",
       "1444959  Thor&#58; Love and Thunder is the most enterta...       POSITIVE   \n",
       "1444960  &quot;Thor&#58; Love and Thunder&quot; is a st...       POSITIVE   \n",
       "1444961  Across Taika Waititi&#8217;s film&#44; a war a...       NEGATIVE   \n",
       "1444962  It&#8217;s the jokes that make it&#44; with th...       POSITIVE   \n",
       "\n",
       "                                                 reviewUrl  normalizedScore  \n",
       "0        http://www.deseretnews.com/article/700003233/B...             8.75  \n",
       "1        http://www.dreadcentral.com/index.php?name=Rev...             2.00  \n",
       "3        https://www.japantimes.co.jp/culture/2019/02/0...             5.00  \n",
       "5        http://www.lawrence.com/weblogs/scenestealers/...             8.00  \n",
       "6        http://wegotthiscovered.com/movies/dangerous-m...             7.00  \n",
       "...                                                    ...              ...  \n",
       "1444958  https://raisingwhasians.com/thor-love-and-thun...             7.00  \n",
       "1444959  https://www.digitalspy.com/movies/a40496050/th...             8.00  \n",
       "1444960  https://www.nextbestpicture.com/thor-love-and-...             8.00  \n",
       "1444961  https://www.slantmagazine.com/film/thor-love-a...             2.50  \n",
       "1444962  https://rogersmovienation.com/2022/07/05/movie...             6.25  \n",
       "\n",
       "[1004642 rows x 12 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie ID: space-zombie-bingo, Estimated Rating: 10\n",
      "Movie ID: the_green_grass, Estimated Rating: 10\n",
      "Movie ID: love_lies, Estimated Rating: 10\n",
      "Movie ID: the_sore_losers_1997, Estimated Rating: 10\n",
      "Movie ID: dinosaur_island_2002, Estimated Rating: 10\n",
      "Movie ID: adrift_2018, Estimated Rating: 10\n",
      "Movie ID: malta-con-huevo-scrambled-beer, Estimated Rating: 10\n",
      "Movie ID: kakabakaba-ka-ba, Estimated Rating: 10\n",
      "Movie ID: sundowning, Estimated Rating: 10\n",
      "Movie ID: 1035316-born_to_kill, Estimated Rating: 10\n",
      "Movie ID: 1015331-number_one_with_a_bullet, Estimated Rating: 10\n",
      "Movie ID: garden_murder_case, Estimated Rating: 10\n",
      "Movie ID: margarita_happy_hour, Estimated Rating: 10\n",
      "Movie ID: leap_of_faith_2019, Estimated Rating: 10\n",
      "Movie ID: shake-rattle-roll-3, Estimated Rating: 10\n",
      "Movie ID: daag_1973, Estimated Rating: 10\n",
      "Movie ID: crown_vs_stevens_1936, Estimated Rating: 10\n",
      "Movie ID: 1221483-paa, Estimated Rating: 10\n",
      "Movie ID: f9ab9165-598c-3ac9-bc3e-a5fb2ac517e5, Estimated Rating: 10\n",
      "Movie ID: the_last_gentleman, Estimated Rating: 10\n",
      "Movie ID: sarah_palin_you_betcha, Estimated Rating: 10\n",
      "Movie ID: 1005521-death_of_a_salesman, Estimated Rating: 10\n",
      "Movie ID: escape_from_alcatraz, Estimated Rating: 10\n",
      "Movie ID: sheffey, Estimated Rating: 10\n",
      "Movie ID: wrong_guy, Estimated Rating: 10\n",
      "Movie ID: cold_room, Estimated Rating: 10\n",
      "Movie ID: s_diary, Estimated Rating: 10\n",
      "Movie ID: deck_the_halls_2011, Estimated Rating: 10\n",
      "Movie ID: alraune, Estimated Rating: 10\n",
      "Movie ID: danger_tomorrow, Estimated Rating: 10\n",
      "Movie ID: angry_rice_wives, Estimated Rating: 10\n",
      "Movie ID: a_state_of_mind_2005, Estimated Rating: 10\n",
      "Movie ID: mugavaree, Estimated Rating: 10\n",
      "Movie ID: the_moth, Estimated Rating: 10\n",
      "Movie ID: standardized_lies_money_and_civil_rights_how_testing_is_ruining_public_education, Estimated Rating: 10\n",
      "Movie ID: outpatient, Estimated Rating: 10\n",
      "Movie ID: cupid, Estimated Rating: 10\n",
      "Movie ID: the_in_laws_1979, Estimated Rating: 10\n",
      "Movie ID: isteve, Estimated Rating: 10\n",
      "Movie ID: campfire_christmas, Estimated Rating: 10\n",
      "Movie ID: gimme_the_power_2012, Estimated Rating: 10\n",
      "Movie ID: disciple_of_death, Estimated Rating: 10\n",
      "Movie ID: de_lhuile_sur_le_feu, Estimated Rating: 10\n",
      "Movie ID: snuff_bottle_connection, Estimated Rating: 10\n",
      "Movie ID: tripping_with_nils_frahm, Estimated Rating: 10\n",
      "Movie ID: haunted_3d, Estimated Rating: 10\n",
      "Movie ID: the_walls_of_the_moon, Estimated Rating: 10\n",
      "Movie ID: beg1994, Estimated Rating: 10\n",
      "Movie ID: top_of_the_heap, Estimated Rating: 10\n",
      "Movie ID: gun_shy_2017, Estimated Rating: 10\n",
      "Movie ID: island_of_love, Estimated Rating: 10\n",
      "Movie ID: sunset_strip-1991, Estimated Rating: 10\n",
      "Movie ID: uncle_tom, Estimated Rating: 10\n",
      "Movie ID: the_awakening, Estimated Rating: 10\n",
      "Movie ID: la_deuda, Estimated Rating: 10\n",
      "Movie ID: pitra_putra_aur_dharamyuddha, Estimated Rating: 10\n",
      "Movie ID: shem_the_penman_sings_again, Estimated Rating: 10\n",
      "Movie ID: new_orleans_politics, Estimated Rating: 10\n",
      "Movie ID: johnny_cool, Estimated Rating: 10\n",
      "Movie ID: starting_at_zero_reimagining_education_in_america, Estimated Rating: 10\n",
      "Movie ID: sufi_paranja_katha, Estimated Rating: 10\n",
      "Movie ID: father_2018, Estimated Rating: 10\n",
      "Movie ID: the_pagan_1929, Estimated Rating: 10\n",
      "Movie ID: race_the_sun, Estimated Rating: 10\n",
      "Movie ID: steve-harvey-still-trippin, Estimated Rating: 10\n",
      "Movie ID: 10010327-seed, Estimated Rating: 10\n",
      "Movie ID: the_immature, Estimated Rating: 10\n",
      "Movie ID: angel_guts_nami, Estimated Rating: 10\n",
      "Movie ID: toorbos, Estimated Rating: 10\n",
      "Movie ID: truth_about_love, Estimated Rating: 10\n",
      "Movie ID: moglie_e_marito, Estimated Rating: 10\n",
      "Movie ID: watch_out_were_mad_2022, Estimated Rating: 10\n",
      "Movie ID: blood_mask, Estimated Rating: 10\n",
      "Movie ID: white_on_white, Estimated Rating: 10\n",
      "Movie ID: bet_to_basic_2000, Estimated Rating: 10\n",
      "Movie ID: shot_in_the_dark, Estimated Rating: 10\n",
      "Movie ID: under_heat, Estimated Rating: 10\n",
      "Movie ID: nipawistamasowin_we_will_stand_up, Estimated Rating: 10\n",
      "Movie ID: darwin_conspiracy, Estimated Rating: 10\n",
      "Movie ID: limo, Estimated Rating: 10\n",
      "Movie ID: prison_girls, Estimated Rating: 10\n",
      "Movie ID: one_summer_night_in_town, Estimated Rating: 10\n",
      "Movie ID: paul_blart_mall_cop, Estimated Rating: 10\n",
      "Movie ID: how_to_disappear_completely, Estimated Rating: 10\n",
      "Movie ID: take-her-shes-mine, Estimated Rating: 10\n",
      "Movie ID: 2112, Estimated Rating: 10\n",
      "Movie ID: tolerance_1989, Estimated Rating: 10\n",
      "Movie ID: not_without_my_daughter, Estimated Rating: 10\n",
      "Movie ID: monsoon_wedding, Estimated Rating: 10\n",
      "Movie ID: between_kings_and_queens_2012, Estimated Rating: 10\n",
      "Movie ID: distiller, Estimated Rating: 10\n",
      "Movie ID: dr_knock_2017, Estimated Rating: 10\n",
      "Movie ID: 33_days_2014, Estimated Rating: 10\n",
      "Movie ID: 10009299-falling, Estimated Rating: 10\n",
      "Movie ID: the_soong_sisters, Estimated Rating: 10\n",
      "Movie ID: sleigh_bells_ring, Estimated Rating: 10\n",
      "Movie ID: rewind_2003, Estimated Rating: 10\n",
      "Movie ID: standstill, Estimated Rating: 10\n",
      "Movie ID: the_railway_station_man, Estimated Rating: 10\n",
      "Movie ID: nice_people, Estimated Rating: 10\n"
     ]
    }
   ],
   "source": [
    "# Generate recommendations for a user\n",
    "user_id = '1145982'\n",
    "top_n = 100\n",
    "\n",
    "# Get a list of all movie ids\n",
    "movie_ids = movies['id'].unique()\n",
    "\n",
    "# Predict ratings for all movies\n",
    "predictions = [algo.predict(user_id, movie_id) for movie_id in movie_ids]\n",
    "\n",
    "# Sort by estimated rating\n",
    "recommendations = sorted(predictions, key=lambda x: x.est, reverse=True)[:top_n]\n",
    "\n",
    "# Display top 10 recommendations\n",
    "for rec in recommendations:\n",
    "    print(f\"Movie ID: {rec.iid}, Estimated Rating: {rec.est}\")"
   ]
  }
 ],
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