movie-rec-system-final-op-2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": 32,
   "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": 33,
   "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": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "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": 35,
   "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": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "reviews = reviews_cleaned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1004642, 12)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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": 40,
   "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": 41,
   "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": 42,
   "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": 43,
   "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": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishn\\AppData\\Local\\Temp\\ipykernel_30356\\2134284064.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\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.drop(columns=['originalScore', 'reviewText', 'reviewUrl'], inplace=True)\n"
     ]
    }
   ],
   "source": [
    "reviews.drop(columns=['originalScore', 'reviewText', 'reviewUrl'], inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                 0\n",
      "reviewId           0\n",
      "creationDate       0\n",
      "criticName         0\n",
      "isTopCritic        0\n",
      "reviewState        0\n",
      "publicatioName     0\n",
      "scoreSentiment     0\n",
      "normalizedScore    0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(reviews.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\vishn\\AppData\\Local\\Temp\\ipykernel_30356\\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": 47,
   "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": 48,
   "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": 49,
   "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": 50,
   "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": 51,
   "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": 52,
   "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": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['id', 'reviewId', 'creationDate', 'criticName', 'isTopCritic',\n",
      "       'reviewState', 'publicatioName', 'scoreSentiment', 'normalizedScore'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(reviews.columns)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "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>reviewState</th>\n",
       "      <th>publicatioName</th>\n",
       "      <th>scoreSentiment</th>\n",
       "      <th>normalizedScore</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1443511</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>102675351</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>MaryAnn Johanson</td>\n",
       "      <td>False</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Flick Filosopher</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443512</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>102673881</td>\n",
       "      <td>2022-03-29</td>\n",
       "      <td>Nick Hasted</td>\n",
       "      <td>False</td>\n",
       "      <td>rotten</td>\n",
       "      <td>The Arts Desk</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443513</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>102673096</td>\n",
       "      <td>2022-03-27</td>\n",
       "      <td>Edward Porter</td>\n",
       "      <td>False</td>\n",
       "      <td>fresh</td>\n",
       "      <td>Sunday Times (UK)</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>6.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443514</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>102673095</td>\n",
       "      <td>2022-03-27</td>\n",
       "      <td>Wendy Ide</td>\n",
       "      <td>True</td>\n",
       "      <td>rotten</td>\n",
       "      <td>Observer (UK)</td>\n",
       "      <td>NEGATIVE</td>\n",
       "      <td>4.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1443515</th>\n",
       "      <td>ambulance</td>\n",
       "      <td>102673036</td>\n",
       "      <td>2022-03-26</td>\n",
       "      <td>Scott Campbell</td>\n",
       "      <td>False</td>\n",
       "      <td>fresh</td>\n",
       "      <td>We Got This Covered</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>6.00</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",
       "    </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>fresh</td>\n",
       "      <td>Raising Whasians</td>\n",
       "      <td>POSITIVE</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>fresh</td>\n",
       "      <td>Digital Spy</td>\n",
       "      <td>POSITIVE</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>fresh</td>\n",
       "      <td>Next Best Picture</td>\n",
       "      <td>POSITIVE</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>rotten</td>\n",
       "      <td>Slant Magazine</td>\n",
       "      <td>NEGATIVE</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>fresh</td>\n",
       "      <td>Movie Nation</td>\n",
       "      <td>POSITIVE</td>\n",
       "      <td>6.25</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                            id   reviewId creationDate        criticName  \\\n",
       "1443511              ambulance  102675351   2022-04-01  MaryAnn Johanson   \n",
       "1443512              ambulance  102673881   2022-03-29       Nick Hasted   \n",
       "1443513              ambulance  102673096   2022-03-27     Edward Porter   \n",
       "1443514              ambulance  102673095   2022-03-27         Wendy Ide   \n",
       "1443515              ambulance  102673036   2022-03-26    Scott Campbell   \n",
       "...                        ...        ...          ...               ...   \n",
       "1444958  thor_love_and_thunder  102706151   2022-07-05   Christie Cronan   \n",
       "1444959  thor_love_and_thunder  102706150   2022-07-05      Ian Sandwell   \n",
       "1444960  thor_love_and_thunder  102706149   2022-07-05    Lauren LaMagna   \n",
       "1444961  thor_love_and_thunder  102706148   2022-07-05         Jake Cole   \n",
       "1444962  thor_love_and_thunder  102706147   2022-07-05       Roger Moore   \n",
       "\n",
       "         isTopCritic reviewState       publicatioName scoreSentiment  \\\n",
       "1443511        False      rotten     Flick Filosopher       NEGATIVE   \n",
       "1443512        False      rotten        The Arts Desk       NEGATIVE   \n",
       "1443513        False       fresh    Sunday Times (UK)       POSITIVE   \n",
       "1443514         True      rotten        Observer (UK)       NEGATIVE   \n",
       "1443515        False       fresh  We Got This Covered       POSITIVE   \n",
       "...              ...         ...                  ...            ...   \n",
       "1444958        False       fresh     Raising Whasians       POSITIVE   \n",
       "1444959        False       fresh          Digital Spy       POSITIVE   \n",
       "1444960        False       fresh    Next Best Picture       POSITIVE   \n",
       "1444961         True      rotten       Slant Magazine       NEGATIVE   \n",
       "1444962        False       fresh         Movie Nation       POSITIVE   \n",
       "\n",
       "         normalizedScore  \n",
       "1443511             0.00  \n",
       "1443512             4.00  \n",
       "1443513             6.00  \n",
       "1443514             4.00  \n",
       "1443515             6.00  \n",
       "...                  ...  \n",
       "1444958             7.00  \n",
       "1444959             8.00  \n",
       "1444960             8.00  \n",
       "1444961             2.50  \n",
       "1444962             6.25  \n",
       "\n",
       "[1000 rows x 9 columns]"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.tail(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "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": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Train-test split\n",
    "trainset, testset = train_test_split(data, test_size=0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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)    4.3814  4.7207  5.6217  4.3665  74363.271214876.472329743.3995\n",
      "MAE (testset)     3.7728  3.7708  3.7746  3.7636  167.275636.4715 65.4021 \n",
      "Fit time          16.49   12.78   13.60   14.63   14.55   14.41   1.24    \n",
      "Test time         1.41    1.36    1.51    0.79    0.79    1.17    0.32    \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'test_rmse': array([4.38138650e+00, 4.72068216e+00, 5.62167099e+00, 4.36645600e+00,\n",
       "        7.43632712e+04]),\n",
       " 'test_mae': array([  3.77276975,   3.77081118,   3.77462888,   3.76362776,\n",
       "        167.27563669]),\n",
       " 'fit_time': (16.49494171142578,\n",
       "  12.78185510635376,\n",
       "  13.599727630615234,\n",
       "  14.631518125534058,\n",
       "  14.55471158027649),\n",
       " 'test_time': (1.4114160537719727,\n",
       "  1.3566539287567139,\n",
       "  1.5149540901184082,\n",
       "  0.7929315567016602,\n",
       "  0.7888388633728027)}"
      ]
     },
     "execution_count": 57,
     "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": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.tail of                                         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 reviewState  \\\n",
       "0        Ivan M. Lincoln        False       fresh   \n",
       "1          The Foywonder        False      rotten   \n",
       "3            Matt Schley        False      rotten   \n",
       "5             Eric Melin        False       fresh   \n",
       "6            Matt Donato        False       fresh   \n",
       "...                  ...          ...         ...   \n",
       "1444958  Christie Cronan        False       fresh   \n",
       "1444959     Ian Sandwell        False       fresh   \n",
       "1444960   Lauren LaMagna        False       fresh   \n",
       "1444961        Jake Cole         True      rotten   \n",
       "1444962      Roger Moore        False       fresh   \n",
       "\n",
       "                        publicatioName scoreSentiment  normalizedScore  \n",
       "0        Deseret News (Salt Lake City)       POSITIVE             8.75  \n",
       "1                        Dread Central       NEGATIVE             2.00  \n",
       "3                          Japan Times       NEGATIVE             5.00  \n",
       "5                         Lawrence.com       POSITIVE             8.00  \n",
       "6                  We Got This Covered       POSITIVE             7.00  \n",
       "...                                ...            ...              ...  \n",
       "1444958               Raising Whasians       POSITIVE             7.00  \n",
       "1444959                    Digital Spy       POSITIVE             8.00  \n",
       "1444960              Next Best Picture       POSITIVE             8.00  \n",
       "1444961                 Slant Magazine       NEGATIVE             2.50  \n",
       "1444962                   Movie Nation       POSITIVE             6.25  \n",
       "\n",
       "[1004642 rows x 9 columns]>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reviews.tail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Movie ID: sobibor, Estimated Rating: 10\n",
      "Movie ID: collateral_beauty, Estimated Rating: 10\n",
      "Movie ID: the_accountant_2016, Estimated Rating: 10\n",
      "Movie ID: we_used_to_know_each_other, Estimated Rating: 10\n",
      "Movie ID: hell_or_high_water, Estimated Rating: 10\n",
      "Movie ID: citizen_kane, Estimated Rating: 9.60728654837241\n",
      "Movie ID: the_godfather, Estimated Rating: 9.50983473606444\n",
      "Movie ID: singin_in_the_rain, Estimated Rating: 9.462061148443283\n",
      "Movie ID: the_third_man, Estimated Rating: 9.440763306642616\n",
      "Movie ID: sunset_boulevard, Estimated Rating: 9.372165012136433\n",
      "Movie ID: godfather_part_ii, Estimated Rating: 9.371508562337333\n",
      "Movie ID: seven_samurai_1956, Estimated Rating: 9.31755205807581\n",
      "Movie ID: psycho, Estimated Rating: 9.313281405078127\n",
      "Movie ID: 400_blows, Estimated Rating: 9.309555737970774\n",
      "Movie ID: chinatown, Estimated Rating: 9.298776398589444\n",
      "Movie ID: lawrence_of_arabia, Estimated Rating: 9.285795703045979\n",
      "Movie ID: the_wizard_of_oz_1939, Estimated Rating: 9.279510819697983\n",
      "Movie ID: 2001_a_space_odyssey, Estimated Rating: 9.273599236691918\n",
      "Movie ID: 1003707-casablanca, Estimated Rating: 9.269363762173054\n",
      "Movie ID: 1008166-general, Estimated Rating: 9.25250726009503\n",
      "Movie ID: 1013139-maltese_falcon, Estimated Rating: 9.229789768432756\n",
      "Movie ID: star_wars_the_empire_strikes_back, Estimated Rating: 9.221788265492183\n",
      "Movie ID: pulp_fiction, Estimated Rating: 9.20681253261883\n",
      "Movie ID: annie_hall, Estimated Rating: 9.194620013316111\n",
      "Movie ID: 1000626-all_about_eve, Estimated Rating: 9.193099423270816\n",
      "Movie ID: schindlers_list, Estimated Rating: 9.191753269221092\n",
      "Movie ID: 1012928-m, Estimated Rating: 9.180193847744956\n",
      "Movie ID: raiders_of_the_lost_ark, Estimated Rating: 9.179313086270245\n",
      "Movie ID: 1013775-metropolis, Estimated Rating: 9.177007223255139\n",
      "Movie ID: tokyo_story, Estimated Rating: 9.17167322584395\n",
      "Movie ID: dolce_vita, Estimated Rating: 9.168217519608504\n",
      "Movie ID: jaws, Estimated Rating: 9.153239203081244\n",
      "Movie ID: et_the_extraterrestrial, Estimated Rating: 9.153159578184795\n",
      "Movie ID: vertigo, Estimated Rating: 9.14857607660135\n",
      "Movie ID: 1017289-rear_window, Estimated Rating: 9.146617784722288\n",
      "Movie ID: taxi_driver, Estimated Rating: 9.136457344223562\n",
      "Movie ID: rashomon, Estimated Rating: 9.120720288472658\n",
      "Movie ID: north-by-northwest, Estimated Rating: 9.108329802202796\n",
      "Movie ID: anhell69, Estimated Rating: 9.082666707510457\n",
      "Movie ID: city_lights, Estimated Rating: 9.08213841608028\n",
      "Movie ID: seventh_seal, Estimated Rating: 9.066642857644803\n",
      "Movie ID: the_cabinet_of_dr_caligari, Estimated Rating: 9.066349170584118\n",
      "Movie ID: the_battle_of_algiers, Estimated Rating: 9.066234866557867\n",
      "Movie ID: parasite_2019, Estimated Rating: 9.066127785325202\n",
      "Movie ID: dr_strangelove, Estimated Rating: 9.061667771100659\n",
      "Movie ID: la_grande_illusion, Estimated Rating: 9.06137717270668\n",
      "Movie ID: alien, Estimated Rating: 9.060542137640144\n",
      "Movie ID: sunrise, Estimated Rating: 9.037157754196372\n",
      "Movie ID: star_wars_a_new_hope, Estimated Rating: 9.032845301647663\n",
      "Movie ID: one_flew_over_the_cuckoos_nest, Estimated Rating: 9.029538473975455\n",
      "Movie ID: on_the_waterfront, Estimated Rating: 9.029291058105649\n",
      "Movie ID: toy_story, Estimated Rating: 9.027050670038054\n",
      "Movie ID: bicycle_thieves, Estimated Rating: 9.021811914189342\n",
      "Movie ID: 1000617-aliens, Estimated Rating: 8.992136117782817\n",
      "Movie ID: bridge_on_the_river_kwai, Estimated Rating: 8.96040181392542\n",
      "Movie ID: drive_my_car, Estimated Rating: 8.959078445039342\n",
      "Movie ID: beatles_a_hard_days_night, Estimated Rating: 8.947492864572897\n",
      "Movie ID: pather_panchali, Estimated Rating: 8.942220820927702\n",
      "Movie ID: passion_of_joan_of_arc, Estimated Rating: 8.937764261648478\n",
      "Movie ID: night_of_the_hunter, Estimated Rating: 8.926971884576858\n",
      "Movie ID: his_girl_friday, Estimated Rating: 8.925272914107207\n",
      "Movie ID: do_the_right_thing, Estimated Rating: 8.918159614703228\n",
      "Movie ID: 1032176-goodfellas, Estimated Rating: 8.91630646839011\n",
      "Movie ID: modern_times, Estimated Rating: 8.915779429220324\n",
      "Movie ID: battleship_potemkin, Estimated Rating: 8.91213532095202\n",
      "Movie ID: la_confidential, Estimated Rating: 8.911263836463373\n",
      "Movie ID: 1017346-red_shoes, Estimated Rating: 8.909651962655431\n",
      "Movie ID: apocalypse_now, Estimated Rating: 8.906806366742957\n",
      "Movie ID: treasure_of_the_sierra_madre, Estimated Rating: 8.904081705986085\n",
      "Movie ID: hamilton_2020, Estimated Rating: 8.903600394824295\n",
      "Movie ID: crouching_tiger_hidden_dragon, Estimated Rating: 8.90347098111467\n",
      "Movie ID: jules_and_jim, Estimated Rating: 8.894264140637892\n",
      "Movie ID: double_indemnity, Estimated Rating: 8.887914996700697\n",
      "Movie ID: paths_of_glory, Estimated Rating: 8.882194792140687\n",
      "Movie ID: boyhood, Estimated Rating: 8.878218580045933\n",
      "Movie ID: 1021749-touch_of_evil, Estimated Rating: 8.873412058064712\n",
      "Movie ID: summer_of_soul, Estimated Rating: 8.860135144688982\n",
      "Movie ID: some_like_it_hot, Estimated Rating: 8.85485323212463\n",
      "Movie ID: the_rules_of_the_game, Estimated Rating: 8.85452348137151\n",
      "Movie ID: raging_bull, Estimated Rating: 8.85353622266111\n",
      "Movie ID: toy_story_2, Estimated Rating: 8.85083826975878\n",
      "Movie ID: the_good_the_bad_and_the_ugly, Estimated Rating: 8.848510164917734\n",
      "Movie ID: duck_soup, Estimated Rating: 8.846358832835259\n",
      "Movie ID: ran, Estimated Rating: 8.845229975081654\n",
      "Movie ID: 1000355-adventures_of_robin_hood, Estimated Rating: 8.833626341697848\n",
      "Movie ID: the_conformist, Estimated Rating: 8.832850512721633\n",
      "Movie ID: once_upon_a_time_in_the_west, Estimated Rating: 8.828551414855305\n",
      "Movie ID: the_leopard_1963, Estimated Rating: 8.825966208770376\n",
      "Movie ID: no_country_for_old_men, Estimated Rating: 8.82559198084737\n",
      "Movie ID: all_the_presidents_men, Estimated Rating: 8.823850529930047\n",
      "Movie ID: fargo, Estimated Rating: 8.821741350851887\n",
      "Movie ID: last_picture_show, Estimated Rating: 8.82022280440104\n",
      "Movie ID: petite_maman, Estimated Rating: 8.818821093065596\n",
      "Movie ID: au_hasard_balthazar, Estimated Rating: 8.818390094441597\n",
      "Movie ID: 1013227-manchurian_candidate, Estimated Rating: 8.811927465470866\n",
      "Movie ID: 1000013_12_angry_men, Estimated Rating: 8.811686222178963\n",
      "Movie ID: a_man_escaped, Estimated Rating: 8.807671966946769\n",
      "Movie ID: it_happened_one_night, Estimated Rating: 8.807219163153999\n",
      "Movie ID: 1015287-notorious, Estimated Rating: 8.807075654478737\n",
      "Movie ID: night_of_the_living_dead, Estimated Rating: 8.806722236693465\n"
     ]
    }
   ],
   "source": [
    "# Generate recommendations for a user\n",
    "user_id = '102673036'\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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