rotten_tomatoes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Statement\n",
    "\n",
    "- Classify movies into 3 classes - 'Rotten', 'Fresh' or 'Certified-Fresh'\\\n",
    "- Criteria is as follows:\n",
    "    - Fresh: Tomatometer score >= 60%\n",
    "    - Rotten: Tomatometer score < 60%\n",
    "    - Certified-Fresh:\n",
    "        - A consistent tomatometer score of at least 75% **and**\n",
    "        - At least 5 reviews from top critics **and**\n",
    "        - A minimum of 80 reviews for films in wide release\n",
    "\n",
    "- We will use 2 approaches:\n",
    "    1. Predict movie status based on **numerical and categorical features**.\n",
    "    2. Predict movie status based on **Review Sentiment**\n",
    "\n",
    "- We will use:\n",
    "    - Decision Tree Classifier\n",
    "    - Random Forest Algorithm\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Data Exploration"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.utils.class_weight import compute_class_weight\n",
    "from sklearn.tree import DecisionTreeClassifier, plot_tree\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.metrics import plot_confusion_matrix, classification_report, confusion_matrix, accuracy_score\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>movie_info</th>\n",
       "      <th>critics_consensus</th>\n",
       "      <th>content_rating</th>\n",
       "      <th>genres</th>\n",
       "      <th>directors</th>\n",
       "      <th>authors</th>\n",
       "      <th>actors</th>\n",
       "      <th>original_release_date</th>\n",
       "      <th>...</th>\n",
       "      <th>production_company</th>\n",
       "      <th>tomatometer_status</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_count</th>\n",
       "      <th>audience_status</th>\n",
       "      <th>audience_rating</th>\n",
       "      <th>audience_count</th>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Always trouble-prone, the life of teenager Per...</td>\n",
       "      <td>Though it may seem like just another Harry Pot...</td>\n",
       "      <td>PG</td>\n",
       "      <td>Action &amp; Adventure, Comedy, Drama, Science Fic...</td>\n",
       "      <td>Chris Columbus</td>\n",
       "      <td>Craig Titley, Chris Columbus, Rick Riordan</td>\n",
       "      <td>Logan Lerman, Brandon T. Jackson, Alexandra Da...</td>\n",
       "      <td>2010-02-12</td>\n",
       "      <td>...</td>\n",
       "      <td>20th Century Fox</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>49.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>254421.0</td>\n",
       "      <td>43</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m/0878835</td>\n",
       "      <td>Please Give</td>\n",
       "      <td>Kate (Catherine Keener) and her husband Alex (...</td>\n",
       "      <td>Nicole Holofcener's newest might seem slight i...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Nicole Holofcener</td>\n",
       "      <td>Catherine Keener, Amanda Peet, Oliver Platt, R...</td>\n",
       "      <td>2010-04-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Sony Pictures Classics</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "      <td>87.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11574.0</td>\n",
       "      <td>44</td>\n",
       "      <td>123</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m/10</td>\n",
       "      <td>10</td>\n",
       "      <td>A successful, middle-aged Hollywood songwriter...</td>\n",
       "      <td>Blake Edwards' bawdy comedy may not score a pe...</td>\n",
       "      <td>R</td>\n",
       "      <td>Comedy, Romance</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Blake Edwards</td>\n",
       "      <td>Dudley Moore, Bo Derek, Julie Andrews, Robert ...</td>\n",
       "      <td>1979-10-05</td>\n",
       "      <td>...</td>\n",
       "      <td>Waner Bros.</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>67.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>Spilled</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14684.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m/1000013-12_angry_men</td>\n",
       "      <td>12 Angry Men (Twelve Angry Men)</td>\n",
       "      <td>Following the closing arguments in a murder tr...</td>\n",
       "      <td>Sidney Lumet's feature debut is a superbly wri...</td>\n",
       "      <td>NR</td>\n",
       "      <td>Classics, Drama</td>\n",
       "      <td>Sidney Lumet</td>\n",
       "      <td>Reginald Rose</td>\n",
       "      <td>Martin Balsam, John Fiedler, Lee J. Cobb, E.G....</td>\n",
       "      <td>1957-04-13</td>\n",
       "      <td>...</td>\n",
       "      <td>Criterion Collection</td>\n",
       "      <td>Certified-Fresh</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>97.0</td>\n",
       "      <td>105386.0</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m/1000079-20000_leagues_under_the_sea</td>\n",
       "      <td>20,000 Leagues Under The Sea</td>\n",
       "      <td>In 1866, Professor Pierre M. Aronnax (Paul Luk...</td>\n",
       "      <td>One of Disney's finest live-action adventures,...</td>\n",
       "      <td>G</td>\n",
       "      <td>Action &amp; Adventure, Drama, Kids &amp; Family</td>\n",
       "      <td>Richard Fleischer</td>\n",
       "      <td>Earl Felton</td>\n",
       "      <td>James Mason, Kirk Douglas, Paul Lukas, Peter L...</td>\n",
       "      <td>1954-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Disney</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>89.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>Upright</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68918.0</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                    rotten_tomatoes_link  \\\n",
       "0                              m/0814255   \n",
       "1                              m/0878835   \n",
       "2                                   m/10   \n",
       "3                 m/1000013-12_angry_men   \n",
       "4  m/1000079-20000_leagues_under_the_sea   \n",
       "\n",
       "                                         movie_title  \\\n",
       "0  Percy Jackson & the Olympians: The Lightning T...   \n",
       "1                                        Please Give   \n",
       "2                                                 10   \n",
       "3                    12 Angry Men (Twelve Angry Men)   \n",
       "4                       20,000 Leagues Under The Sea   \n",
       "\n",
       "                                          movie_info  \\\n",
       "0  Always trouble-prone, the life of teenager Per...   \n",
       "1  Kate (Catherine Keener) and her husband Alex (...   \n",
       "2  A successful, middle-aged Hollywood songwriter...   \n",
       "3  Following the closing arguments in a murder tr...   \n",
       "4  In 1866, Professor Pierre M. Aronnax (Paul Luk...   \n",
       "\n",
       "                                   critics_consensus content_rating  \\\n",
       "0  Though it may seem like just another Harry Pot...             PG   \n",
       "1  Nicole Holofcener's newest might seem slight i...              R   \n",
       "2  Blake Edwards' bawdy comedy may not score a pe...              R   \n",
       "3  Sidney Lumet's feature debut is a superbly wri...             NR   \n",
       "4  One of Disney's finest live-action adventures,...              G   \n",
       "\n",
       "                                              genres          directors  \\\n",
       "0  Action & Adventure, Comedy, Drama, Science Fic...     Chris Columbus   \n",
       "1                                             Comedy  Nicole Holofcener   \n",
       "2                                    Comedy, Romance      Blake Edwards   \n",
       "3                                    Classics, Drama       Sidney Lumet   \n",
       "4           Action & Adventure, Drama, Kids & Family  Richard Fleischer   \n",
       "\n",
       "                                      authors  \\\n",
       "0  Craig Titley, Chris Columbus, Rick Riordan   \n",
       "1                           Nicole Holofcener   \n",
       "2                               Blake Edwards   \n",
       "3                               Reginald Rose   \n",
       "4                                 Earl Felton   \n",
       "\n",
       "                                              actors original_release_date  \\\n",
       "0  Logan Lerman, Brandon T. Jackson, Alexandra Da...            2010-02-12   \n",
       "1  Catherine Keener, Amanda Peet, Oliver Platt, R...            2010-04-30   \n",
       "2  Dudley Moore, Bo Derek, Julie Andrews, Robert ...            1979-10-05   \n",
       "3  Martin Balsam, John Fiedler, Lee J. Cobb, E.G....            1957-04-13   \n",
       "4  James Mason, Kirk Douglas, Paul Lukas, Peter L...            1954-01-01   \n",
       "\n",
       "   ...      production_company  tomatometer_status tomatometer_rating  \\\n",
       "0  ...        20th Century Fox              Rotten               49.0   \n",
       "1  ...  Sony Pictures Classics     Certified-Fresh               87.0   \n",
       "2  ...             Waner Bros.               Fresh               67.0   \n",
       "3  ...    Criterion Collection     Certified-Fresh              100.0   \n",
       "4  ...                  Disney               Fresh               89.0   \n",
       "\n",
       "  tomatometer_count  audience_status  audience_rating audience_count  \\\n",
       "0             149.0          Spilled             53.0       254421.0   \n",
       "1             142.0          Upright             64.0        11574.0   \n",
       "2              24.0          Spilled             53.0        14684.0   \n",
       "3              54.0          Upright             97.0       105386.0   \n",
       "4              27.0          Upright             74.0        68918.0   \n",
       "\n",
       "   tomatometer_top_critics_count  tomatometer_fresh_critics_count  \\\n",
       "0                             43                               73   \n",
       "1                             44                              123   \n",
       "2                              2                               16   \n",
       "3                              6                               54   \n",
       "4                              5                               24   \n",
       "\n",
       "   tomatometer_rotten_critics_count  \n",
       "0                                76  \n",
       "1                                19  \n",
       "2                                 8  \n",
       "3                                 0  \n",
       "4                                 3  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movie = pd.read_csv('rotten_tomatoes_movies.csv')\n",
    "df_movie.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>runtime</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_count</th>\n",
       "      <th>audience_rating</th>\n",
       "      <th>audience_count</th>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17398.000000</td>\n",
       "      <td>17668.000000</td>\n",
       "      <td>17668.000000</td>\n",
       "      <td>17416.000000</td>\n",
       "      <td>1.741500e+04</td>\n",
       "      <td>17712.000000</td>\n",
       "      <td>17712.000000</td>\n",
       "      <td>17712.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>102.214048</td>\n",
       "      <td>60.884763</td>\n",
       "      <td>57.139801</td>\n",
       "      <td>60.554260</td>\n",
       "      <td>1.439401e+05</td>\n",
       "      <td>14.586326</td>\n",
       "      <td>36.374831</td>\n",
       "      <td>20.703139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>18.702511</td>\n",
       "      <td>28.443348</td>\n",
       "      <td>68.370047</td>\n",
       "      <td>20.543369</td>\n",
       "      <td>1.763577e+06</td>\n",
       "      <td>15.146349</td>\n",
       "      <td>52.601038</td>\n",
       "      <td>30.248435</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>90.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>45.000000</td>\n",
       "      <td>7.075000e+02</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>99.000000</td>\n",
       "      <td>67.000000</td>\n",
       "      <td>28.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>4.277000e+03</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>8.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>111.000000</td>\n",
       "      <td>86.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>2.498800e+04</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>24.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>266.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>574.000000</td>\n",
       "      <td>100.000000</td>\n",
       "      <td>3.579764e+07</td>\n",
       "      <td>69.000000</td>\n",
       "      <td>497.000000</td>\n",
       "      <td>303.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            runtime  tomatometer_rating  tomatometer_count  audience_rating  \\\n",
       "count  17398.000000        17668.000000       17668.000000     17416.000000   \n",
       "mean     102.214048           60.884763          57.139801        60.554260   \n",
       "std       18.702511           28.443348          68.370047        20.543369   \n",
       "min        5.000000            0.000000           5.000000         0.000000   \n",
       "25%       90.000000           38.000000          12.000000        45.000000   \n",
       "50%       99.000000           67.000000          28.000000        63.000000   \n",
       "75%      111.000000           86.000000          75.000000        78.000000   \n",
       "max      266.000000          100.000000         574.000000       100.000000   \n",
       "\n",
       "       audience_count  tomatometer_top_critics_count  \\\n",
       "count    1.741500e+04                   17712.000000   \n",
       "mean     1.439401e+05                      14.586326   \n",
       "std      1.763577e+06                      15.146349   \n",
       "min      5.000000e+00                       0.000000   \n",
       "25%      7.075000e+02                       3.000000   \n",
       "50%      4.277000e+03                       8.000000   \n",
       "75%      2.498800e+04                      23.000000   \n",
       "max      3.579764e+07                      69.000000   \n",
       "\n",
       "       tomatometer_fresh_critics_count  tomatometer_rotten_critics_count  \n",
       "count                     17712.000000                      17712.000000  \n",
       "mean                         36.374831                         20.703139  \n",
       "std                          52.601038                         30.248435  \n",
       "min                           0.000000                          0.000000  \n",
       "25%                           6.000000                          3.000000  \n",
       "50%                          16.000000                          8.000000  \n",
       "75%                          44.000000                         24.000000  \n",
       "max                         497.000000                        303.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_movie.describe() # gives the description of the numerical features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We have a large number of numerical features.\n",
    "- Our target variable is `tomatometer_status`\n",
    "- It is important to note that features have missing values, we need to deal with."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Content Rating category: ['PG' 'R' 'NR' 'G' 'PG-13' 'NC17']\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '6377'),\n",
       " Text(0, 0, '5474'),\n",
       " Text(0, 0, '2979'),\n",
       " Text(0, 0, '2168'),\n",
       " Text(0, 0, '676'),\n",
       " Text(0, 0, '38')]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing content rating\n",
    "print(f'Content Rating category: {df_movie.content_rating.unique()}')\n",
    "\n",
    "ax = df_movie['content_rating'].value_counts().plot(kind='bar', figsize=(12,9))\n",
    "ax.bar_label(ax.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Need to convert this feature into numerical representations\n",
    "- Two popular methods: **ordinal encoding** and **one-hot encoding**\n",
    "### Ordinal Encoding\n",
    "It is used when the categorical feature represents a degree of magnitude (think S, M, L).\n",
    "### One Hot Encoding\n",
    "It is used when one value is not more important than other values in a categorical feature."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>G</th>\n",
       "      <th>NC17</th>\n",
       "      <th>NR</th>\n",
       "      <th>PG</th>\n",
       "      <th>PG-13</th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   G  NC17  NR  PG  PG-13  R\n",
       "0  0     0   0   1      0  0\n",
       "1  0     0   0   0      0  1\n",
       "2  0     0   0   0      0  1\n",
       "3  0     0   1   0      0  0\n",
       "4  1     0   0   0      0  0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# one-hot encoding the content rating feature\n",
    "content_rating = pd.get_dummies(df_movie['content_rating'])\n",
    "content_rating.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Audience Status has the values 'Spilled' and 'Upright'.\n",
    "- It needs ordinal encoding."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Audience Status Category: ['Spilled' 'Upright' nan]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '9390'), Text(0, 0, '7874')]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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StWvXdO3aNePGjUtLS0u6du2aK664YqMfG/DSOnRG/L3vfW/OOuusbLPNNnnTm96UX/3qV7ngggtyzDHHJElqamoyceLEfPnLX84OO+yQwYMH55RTTsnAgQNz6KGHJkl22mmnHHjggTn22GMzderUrF69OhMmTMhhhx1W+ZfAww8/PKeffnrGjRuXE088Mb/5zW/yta99LRdeeOGre/QAAFAlV155Zfr375/Ro//vrhN///vfk+QlT36t09bWliuvvDJHHnlkunXr1m7f/Pnz212Z/Qc/+EHOPffczJs3L69//etf7UMBOqhDIf71r389p5xySj7zmc9k6dKlGThwYD75yU9m8uTJlTVf/OIX8+yzz+a4447L8uXL8/a3vz233nprevToUVlz9dVXZ8KECRk+fHi6dOmSMWPG5OKLL67sr6+vz+23357x48dnzz33zFZbbZXJkye3u9c4AABsqtauXZsrr7wyRx11VLp2/b+/kg8dOjRDhgzJJz/5yZx33nnp169fbrrppsyaNSszZ85s9xpz5szJY4891u6M+jo77bRTu59/+ctfpkuXLtl55503zgEBHdKh+4hvStxH/LXHfcShc3MfcYCX7/bbb6/cnveNb3xju32PPPJIJk2alLvuuisrVqzIkCFD8oUvfCFHHHFEu3WHH354/vSnP+XnP//5f/x906dPz8SJE91HHDayl9uhQpxNhhCHzk2IAwD/7V5uh3boYm0AAADAK9Oh74gDALDp8aky6Nx8quy/jzPiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQUIcAAAAChLiAAAAUJAQBwAAgIKEOAAAABQkxAEAAKAgIQ4AAAAFCXEAAAAoSIgDAABAQR0O8SeeeCIf+9jH0q9fv/Ts2TO77LJLfvnLX1b2t7W1ZfLkyRkwYEB69uyZESNG5JFHHmn3GsuWLcvYsWNTV1eXvn37Zty4cVmxYkW7NQ888EDe8Y53pEePHhk0aFCmTJmygYcIAAAAnUeHQvzpp5/O2972tnTr1i0//vGP89vf/jbnn39+tthii8qaKVOm5OKLL87UqVNz9913p1evXhk1alSee+65ypqxY8fmwQcfzKxZszJz5szccccdOe644yr7W1tbM3LkyGy77bZZsGBBvvrVr+a0007L5Zdf/iocMgAAAFRP144sPvfcczNo0KBceeWVlW2DBw+u/LmtrS0XXXRRTj755Lzvfe9Lknz7299OQ0NDbrrpphx22GH53e9+l1tvvTX33ntv9tprryTJ17/+9bznPe/Jeeedl4EDB+bqq6/OqlWrcsUVV6R79+5505velPvvvz8XXHBBu2AHAACATU2HzojffPPN2WuvvfKhD30o/fv3z+67755vfvOblf2PPfZYmpubM2LEiMq2+vr67L333pk/f36SZP78+enbt28lwpNkxIgR6dKlS+6+++7Kmv322y/du3evrBk1alQWLVqUp59+er2zrVy5Mq2tre0eAAAA0Nl0KMT/8Ic/5LLLLssOO+yQ2267LZ/+9KfzP//zP5kxY0aSpLm5OUnS0NDQ7nkNDQ2Vfc3Nzenfv3+7/V27ds2WW27Zbs36XuNff8e/O/vss1NfX195DBo0qCOHBgAAAEV0KMTXrl2bPfbYI1/5yley++6757jjjsuxxx6bqVOnbqz5XraTTjopLS0tlcfixYurPRIAAAC8QIdCfMCAARk2bFi7bTvttFMef/zxJEljY2OSZMmSJe3WLFmypLKvsbExS5cubbf/+eefz7Jly9qtWd9r/Ovv+He1tbWpq6tr9wAAAIDOpkMh/ra3vS2LFi1qt+3hhx/Otttum+SfF25rbGzM7NmzK/tbW1tz9913p6mpKUnS1NSU5cuXZ8GCBZU1c+bMydq1a7P33ntX1txxxx1ZvXp1Zc2sWbOy4447trtCOwAAAGxqOhTin/vc5/KLX/wiX/nKV/L73/8+11xzTS6//PKMHz8+SVJTU5OJEyfmy1/+cm6++eYsXLgwRx55ZAYOHJhDDz00yT/PoB944IE59thjc8899+TnP/95JkyYkMMOOywDBw5Mkhx++OHp3r17xo0blwcffDDXXXddvva1r+X4449/dY8eAAAACuvQ7cve8pa35Pvf/35OOumknHHGGRk8eHAuuuiijB07trLmi1/8Yp599tkcd9xxWb58ed7+9rfn1ltvTY8ePSprrr766kyYMCHDhw9Ply5dMmbMmFx88cWV/fX19bn99tszfvz47Lnnntlqq60yefJkty4DAABgk1fT1tbWVu0hNobW1tbU19enpaXF98VfI7abdEu1RwBewh/PGV3tEYAX4T0UOjfvoa8dL7dDO/TRdAAAAOCVEeIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKekUhfs4556SmpiYTJ06sbHvuuecyfvz49OvXL717986YMWOyZMmSds97/PHHM3r06Gy++ebp379/TjjhhDz//PPt1vzsZz/LHnvskdra2gwZMiTTp09/JaMCAABAp7DBIX7vvffmG9/4Rt785je32/65z30uP/zhD3PDDTdk7ty5efLJJ/OBD3ygsn/NmjUZPXp0Vq1alXnz5mXGjBmZPn16Jk+eXFnz2GOPZfTo0XnXu96V+++/PxMnTswnPvGJ3HbbbRs6LgAAAHQKGxTiK1asyNixY/PNb34zW2yxRWV7S0tLpk2blgsuuCDvfve7s+eee+bKK6/MvHnz8otf/CJJcvvtt+e3v/1trrrqquy222456KCDcuaZZ+bSSy/NqlWrkiRTp07N4MGDc/7552ennXbKhAkT8sEPfjAXXnjhq3DIAAAAUD0bFOLjx4/P6NGjM2LEiHbbFyxYkNWrV7fbPnTo0GyzzTaZP39+kmT+/PnZZZdd0tDQUFkzatSotLa25sEHH6ys+ffXHjVqVOU11mflypVpbW1t9wAAAIDOpmtHn3Dttdfmvvvuy7333vuCfc3NzenevXv69u3bbntDQ0Oam5sra/41wtftX7fvpda0trbmH//4R3r27PmC33322Wfn9NNP7+jhAAAAQFEdOiO+ePHi/L//9/9y9dVXp0ePHhtrpg1y0kknpaWlpfJYvHhxtUcCAACAF+hQiC9YsCBLly7NHnvska5du6Zr166ZO3duLr744nTt2jUNDQ1ZtWpVli9f3u55S5YsSWNjY5KksbHxBVdRX/fzf1pTV1e33rPhSVJbW5u6urp2DwAAAOhsOhTiw4cPz8KFC3P//fdXHnvttVfGjh1b+XO3bt0ye/bsynMWLVqUxx9/PE1NTUmSpqamLFy4MEuXLq2smTVrVurq6jJs2LDKmn99jXVr1r0GAAAAbKo69B3xPn36ZOedd263rVevXunXr19l+7hx43L88cdnyy23TF1dXT772c+mqakp++yzT5Jk5MiRGTZsWI444ohMmTIlzc3NOfnkkzN+/PjU1tYmST71qU/lkksuyRe/+MUcc8wxmTNnTq6//vrccsstr8YxAwAAQNV0+GJt/8mFF16YLl26ZMyYMVm5cmVGjRqV//3f/63s32yzzTJz5sx8+tOfTlNTU3r16pWjjjoqZ5xxRmXN4MGDc8stt+Rzn/tcvva1r2XrrbfOt771rYwaNerVHhcAAACKqmlra2ur9hAbQ2tra+rr69PS0uL74q8R203yiQjozP54zuhqjwC8CO+h0Ll5D33teLkdukH3EQcAAAA2jBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgjoU4meffXbe8pa3pE+fPunfv38OPfTQLFq0qN2a5557LuPHj0+/fv3Su3fvjBkzJkuWLGm35vHHH8/o0aOz+eabp3///jnhhBPy/PPPt1vzs5/9LHvssUdqa2szZMiQTJ8+fcOOEAAAADqRDoX43LlzM378+PziF7/IrFmzsnr16owcOTLPPvtsZc3nPve5/PCHP8wNN9yQuXPn5sknn8wHPvCByv41a9Zk9OjRWbVqVebNm5cZM2Zk+vTpmTx5cmXNY489ltGjR+dd73pX7r///kycODGf+MQnctttt70KhwwAAADVU9PW1ta2oU9+6qmn0r9//8ydOzf77bdfWlpa8rrXvS7XXHNNPvjBDyZJHnrooey0006ZP39+9tlnn/z4xz/OwQcfnCeffDINDQ1JkqlTp+bEE0/MU089le7du+fEE0/MLbfckt/85jeV33XYYYdl+fLlufXWW1/WbK2tramvr09LS0vq6uo29BDpRLabdEu1RwBewh/PGV3tEYAX4T0UOjfvoa8dL7dDX9F3xFtaWpIkW265ZZJkwYIFWb16dUaMGFFZM3To0GyzzTaZP39+kmT+/PnZZZddKhGeJKNGjUpra2sefPDBypp/fY11a9a9xvqsXLkyra2t7R4AAADQ2WxwiK9duzYTJ07M2972tuy8885Jkubm5nTv3j19+/Ztt7ahoSHNzc2VNf8a4ev2r9v3UmtaW1vzj3/8Y73znH322amvr688Bg0atKGHBgAAABvNBof4+PHj85vf/CbXXnvtqznPBjvppJPS0tJSeSxevLjaIwEAAMALdN2QJ02YMCEzZ87MHXfcka233rqyvbGxMatWrcry5cvbnRVfsmRJGhsbK2vuueeedq+37qrq/7rm36+0vmTJktTV1aVnz57rnam2tja1tbUbcjgAAABQTIfOiLe1tWXChAn5/ve/nzlz5mTw4MHt9u+5557p1q1bZs+eXdm2aNGiPP7442lqakqSNDU1ZeHChVm6dGllzaxZs1JXV5dhw4ZV1vzra6xbs+41AAAAYFPVoTPi48ePzzXXXJMf/OAH6dOnT+U73fX19enZs2fq6+szbty4HH/88dlyyy1TV1eXz372s2lqaso+++yTJBk5cmSGDRuWI444IlOmTElzc3NOPvnkjB8/vnJG+1Of+lQuueSSfPGLX8wxxxyTOXPm5Prrr88tt7jiJwAAAJu2Dp0Rv+yyy9LS0pJ3vvOdGTBgQOVx3XXXVdZceOGFOfjggzNmzJjst99+aWxszI033ljZv9lmm2XmzJnZbLPN0tTUlI997GM58sgjc8YZZ1TWDB48OLfccktmzZqVXXfdNeeff36+9a1vZdSoUa/CIQMAAED1vKL7iHdm7iP+2uMeqNC5uQcqdF7eQ6Fz8x762lHkPuIAAABAxwhxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABQlxAAAAKEiIAwAAQEFCHAAAAAoS4gAAAFCQEAcAAICChDgAAAAUJMQBAACgICEOAAAABXXqEL/00kuz3XbbpUePHtl7771zzz33VHskAAAAeEU6bYhfd911Of7443Pqqafmvvvuy6677ppRo0Zl6dKl1R4NAAAANlinDfELLrggxx57bI4++ugMGzYsU6dOzeabb54rrrii2qMBAADABuuUIb5q1aosWLAgI0aMqGzr0qVLRowYkfnz51dxMgAAAHhlulZ7gPX561//mjVr1qShoaHd9oaGhjz00EPrfc7KlSuzcuXKys8tLS1JktbW1o03KEWtXfn3ao8AvAT/fwudl/dQ6Ny8h752rPvvsq2t7SXXdcoQ3xBnn312Tj/99BdsHzRoUBWmAfjvU39RtScAgE2T99DXnmeeeSb19fUvur9ThvhWW22VzTbbLEuWLGm3fcmSJWlsbFzvc0466aQcf/zxlZ/Xrl2bZcuWpV+/fqmpqdmo8wId09ramkGDBmXx4sWpq6ur9jgAsMnwHgqdW1tbW5555pkMHDjwJdd1yhDv3r179txzz8yePTuHHnpokn+G9ezZszNhwoT1Pqe2tja1tbXttvXt23cjTwq8EnV1df4SAQAbwHsodF4vdSZ8nU4Z4kly/PHH56ijjspee+2Vt771rbnooovy7LPP5uijj672aAAAALDBOm2If+QjH8lTTz2VyZMnp7m5ObvttltuvfXWF1zADQAAADYlnTbEk2TChAkv+lF0YNNVW1ubU0899QVfJwEAXpr3UHhtqGn7T9dVBwAAAF41Xao9AAAAAPw3EeIAAABQkBAHAACAgoQ4AAAAFCTEAQAAoKBOffsy4LXl29/+dj7ykY+84JYrq1atyrXXXpsjjzyySpMBQOdy8803v+y1hxxyyEacBNgY3L4MKGazzTbLX/7yl/Tv37/d9r/97W/p379/1qxZU6XJAKBz6dKl/QdXa2pq8q9/ba+pqan82fsnbHp8NB0opq2trd1fHNb585//nPr6+ipMBACd09q1ayuP22+/Pbvttlt+/OMfZ/ny5Vm+fHl+9KMfZY899sitt95a7VGBDeCj6cBGt/vuu6empiY1NTUZPnx4unb9v//rWbNmTR577LEceOCBVZwQADqviRMnZurUqXn7299e2TZq1KhsvvnmOe644/K73/2uitMBG0KIAxvdoYcemiS5//77M2rUqPTu3buyr3v37tluu+0yZsyYKk0HAJ3bo48+mr59+75ge319ff74xz8Wnwd45XxHHChmxowZ+chHPpIePXpUexQA2GTst99+6dGjR77zne+koaEhSbJkyZIceeSRee655zJ37twqTwh0lBAHilu1alWWLl2atWvXttu+zTbbVGkiAOi8fv/73+f9739/Hn744QwaNChJsnjx4uywww656aabMmTIkCpPCHSUEAeKeeSRR3LMMcdk3rx57bavu4ibq74CwPq1tbVl1qxZeeihh5IkO+20U0aMGLHei6ACnZ8QB4p529velq5du2bSpEkZMGDAC/7ysOuuu1ZpMgDYNDz33HOpra0V4LCJE+JAMb169cqCBQsydOjQao8CAJuMtWvX5qyzzsrUqVOzZMmSPPzww3nDG96QU045Jdttt13GjRtX7RGBDnIfcaCYYcOG5a9//Wu1xwCATcqXv/zlTJ8+PVOmTEn37t0r23feeed861vfquJkwIZyRhzYqFpbWyt//uUvf5mTTz45X/nKV7LLLrukW7du7dbW1dWVHg8AOr0hQ4bkG9/4RoYPH54+ffrk17/+dd7whjfkoYceSlNTU55++ulqjwh0kPuIAxtV3759232Pra2tLcOHD2+3xsXaAODFPfHEE+u9MvratWuzevXqKkwEvFJCHNiofvrTn1Z7BADYpA0bNix33nlntt1223bbv/vd72b33Xev0lTAKyHEgY1q//33r/YIALBJmzx5co466qg88cQTWbt2bW688cYsWrQo3/72tzNz5sxqjwdsAN8RB4p54IEH1ru9pqYmPXr0yDbbbJPa2trCUwFA53fnnXfmjDPOyK9//eusWLEie+yxRyZPnpyRI0dWezRgAwhxoJguXbq85H1Pu3Xrlo985CP5xje+kR49ehScDAAAynH7MqCY73//+9lhhx1y+eWX5/7778/999+fyy+/PDvuuGOuueaaTJs2LXPmzMnJJ59c7VEBAGCjcUYcKOatb31rzjzzzIwaNard9ttuuy2nnHJK7rnnntx00035/Oc/n0cffbRKUwJA9W2xxRYv+Smyf7Vs2bKNPA3wanOxNqCYhQsXvuCKr0my7bbbZuHChUmS3XbbLX/5y19KjwYAncpFF11U7RGAjUiIA8UMHTo055xzTi6//PJ07949SbJ69eqcc845GTp0aJJ/3iu1oaGhmmMCQNUdddRR1R4B2IiEOFDMpZdemkMOOSRbb7113vzmNyf551nyNWvWVG6/8oc//CGf+cxnqjkmAFRda2vry15bV1e3EScBNgbfEQeKeuaZZ3L11Vfn4YcfTpLsuOOOOfzww9OnT58qTwYAncd/utNIkrS1taWmpiZr1qwpNBXwahHiAADQycydO/dlr91///034iTAxiDEgY3q5ptvzkEHHZRu3brl5ptvfsm1hxxySKGpAACgeoQ4sFF16dIlzc3N6d+/f7p06fKi63y0DgD+zwMPPJCdd945Xbp0yQMPPPCSa9dddwXYdAhxAADoZP79H7Jramqyvr+2+4ds2DS5ajpQxOrVq3PggQdm6tSp2WGHHao9DgB0ao899lhe97rXVf4MvLYIcaCIbt26/ceP1gEA/7Ttttuu98/Aa8OLf2ET4FX2sY99LNOmTav2GACwyVm0aFEmTJiQ4cOHZ/jw4ZkwYUIWLVpU7bGADeSMOFDM888/nyuuuCI/+clPsueee6ZXr17t9l9wwQVVmgwAOq/vfe97Oeyww7LXXnulqakpSfKLX/wiO++8c6699tqMGTOmyhMCHeVibUAx73rXu150X01NTebMmVNwGgDYNGy//fYZO3ZszjjjjHbbTz311Fx11VV59NFHqzQZsKGEOAAAdGKbb755HnjggQwZMqTd9kceeSS77rpr/v73v1dpMmBD+Y44UBWLFy/O4sWLqz0GAHR673znO3PnnXe+YPtdd92Vd7zjHVWYCHilfEccKOb555/P6aefnosvvjgrVqxIkvTu3Tuf/exnc+qpp6Zbt25VnhAAOp9DDjkkJ554YhYsWJB99tknyT+/I37DDTfk9NNPz80339xuLdD5+Wg6UMynP/3p3HjjjTnjjDMqF5uZP39+TjvttBx66KG57LLLqjwhAHQ+Xbq8vA+x1tTUZM2aNRt5GuDVIMSBYurr63PttdfmoIMOarf9Rz/6UT760Y+mpaWlSpMBAEA5viMOFFNbW5vtttvuBdsHDx6c7t27lx8IADqx+fPnZ+bMme22ffvb387gwYPTv3//HHfccVm5cmWVpgNeCSEOFDNhwoSceeaZ7f7SsHLlypx11lmZMGFCFScDgM7njDPOyIMPPlj5eeHChRk3blxGjBiRSZMm5Yc//GHOPvvsKk4IbCgfTQeKef/735/Zs2entrY2u+66a5Lk17/+dVatWpXhw4e3W3vjjTdWY0QA6DQGDBiQH/7wh9lrr72SJF/60pcyd+7c3HXXXUmSG264Iaeeemp++9vfVnNMYAO4ajpQTN++fTNmzJh22wYNGlSlaQCgc3v66afT0NBQ+Xnu3LntrrPylre8xa1AYRMlxIFirrzyymqPAACbjIaGhjz22GMZNGhQVq1alfvuuy+nn356Zf8zzzzj1p+wiRLiwEa3xRZbpKam5gXb6+vr88Y3vjFf+MIXcsABB1RhMgDovN7znvdk0qRJOffcc3PTTTdl8803zzve8Y7K/gceeCDbb799FScENpQQBza6iy66aL3bly9fngULFuTggw/Od7/73bz3ve8tOxgAdGJnnnlmPvCBD2T//fdP7969M2PGjHZ3GbniiisycuTIKk4IbCgXawOq7oILLsh3v/vdzJs3r9qjAECn09LSkt69e2ezzTZrt33ZsmXp3bu3W4DCJkiIA1X38MMPZ5999smyZcuqPQoAAGx07iMOVN3KlSv9az4AAP81hDhQddOmTctuu+1W7TEAAKAIF2sDNrrjjz9+vdtbWlpy33335eGHH84dd9xReCoAAKgOIQ5sdL/61a/Wu72uri4HHHBAbrzxxgwePLjwVAAAUB0u1gYAAAAF+Y44AAAAFCTEAQAAoCAhDgAAAAUJcQAAAChIiAMAAEBBQhwAAAAKEuIAAABQkBAHAACAgv4/9lpEPxDAz0IAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(f'Audience Status Category: {df_movie.audience_status.unique()}')\n",
    "\n",
    "ax = df_movie.audience_status.value_counts().plot(kind='bar', figsize=(12,9))\n",
    "ax.bar_label(ax.containers[0])"
   ]
  },
  {
   "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>audience_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   audience_status\n",
       "0              0.0\n",
       "1              1.0\n",
       "2              0.0\n",
       "3              1.0\n",
       "4              1.0"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ordinal coding for audience status\n",
    "audience_status = pd.DataFrame(df_movie.audience_status.replace(['Spilled', 'Upright'], [0,1]))\n",
    "audience_status.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note: You can use the `.replace()` method in pandas DataFrame to encode categorical values of a feature into numerical values**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   tomatometer_status\n",
       "0                 0.0\n",
       "1                 2.0\n",
       "2                 1.0\n",
       "3                 2.0\n",
       "4                 1.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Encode tomatometer status with ordinal encoding\n",
    "tomatometer_status = pd.DataFrame(df_movie.tomatometer_status.replace(['Rotten', 'Fresh', 'Certified-Fresh'], [0,1,2]))\n",
    "tomatometer_status.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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>runtime</th>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <th>tomatometer_count</th>\n",
       "      <th>audience_rating</th>\n",
       "      <th>audience_count</th>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "      <th>G</th>\n",
       "      <th>NC17</th>\n",
       "      <th>NR</th>\n",
       "      <th>PG</th>\n",
       "      <th>PG-13</th>\n",
       "      <th>R</th>\n",
       "      <th>audience_status</th>\n",
       "      <th>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>119.0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>149.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>254421.0</td>\n",
       "      <td>43</td>\n",
       "      <td>73</td>\n",
       "      <td>76</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>11574.0</td>\n",
       "      <td>44</td>\n",
       "      <td>123</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>122.0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>53.0</td>\n",
       "      <td>14684.0</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>95.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>105386.0</td>\n",
       "      <td>6</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>127.0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>68918.0</td>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   runtime  tomatometer_rating  tomatometer_count  audience_rating  \\\n",
       "0    119.0                49.0              149.0             53.0   \n",
       "1     90.0                87.0              142.0             64.0   \n",
       "2    122.0                67.0               24.0             53.0   \n",
       "3     95.0               100.0               54.0             97.0   \n",
       "4    127.0                89.0               27.0             74.0   \n",
       "\n",
       "   audience_count  tomatometer_top_critics_count  \\\n",
       "0        254421.0                             43   \n",
       "1         11574.0                             44   \n",
       "2         14684.0                              2   \n",
       "3        105386.0                              6   \n",
       "4         68918.0                              5   \n",
       "\n",
       "   tomatometer_fresh_critics_count  tomatometer_rotten_critics_count  G  NC17  \\\n",
       "0                               73                                76  0     0   \n",
       "1                              123                                19  0     0   \n",
       "2                               16                                 8  0     0   \n",
       "3                               54                                 0  0     0   \n",
       "4                               24                                 3  1     0   \n",
       "\n",
       "   NR  PG  PG-13  R  audience_status  tomatometer_status  \n",
       "0   0   1      0  0              0.0                 0.0  \n",
       "1   0   0      0  1              1.0                 2.0  \n",
       "2   0   0      0  1              0.0                 1.0  \n",
       "3   1   0      0  0              1.0                 2.0  \n",
       "4   0   0      0  0              1.0                 1.0  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Combine all features into one dataframe. Drop NULL values\n",
    "df_feature = pd.concat([df_movie[['runtime', 'tomatometer_rating', 'tomatometer_count', 'audience_rating', 'audience_count', \\\n",
    "    'tomatometer_top_critics_count', 'tomatometer_fresh_critics_count', 'tomatometer_rotten_critics_count']], \\\n",
    "        content_rating, audience_status, tomatometer_status], axis=1).dropna()\n",
    "df_feature.head()"
   ]
  },
  {
   "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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>runtime</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>102.296233</td>\n",
       "      <td>1.865365e+01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>266.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomatometer_rating</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>60.519539</td>\n",
       "      <td>2.843640e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomatometer_count</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>57.097197</td>\n",
       "      <td>6.681458e+01</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>535.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audience_rating</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>60.324499</td>\n",
       "      <td>2.048060e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audience_count</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>147118.274843</td>\n",
       "      <td>1.783955e+06</td>\n",
       "      <td>5.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>4423.0</td>\n",
       "      <td>26067.0</td>\n",
       "      <td>35797635.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomatometer_top_critics_count</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>14.765999</td>\n",
       "      <td>1.517226e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>69.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomatometer_fresh_critics_count</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>36.237292</td>\n",
       "      <td>5.136245e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>497.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomatometer_rotten_critics_count</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>20.871188</td>\n",
       "      <td>2.997890e+01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>G</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.039314</td>\n",
       "      <td>1.943458e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NC17</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.002174</td>\n",
       "      <td>4.658000e-02</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NR</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.294940</td>\n",
       "      <td>4.560293e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.124993</td>\n",
       "      <td>3.307203e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG-13</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.171593</td>\n",
       "      <td>3.770375e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>R</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.366986</td>\n",
       "      <td>4.819968e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>audience_status</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.542928</td>\n",
       "      <td>4.981684e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tomatometer_status</th>\n",
       "      <td>17017.0</td>\n",
       "      <td>0.752718</td>\n",
       "      <td>7.472500e-01</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    count           mean           std  min  \\\n",
       "runtime                           17017.0     102.296233  1.865365e+01  5.0   \n",
       "tomatometer_rating                17017.0      60.519539  2.843640e+01  0.0   \n",
       "tomatometer_count                 17017.0      57.097197  6.681458e+01  5.0   \n",
       "audience_rating                   17017.0      60.324499  2.048060e+01  0.0   \n",
       "audience_count                    17017.0  147118.274843  1.783955e+06  5.0   \n",
       "tomatometer_top_critics_count     17017.0      14.765999  1.517226e+01  0.0   \n",
       "tomatometer_fresh_critics_count   17017.0      36.237292  5.136245e+01  0.0   \n",
       "tomatometer_rotten_critics_count  17017.0      20.871188  2.997890e+01  0.0   \n",
       "G                                 17017.0       0.039314  1.943458e-01  0.0   \n",
       "NC17                              17017.0       0.002174  4.658000e-02  0.0   \n",
       "NR                                17017.0       0.294940  4.560293e-01  0.0   \n",
       "PG                                17017.0       0.124993  3.307203e-01  0.0   \n",
       "PG-13                             17017.0       0.171593  3.770375e-01  0.0   \n",
       "R                                 17017.0       0.366986  4.819968e-01  0.0   \n",
       "audience_status                   17017.0       0.542928  4.981684e-01  0.0   \n",
       "tomatometer_status                17017.0       0.752718  7.472500e-01  0.0   \n",
       "\n",
       "                                    25%     50%      75%         max  \n",
       "runtime                            90.0    99.0    111.0       266.0  \n",
       "tomatometer_rating                 38.0    66.0     86.0       100.0  \n",
       "tomatometer_count                  12.0    29.0     76.0       535.0  \n",
       "audience_rating                    44.0    62.0     77.0       100.0  \n",
       "audience_count                    741.0  4423.0  26067.0  35797635.0  \n",
       "tomatometer_top_critics_count       3.0     8.0     23.0        69.0  \n",
       "tomatometer_fresh_critics_count     6.0    17.0     44.0       497.0  \n",
       "tomatometer_rotten_critics_count    3.0     8.0     24.0       303.0  \n",
       "G                                   0.0     0.0      0.0         1.0  \n",
       "NC17                                0.0     0.0      0.0         1.0  \n",
       "NR                                  0.0     0.0      1.0         1.0  \n",
       "PG                                  0.0     0.0      0.0         1.0  \n",
       "PG-13                               0.0     0.0      0.0         1.0  \n",
       "R                                   0.0     0.0      1.0         1.0  \n",
       "audience_status                     0.0     1.0      1.0         1.0  \n",
       "tomatometer_status                  0.0     1.0      1.0         2.0  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_feature.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '7375'), Text(0, 0, '6475'), Text(0, 0, '3167')]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get the distribution of the target variable\n",
    "ax = df_feature.tomatometer_status.value_counts().plot(kind='bar', figsize=(12,9))\n",
    "ax.bar_label(ax.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a class imbalance problem, need to address this later.\n",
    "\n",
    "- Let us now create train and test splits of the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training data is 13613 and the size of the test data is 3404\n"
     ]
    }
   ],
   "source": [
    "# Split the data into train and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_feature.drop(['tomatometer_status'], axis=1), \\\n",
    "    df_feature['tomatometer_status'], test_size=0.2, random_state=42)\n",
    "\n",
    "print(f'Size of training data is {len(X_train)} and the size of the test data is {len(X_test)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Classifier\n",
    "- Constrained to 3 leaf nodes because we have 3 classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9462397179788484\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00      1488\n",
      "         1.0       0.97      0.88      0.93      1286\n",
      "         2.0       0.80      0.95      0.87       630\n",
      "\n",
      "    accuracy                           0.95      3404\n",
      "   macro avg       0.92      0.94      0.93      3404\n",
      "weighted avg       0.95      0.95      0.95      3404\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17f9ba200>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize the tree classifier\n",
    "tree_3_leaf = DecisionTreeClassifier(max_leaf_nodes=3, random_state=2)\n",
    "\n",
    "# Train the classifier on training data\n",
    "tree_3_leaf.fit(X_train, y_train)\n",
    "\n",
    "# Predict the test data with the trained model\n",
    "y_predict = tree_3_leaf.predict(X_test)\n",
    "\n",
    "# Print accuracy and classification report on test data\n",
    "print(accuracy_score(y_test, y_predict))\n",
    "print(classification_report(y_test, y_predict))\n",
    "\n",
    "# Plot confusion matrix on test data\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_confusion_matrix(tree_3_leaf, X_test, y_test, cmap = 'YlGn', ax=ax)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the decision logic of the decision tree model\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_tree(tree_3_leaf, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- X[1] is the `tomatometer_rating` feature\n",
    "- X[6] is the `tomatometer_fresh_critics_count` feature\n",
    "\n",
    "# Decision Tree Unconstrained"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9900117508813161\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00      1488\n",
      "         1.0       0.99      0.99      0.99      1286\n",
      "         2.0       0.97      0.97      0.97       630\n",
      "\n",
      "    accuracy                           0.99      3404\n",
      "   macro avg       0.99      0.99      0.99      3404\n",
      "weighted avg       0.99      0.99      0.99      3404\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17faf2770>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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DxkkbOFAvvPOOXnn3XW3d/Y3unD5NxSUlGpzQx+nQgDqNzyZQ9zha0ZOktLQ0JScnq1u3bjr//PM1depUFRcXa/DgwZKkm2++WaeeeqrS09MlScOGDVOPHj305JNPqn///lqwYIE2bNig559/3snHwEnodmZbffDEk97XT91xpyRpzrvLNfiJ3/9unnR41c0+fx+tRwffqpWTnlD9evW05ZtvNGDcWH329deSpJw9e3Tl2If08I03K3PadLndbm3asV19/j5aufn5/nkwoA66rmcvfV9QoLFz5yj3wAF1bnWGlj2aftQiEADsxWcTNZHjK18a3rtpeX7dy+aQp59+WpMnT1Zubq46d+6s6dOnq3v37pKknj176vTTT9ecOXO84xcuXKiHHnpIu3btUps2bTRp0iT169evUnMVFhYqLCxM6nWmFMBG2UBN4nn3PadDAACgVigs/FFhYe1UUFCg0NBQp8OpkiO/j5+SeoGsIOfqTp7SQyp6+uNa+TOsjBqR6NmJRA+ouUj0AACoHBK9k2d6oud46yYAAACAusfxlS/N7tw0f3sFAAAAAKhrqOgBAAAAsJ1LzhbVTP/+GhU9AAAAADAMiR4AAAAAGIbWTQAAAAC2Yx89/6KiBwAAAACGIdEDAAAAAMOQ6AEAAACw3ZF99Jw8qmr16tW68sorFRMTI8uytHjxYu+18vJyjRw5Uh07dlSjRo0UExOjm2++WXv37vV5j/z8fCUlJSk0NFTh4eEaMmSIioqKfMZ89tlnuuSSSxQcHKzY2FhNmjSpyrGS6AEAAABAJRQXF6tTp06aOXPmUdd++uknbdy4UWPGjNHGjRv15ptvKicnR1dddZXPuKSkJG3ZskUZGRlasmSJVq9erdtvv917vbCwUL1791aLFi2UlZWlyZMna9y4cXr++eerFCuLsQAAAACw3wlW1aqL5wTm7tu3r/r27XvMa2FhYcrIyPA59/TTT+v888/X7t271bx5c23dulXLli3TJ598om7dukmSZsyYoX79+umJJ55QTEyM5s2bp7KyMr388ssKDAzUWWedpezsbE2ZMsUnIfwjVPQAAAAA1FmFhYU+R2lpabW9d0FBgSzLUnh4uCQpMzNT4eHh3iRPkuLj4+VyubRu3TrvmEsvvVSBgYHeMQkJCcrJydGBAwcqPTeJHgAAAIA6KzY2VmFhYd4jPT29Wt63pKREI0eO1PXXX6/Q0FBJUm5uriIjI33GBQQEKCIiQrm5ud4xUVFRPmOOvD4ypjJo3QQAAABgO6f30Tsy9549e7yJmCQFBQWd9HuXl5fr2muvlcfj0bPPPnvS73ciSPQAAAAA1FmhoaE+id7JOpLkffPNN1q5cqXPe0dHR2v//v0+4w8dOqT8/HxFR0d7x+Tl5fmMOfL6yJjKoHUTAAAAAKrBkSTvq6++0nvvvacmTZr4XI+Li9PBgweVlZXlPbdy5Uq53W51797dO2b16tUqLy/3jsnIyFDbtm3VuHHjSsdCogcAAADAdk7voXciXaNFRUXKzs5Wdna2JGnnzp3Kzs7W7t27VV5err/+9a/asGGD5s2bp4qKCuXm5io3N1dlZWWSpPbt26tPnz4aOnSo1q9fr48++kipqakaNGiQYmJiJEk33HCDAgMDNWTIEG3ZskWvvfaapk2bprS0tCrFSusmAAAAAFTChg0b1KtXL+/rI8lXcnKyxo0bp7feekuS1LlzZ5/73n//ffXs2VOSNG/ePKWmpuryyy+Xy+XSwIEDNX36dO/YsLAwvfvuu0pJSVHXrl3VtGlTjR07tkpbK0gkegAAAAAcUFMWY6mKnj17yuPxHPf67107IiIiQvPnz//dMeecc47WrFlT5fh+jdZNAAAAADAMiR4AAAAAGIbWTQAAAAC2O9EFUapzfpNR0QMAAAAAw5DoAQAAAIBhaN0EAAAAYDvrl8PJ+U1GRQ8AAAAADEOiBwAAAACGoXUTAAAAgO1q44bptQkVPQAAAAAwDBU9AAAAALZjHz3/oqIHAAAAAIYh0QMAAAAAw9C6CQAAAMB2Luvw4RhaNwEAAAAAtQmJHgAAAAAYhtZNAAAAALZjHz3/oqIHAAAAAIahogcAAADAdpYc3kfPualtQUUPAAAAAAxDogcAAAAAhqF1EwAAAIDtWIzFv6joAQAAAIBhSPQAAAAAwDC0bgIAAACwnWU5vOqm2Z2bVPQAAAAAwDQkegAAAABgGFo3AQAAANjOkrOblhveuUlFDwAAAABMQ0UPAAAAgO3YR8+/qOgBAAAAgGFI9AAAAADAMLRuAgAAALAd++j5FxU9AAAAADAMiR4AAAAAGIbWTQAAAAC2s1yWLJeDq246OLcdqOgBAAAAgGGo6AEAAACwn8P76Jm+GgsVPQAAAAAwDIkeAAAAABiG1k0AAAAAtrMcbt10tG3UBlT0AAAAAMAwJHoAAAAAYBhaNwEAAADYzrKcXfjS8M5NKnoAAAAAYBoSPQAAAAAwDK2bAAAAAGzHqpv+RUUPAAAAAAxDRQ8AAACA7SyXJcvlYEXPwbntQEUPAAAAAAxDogcAAAAAhqF1EwAAAIDtDu+j5+RiLI5NbQsqegAAAABgGBI9AAAAADAMrZsAAAAAbMc+ev5FRQ8AAAAADENFDwAAAIDtqOj5FxU9AAAAADAMiR4AAAAAGIbWTQAAAAC2s1yHDyfnN5nhjwcAAAAAdQ+JHgAAAAAYhtZNAAAAALaz5PCqm2LVTQAAAABALUJFDwAAAIDt2EfPv6joAQAAAIBhSPQAAAAAwDC0bgIAAACwn2UdPpyc32BU9AAAAADAMCR6AAAAAGAYWjcBAAAA2I5VN/2Lih4AAAAAGIZEDwAAAAAMQ+smAAAAANtZrsOHk/ObzPDHAwAAAIC6h4oeAAAAANuxGIt/UdEDAAAAAMOQ6AEAAACAYWjdBAAAAGA7Wjf9i4oeAAAAABiGRA8AAAAADEPrJgAAAADb0brpX1T0AAAAAKASVq9erSuvvFIxMTGyLEuLFy/2ue7xeDR27Fg1a9ZMDRo0UHx8vL766iufMfn5+UpKSlJoaKjCw8M1ZMgQFRUV+Yz57LPPdMkllyg4OFixsbGaNGlSlWMl0QMAAABgO8tlOX5UVXFxsTp16qSZM2ce8/qkSZM0ffp0zZo1S+vWrVOjRo2UkJCgkpIS75ikpCRt2bJFGRkZWrJkiVavXq3bb7/de72wsFC9e/dWixYtlJWVpcmTJ2vcuHF6/vnnqxQrrZsAAAAAUAl9+/ZV3759j3nN4/Fo6tSpeuihhzRgwABJ0ty5cxUVFaXFixdr0KBB2rp1q5YtW6ZPPvlE3bp1kyTNmDFD/fr10xNPPKGYmBjNmzdPZWVlevnllxUYGKizzjpL2dnZmjJlik9C+Eeo6AEAAACoswoLC32O0tLSE3qfnTt3Kjc3V/Hx8d5zYWFh6t69uzIzMyVJmZmZCg8P9yZ5khQfHy+Xy6V169Z5x1x66aUKDAz0jklISFBOTo4OHDhQ6XhI9AAAAADY7shiLE4ekhQbG6uwsDDvkZ6efkLPk5ubK0mKioryOR8VFeW9lpubq8jISJ/rAQEBioiI8BlzrPf49RyVQesmAAAAgDprz549Cg0N9b4OCgpyMJrqQ0UPAAAAQJ0VGhrqc5xoohcdHS1JysvL8zmfl5fnvRYdHa39+/f7XD906JDy8/N9xhzrPX49R2WQ6AEAAACwnyVZDh6q5m30WrZsqejoaK1YscJ7rrCwUOvWrVNcXJwkKS4uTgcPHlRWVpZ3zMqVK+V2u9W9e3fvmNWrV6u8vNw7JiMjQ23btlXjxo0rHU+dbd0sWPyWQkNDnA4DwK8E3j3Q6RAAHEPp9NedDgHAb3g8FU6HUCcVFRVp+/bt3tc7d+5Udna2IiIi1Lx5cw0fPlyPPPKI2rRpo5YtW2rMmDGKiYlRYmKiJKl9+/bq06ePhg4dqlmzZqm8vFypqakaNGiQYmJiJEk33HCDxo8fryFDhmjkyJHavHmzpk2bpqeeeqpKsdbZRA8AAAAAqmLDhg3q1auX93VaWpokKTk5WXPmzNEDDzyg4uJi3X777Tp48KAuvvhiLVu2TMHBwd575s2bp9TUVF1++eVyuVwaOHCgpk+f7r0eFhamd999VykpKeratauaNm2qsWPHVmlrBUmyPB6P5ySft1YpLCxUWFiYCgq2UdEDahgqekDNREUPqHkKC39UePhZKigo8FlIpDY48vt4t2cSFdCgvmNxHPq5XBvuWlwrf4aVwXf0AAAAAMAwtG4CAAAAsN2v97Jzan6TUdEDAAAAAMOQ6AEAAACAYWjdBAAAAGA7y2XJcjnYuung3HagogcAAAAAhiHRAwAAAADD0LoJAAAAwHasuulfVPQAAAAAwDBU9AAAAADYzpLkZFHN7HoeFT0AAAAAMA6JHgAAAAAYhtZNAAAAALZjMRb/oqIHAAAAAIYh0QMAAAAAw9C6CQAAAMB2lsuS5XKwddPBue1ARQ8AAAAADENFDwAAAIDtWIzFv6joAQAAAIBhSPQAAAAAwDC0bgIAAACwnyXJyfZJszs3qegBAAAAgGlI9AAAAADAMLRuAgAAALCdZTncuUnrJgAAAACgNiHRAwAAAADD0LoJAAAAwHYuy5LLwf5JJ+e2AxU9AAAAADAMFT0AAAAAtrMsS5aDVTUn57YDFT0AAAAAMAyJHgAAAAAYhtZNAAAAALZjMRb/oqIHAAAAAIYh0QMAAAAAw9C6CQAAAMB2tG76FxU9AAAAADAMFT0AAAAAtrMcruixjx4AAAAAoFYh0QMAAAAAw9C6CQAAAMB2lmU52j5J6yYAAAAAoFYh0QMAAAAAw9C6CQAAAMB2LllyycF99Byc2w5U9AAAAADAMCR6AAAAAGAYWjcBAAAA2M5lHT6cnN9kVPQAAAAAwDBU9AAAAADYjn30/IuKHgAAAAAYhkQPAAAAAAxD6yYAAAAA27ksSy4H2yednNsOVPQAAAAAwDAkegAAAABgGFo3AQAAANiO1k3/oqIHAAAAAIahogcAAADAdlT0/IuKHgAAAAAYhkQPAAAAAAxD6yYAAAAA21m//HFyfpNR0QMAAAAAw5DoAQAAAIBhaN0EAAAAYDtW3fQvKnoAAAAAYBgqegAAAABs55LDFT0WYwEAAAAA1CYkegAAAABgGFo3AQAAANjOcngxFovFWAAAAAAAtQmJHgAAAAAYhtZNAAAAALazrMOHk/ObjIoeAAAAABiGRA8AAAAADEPrJgAAAADbuRxeddPJue1ARQ8AAAAADENFDwAAAIDtqOj5FxU9AAAAADAMiR4AAAAAGIbWTQAAAAC2syRZcq590uzGTSp6AAAAAGAcEj0AAAAAMEylWjffeuutSr/hVVdddcLBAAAAAKgbWHXTvyqV6CUmJlbqzSzLUkVFxcnEAwAAAAA4SZVq3XS73ZU6SPIAAAAAVMaRip6TR1VUVFRozJgxatmypRo0aKAzzjhDEydOlMfj8Y7xeDwaO3asmjVrpgYNGig+Pl5fffWVz/vk5+crKSlJoaGhCg8P15AhQ1RUVFQtP9NfO6nv6JWUlFRXHAAAAABQYz3++ON69tln9fTTT2vr1q16/PHHNWnSJM2YMcM7ZtKkSZo+fbpmzZqldevWqVGjRkpISPDJm5KSkrRlyxZlZGRoyZIlWr16tW6//fZqj7fKiV5FRYUmTpyoU089Vaeccoq+/vprSdKYMWP00ksvVXuAAAAAAOC0tWvXasCAAerfv79OP/10/fWvf1Xv3r21fv16SYereVOnTtVDDz2kAQMG6JxzztHcuXO1d+9eLV68WJK0detWLVu2TC+++KK6d++uiy++WDNmzNCCBQu0d+/eao23yoneo48+qjlz5mjSpEkKDAz0nj/77LP14osvVmtwAAAAAMzkdNvmkdbNwsJCn6O0tPSY8V544YVasWKFvvzyS0nSp59+qg8//FB9+/aVJO3cuVO5ubmKj4/33hMWFqbu3bsrMzNTkpSZmanw8HB169bNOyY+Pl4ul0vr1q2r3p9vVW+YO3eunn/+eSUlJalevXre8506ddK2bduqNTgAAAAA8KfY2FiFhYV5j/T09GOOGzVqlAYNGqR27dqpfv366tKli4YPH66kpCRJUm5uriQpKirK576oqCjvtdzcXEVGRvpcDwgIUEREhHdMdanUqpu/9t1336l169ZHnXe73SovL6+WoAAAAADADnv27FFoaKj3dVBQ0DHHvf7665o3b57mz5+vs846S9nZ2Ro+fLhiYmKUnJxsV7iVVuVEr0OHDlqzZo1atGjhc/7f//63unTpUm2BAQAAADCXZVmyHNzL7sjcoaGhPone8YwYMcJb1ZOkjh076ptvvlF6erqSk5MVHR0tScrLy1OzZs289+Xl5alz586SpOjoaO3fv9/nfQ8dOqT8/Hzv/dWlyone2LFjlZycrO+++05ut1tvvvmmcnJyNHfuXC1ZsqRagwMAAACAmuCnn36Sy+X7zbd69erJ7XZLklq2bKno6GitWLHCm9gVFhZq3bp1uvPOOyVJcXFxOnjwoLKystS1a1dJ0sqVK+V2u9W9e/dqjbfKid6AAQP09ttva8KECWrUqJHGjh2rc889V2+//bauuOKKag0OAAAAAGqCK6+8Uo8++qiaN2+us846S5s2bdKUKVN06623SjpcIRw+fLgeeeQRtWnTRi1bttSYMWMUExOjxMRESVL79u3Vp08fDR06VLNmzVJ5eblSU1M1aNAgxcTEVGu8VU70JOmSSy5RRkZGtQYCAAAAoO5wWYcPJ+evihkzZmjMmDG66667tH//fsXExOhvf/ubxo4d6x3zwAMPqLi4WLfffrsOHjyoiy++WMuWLVNwcLB3zLx585SamqrLL79cLpdLAwcO1PTp06vrsbwsz6+3cq+CDRs2aOvWrZIOf2/vSOmxpissLFRYWJgKCrYpNDTE6XAA/Erg3QOdDgHAMZROf93pEAD8RmHhjwoPP0sFBQWV+n5ZTXLk9/GhS1MU2OjYC5/Yoay4VC/0n1krf4aVUeWK3rfffqvrr79eH330kcLDwyVJBw8e1IUXXqgFCxbotNNOq+4YAQAAABjGJUsuOVfSc3JuO1R5H73bbrtN5eXl2rp1q/Lz85Wfn6+tW7fK7Xbrtttu80eMAAAAAIAqqHJFb9WqVVq7dq3atm3rPde2bVvNmDFDl1xySbUGBwAAAACouionerGxscfcGL2ioqLaV4oBAAAAYCbLsuSqAfvomarKrZuTJ0/W3XffrQ0bNnjPbdiwQcOGDdMTTzxRrcEBAAAAAKquUhW9xo0b+2S8xcXF6t69uwICDt9+6NAhBQQE6NZbb/XuEQEAAAAAcEalEr2pU6f6OQwAAAAAdYllWY62T5reulmpRC85OdnfcQAAAAAAqkmVF2P5tZKSEpWVlfmcM3GzQQAAAADVy+XwYixOzm2HKi/GUlxcrNTUVEVGRqpRo0Zq3LixzwEAAAAAcFaVE70HHnhAK1eu1LPPPqugoCC9+OKLGj9+vGJiYjR37lx/xAgAAAAAqIIqt26+/fbbmjt3rnr27KnBgwfrkksuUevWrdWiRQvNmzdPSUlJ/ogTAAAAgEFo3fSvKlf08vPz1apVK0mHv4+Xn58vSbr44ou1evXq6o0OAAAAAFBlVa7otWrVSjt37lTz5s3Vrl07vf766zr//PP19ttvKzw83A8hAsc2863/aPLC15Wbn69Orc7QjJRUnd+undNhAca4+IwOuu/yAerSvJViwiL01xce11ufrZckBbjqacKfr1efs85VyyZRKij5SStzPtOD//mn9hUe8Hmfvmedqwf7XKOOMS1Ucqhca7Z/ob++8Lj3eq8zO2pc/0E6O6aFistK9M91H2jMkvmqcLttfV7AJKs//1xPLFyorK++0r78fL358MNKvPBC73VXQsIx73v8tts04ppr7AoTgB9VOdEbPHiwPv30U/Xo0UOjRo3SlVdeqaefflrl5eWaMmWKP2IEjvLaB+8r7blZmnXPMHVv115T33xDCX8fpZyXZiuSRYGAatEoKEiffbdLcz5eoYVDR/pcaxgYpM6xrfTYsn/rs+92KbxhI00ZeKve/NsoxU3+39i/dLpAz15/h8a8PV8ffPm5AurV01nNmnuvn3NqC711x4P6x7tv6NZXZygmPEIzr/ubXC6XRi3me9/AiSouKdE5rVppcEKCBk6YcNT1vf/6l8/r/37yiW576ikNvPhiu0IE2EfPz6qc6N17773ev8fHx2vbtm3KyspS69atdc4551TpvVavXq3JkycrKytL+/bt06JFi5SYmPi793zwwQdKS0vTli1bFBsbq4ceeki33HJLVR8DtdyUN97Q0L79NDihjyRp1rDhWrp+nV5evkyjBl3vcHSAGZZ/sUnLv9h0zGuFJT+p30zfXx6HLXxRmSMmKbZxU+058IPquVx6cuCtGrX4Vc35eIV33Nbcb71/v+bci/T53m/06LKFkqQdP+Rq9H9e1fzBaXrkv6+rqLTED08GmK/veeep73nnHfd6dESEz+u3MjPVq1MntWrWzN+hAbBJlb+j91stWrTQ1VdfXeUkTzq8VUOnTp00c+bMSo3fuXOn+vfvr169eik7O1vDhw/XbbfdpuXLl1d5btReZeXlyvrqS8V3Odd7zuVyKb7Lucrc+oWDkQF1W1iDRnK73Tr4c7EkqUtsK53WuIncHrfWPzBZ3zzyot6680Gd1SzWe09gQH2VHPLdj/Xn8jI1CAzSubFn2Bo/UFflHTigpevX69bjtHMCqJ0qVdGbPn16pd/wnnvuqfTYvn37qm/fvpUeP2vWLLVs2VJPPvmkJKl9+/b68MMP9dRTTymB/3OqM34oLFCF262o37RoRjVurG179jgUFVC3BQXU12NX3ajXsj7UjyU/S5JaNYmSJI3pd50eeHOOduXv172XXaWMeyborIl368BPRcrYmq17evbXdV0v1sKNaxUdGq4H+xz+flCzMNqwATu8kpGhkAYNdDVtm7AZq276V6USvaeeeqpSb2ZZVpUSvarKzMxUfHy8z7mEhAQNHz78uPeUlpaqtLTU+7qwsNBf4QFAnRTgqqd/3XqfLMtS6uvPe88f+Qf0H8vf0KJPP5Yk3Tbvae2c8LwGdonTix9l6L1tn2rU4lf19HW3a/ZN96j0ULkeW/5vXdK6g9weFmMB7DB7+XLdcNllCg4MdDoUANWoUonezp07/R1HpeTm5ioqKsrnXFRUlAoLC/Xzzz+rQYMGR92Tnp6u8ePH2xUibNA0NEz1XC7lHfBd2S/vwAFFR1ABAOx0JMlrHvEn9Z7+sLeaJ0n7Cg9Kkrbm/q/SXnbokHb+X56aN/6T99y099/WtPffVrPQxjrwc7FOj/iTHr3qRn39Q55tzwHUVWs+/1w5336rBX//u9OhoA5yqRq+R3aS85vM9OfT6NGjVVBQ4D320NpX6wXWr6+ubc7UiuyN3nNut1srsjcprn0HByMD6pYjSV7rPzVTn6fHK/+nIp/rG/fsUEl5mc6MPNXnnhYRkfom//uj3m9f4QGVlJfpuq6XaHf+99q0p2b8R0bAZC8vX66ubdqo0xl8JxYwTZVX3XRSdHS08vJ8/wtvXl6eQkNDj1nNk6SgoCAFBQXZER5slDZwoJInT1K3Nm11fru2mvrmmyouKfGuwgng5DUKDFbrP0V7X5/eJFKdTj1d+T8VaV/BAb025H51jm2lvzz3mOpZLkWFhEuS8n8qUnnFIf1Y8rOe//Bdje13nb49+IN253+vtMsHSJLe2LTW+75plw/Qu19sktvjUWKn7hpxRaJumD2F1k3gJBT9/LO2793rfb0zN1fZO3YoIiREzSMjJUmFxcVauHq1nrj9dqfCBOBHtSrRi4uL0zvvvONzLiMjQ3FxcQ5FBKdc17OXvi8o0Ni5c5R74IA6tzpDyx5NP2qBFgAnrmvzM/TesP9tofDE1YMlSXPXva+J77ymK885X5K0YZTvHqrx08Zq9fYtkqRRi+fqkLtCs2+6Rw3qB2r9N18pYcY478qckpTQoYtG9R6ooIAAffbdNxr4wuPH3dYBQOVs+PJLXfbAA97X9z33nCQp+YorNPv++yVJC1atkkfS9b16OREiwD56fmZ5PB6PU5MXFRVp+/btkqQuXbpoypQp6tWrlyIiItS8eXONHj1a3333nebOPbxp7s6dO3X22WcrJSVFt956q1auXKl77rlHS5curfSqm4WFhQoLC1NBwTaFhob47dkAVF3g3QOdDgHAMZROf93pEAD8RmHhjwoPP0sFBQUKDQ11OpwqOfL7+H3v3aegRs513pUWl+rJ+Cdr5c+wMhz9jt6GDRvUpUsXdenSRZKUlpamLl26aOzYsZKkffv2affu3d7xLVu21NKlS5WRkaFOnTrpySef1IsvvsjWCgAAAADwKyfUurlmzRo999xz2rFjh/7973/r1FNP1auvvqqWLVvq4irswdKzZ0/9XkFxzpw5x7xn0yZaegAAAIDajH30/KvKFb033nhDCQkJatCggTZt2uTdo66goECPPfZYtQcIAAAAAKiaKid6jzzyiGbNmqUXXnhB9evX956/6KKLtHHjxt+5EwAAAAAOc0lyWQ4eTv8A/KzKz5eTk6NLL730qPNhYWE6ePBgdcQEAAAAADgJVU70oqOjvStl/tqHH36oVq1aVUtQAAAAAIATV+VEb+jQoRo2bJjWrVsny7K0d+9ezZs3T/fff7/uvPNOf8QIAAAAwDBWDfhjsiqvujlq1Ci53W5dfvnl+umnn3TppZcqKChI999/v+6++25/xAgAAAAAqIIqJ3qWZenBBx/UiBEjtH37dhUVFalDhw465ZRT/BEfAAAAAKCKTmgfPUkKDAxUhw4dqjMWAAAAAHWE5fA+epbh++hVOdHr1avX7/5QVq5ceVIBAQAAAABOTpUTvc6dO/u8Li8vV3Z2tjZv3qzk5OTqigsAAACAwY7sZ+fk/CarcqL31FNPHfP8uHHjVFRUdNIBAQAAAABOTrVtCH/jjTfq5Zdfrq63AwAAAACcoBNejOW3MjMzFRwcXF1vBwAAAMBgTu9lxz56v3H11Vf7vPZ4PNq3b582bNigMWPGVFtgAAAAAIATU+VELywszOe1y+VS27ZtNWHCBPXu3bvaAgMAAAAAnJgqJXoVFRUaPHiwOnbsqMaNG/srJgAAAACGczm8j56Tc9uhSoux1KtXT71799bBgwf9FA4AAAAA4GRVedXNs88+W19//bU/YgEAAAAAVIMqJ3qPPPKI7r//fi1ZskT79u1TYWGhzwEAAAAAf+TIhulOHiar9Hf0JkyYoPvuu0/9+vWTJF111VWyftXX6vF4ZFmWKioqqj9KAAAAAEClVTrRGz9+vO644w69//77/owHAAAAQB1gWfIpHDkxv8kqneh5PB5JUo8ePfwWDAAAAADg5FXpO3pOZtwAAAAAgMqp0j56Z5555h8me/n5+ScVEAAAAADzuWTJJQf30XNwbjtUKdEbP368wsLC/BULAAAAAKAaVCnRGzRokCIjI/0VCwAAAACgGlQ60eP7eQAAAACqi9N72Zm+j16lF2M5suomAAAAAKBmq3RFz+12+zMOAAAAAHWIZVkO76NndkmvStsrAAAAAABqPhI9AAAAADBMlVbdBAAAAIDqwD56/kVFDwAAAAAMQ6IHAAAAAIahdRMAAACA7SyH99EzfNFNKnoAAAAAYBoSPQAAAAAwDK2bAAAAAGzHhun+RUUPAAAAAAxDRQ8AAACA7VyWJZeDVTUn57YDFT0AAAAAMAyJHgAAAAAYhtZNAAAAALZzydmqk+kVL9OfDwAAAADqHBI9AAAAADAMrZsAAAAAbMc+ev5FRQ8AAAAADENFDwAAAIDt2EfPv6joAQAAAIBhSPQAAAAAwDC0bgIAAACwncs6fDg5v8mo6AEAAACAYUj0AAAAAMAwtG4CAAAAsJ31yx8n5zcZFT0AAAAAMAwVPQAAAAC2YzEW/6KiBwAAAACV8N133+nGG29UkyZN1KBBA3Xs2FEbNmzwXvd4PBo7dqyaNWumBg0aKD4+Xl999ZXPe+Tn5yspKUmhoaEKDw/XkCFDVFRUVO2xkugBAAAAwB84cOCALrroItWvX1///e9/9cUXX+jJJ59U48aNvWMmTZqk6dOna9asWVq3bp0aNWqkhIQElZSUeMckJSVpy5YtysjI0JIlS7R69Wrdfvvt1R4vrZsAAAAAbOeyLLks5/onqzr3448/rtjYWM2ePdt7rmXLlt6/ezweTZ06VQ899JAGDBggSZo7d66ioqK0ePFiDRo0SFu3btWyZcv0ySefqFu3bpKkGTNmqF+/fnriiScUExNTDU92GBU9AAAAAHVWYWGhz1FaWnrMcW+99Za6deuma665RpGRkerSpYteeOEF7/WdO3cqNzdX8fHx3nNhYWHq3r27MjMzJUmZmZkKDw/3JnmSFB8fL5fLpXXr1lXrc5HoAQAAAKizYmNjFRYW5j3S09OPOe7rr7/Ws88+qzZt2mj58uW68847dc899+iVV16RJOXm5kqSoqKifO6LioryXsvNzVVkZKTP9YCAAEVERHjHVBdaNwEAAADYrqbso7dnzx6FhoZ6zwcFBR1zvNvtVrdu3fTYY49Jkrp06aLNmzdr1qxZSk5O9n/AVURFDwAAAECdFRoa6nMcL9Fr1qyZOnTo4HOuffv22r17tyQpOjpakpSXl+czJi8vz3stOjpa+/fv97l+6NAh5efne8dUFxI9AAAAAPgDF110kXJycnzOffnll2rRooWkwwuzREdHa8WKFd7rhYWFWrduneLi4iRJcXFxOnjwoLKysrxjVq5cKbfbre7du1drvLRuAgAAALCd5fCG6VVd8PPee+/VhRdeqMcee0zXXnut1q9fr+eff17PP//8L+9nafjw4XrkkUfUpk0btWzZUmPGjFFMTIwSExMlHa4A9unTR0OHDtWsWbNUXl6u1NRUDRo0qFpX3JRI9AAAAADgD5133nlatGiRRo8erQkTJqhly5aaOnWqkpKSvGMeeOABFRcX6/bbb9fBgwd18cUXa9myZQoODvaOmTdvnlJTU3X55ZfL5XJp4MCBmj59erXHS6IHAAAAwHa1bR89Sfrzn/+sP//5z8e9blmWJkyYoAkTJhx3TEREhObPn1/luauK7+gBAAAAgGFI9AAAAADAMLRuAgAAALCdJUuWg62bTu7hZwcqegAAAABgGBI9AAAAADAMrZsAAAAAbOeSs1Un0ytepj8fAAAAANQ5VPQAAAAA2K427qNXm1DRAwAAAADDkOgBAAAAgGFo3QQAAABgO8tyeB89WjcBAAAAALUJiR4AAAAAGIbWTQAAAAC2Yx89/zL9+QAAAACgziHRAwAAAADD0LoJAAAAwHasuulfVPQAAAAAwDBU9AAAAADYzmVZcjlYVXNybjtQ0QMAAAAAw5DoAQAAAIBhaN0EAAAAYDvrl8PJ+U1GRQ8AAAAADEOiBwAAAACGoXUTAAAAgO3YR8+/qOgBAAAAgGGo6AEAAACwnUuSy8ElUUyveJn+fAAAAABQ55DoAQAAAIBhaN0EAAAAYDvLOnw4Ob/JqOgBAAAAgGFI9AAAAADAMLRuAgAAALCdZVlysY+e31DRAwAAAADDkOgBAAAAgGFo3QQAAABgO+uXP07ObzIqegAAAABgGCp6AAAAAGzHPnr+RUUPAAAAAAxDogcAAAAAhqF1EwAAAIDtXLLkcnBBFCfntgMVPQAAAAAwDBU9ADVG6fTXnQ4BwDHM3jbP6RAA/MbPRSVOh4AajkQPAAAAgO0sy5Ll4NKXTs5tB1o3AQAAAMAwVPQAAAAA2I599PyLih4AAAAAGIZEDwAAAAAMQ+smAAAAANuxj55/UdEDAAAAAMOQ6AEAAACAYWjdBAAAAGA79tHzLyp6AAAAAGAYKnoAAAAAbOeSs1Un0ytepj8fAAAAANQ5JHoAAAAAYBhaNwEAAADYjsVY/IuKHgAAAAAYhkQPAAAAAAxD6yYAAAAA29G66V9U9AAAAADAMCR6AAAAAGAYWjcBAAAA2I4N0/3L9OcDAAAAgDqHih4AAAAA27EYi39R0QMAAAAAw5DoAQAAAIBhaN0EAAAA4ABLlpxsn6R1EwAAAABQi5DoAQAAAIBhaN0EAAAAYDuXdfhwcn6TUdEDAAAAAMNQ0QMAAABgO8vhxVicXQjG/6joAQAAAIBhSPQAAAAAwDC0bgIAAACwncuy5LKca590cm47UNEDAAAAAMOQ6AEAAACAYWjdBAAAAGA7yzp8ODm/yajoAQAAAIBhSPQAAAAAwDC0bgIAAACwHRum+xcVPQAAAACoon/84x+yLEvDhw/3nispKVFKSoqaNGmiU045RQMHDlReXp7Pfbt371b//v3VsGFDRUZGasSIETp06FC1x0dFDwAAAIDtXHJ4H72TqOh98skneu6553TOOef4nL/33nu1dOlSLVy4UGFhYUpNTdXVV1+tjz76SJJUUVGh/v37Kzo6WmvXrtW+fft08803q379+nrsscdO6nl+i4oeAAAAAFRSUVGRkpKS9MILL6hx48be8wUFBXrppZc0ZcoUXXbZZeratatmz56ttWvX6uOPP5Ykvfvuu/riiy/0z3/+U507d1bfvn01ceJEzZw5U2VlZdUaJ4keAAAAgDqrsLDQ5ygtLf3d8SkpKerfv7/i4+N9zmdlZam8vNznfLt27dS8eXNlZmZKkjIzM9WxY0dFRUV5xyQkJKiwsFBbtmypxqci0QMAAADgAKsGHJIUGxursLAw75Genn7cmBcsWKCNGzcec0xubq4CAwMVHh7ucz4qKkq5ubneMb9O8o5cP3KtOvEdPQAAAAB11p49exQaGup9HRQUdNxxw4YNU0ZGhoKDg+0K74RR0QMAAABQZ4WGhvocx0v0srKytH//fp177rkKCAhQQECAVq1apenTpysgIEBRUVEqKyvTwYMHfe7Ly8tTdHS0JCk6OvqoVTiPvD4yprqQ6AEAAACwncuyHD+q4vLLL9fnn3+u7Oxs79GtWzclJSV5/16/fn2tWLHCe09OTo52796tuLg4SVJcXJw+//xz7d+/3zsmIyNDoaGh6tChQ/X8YH9B6yYAAAAA/IGQkBCdffbZPucaNWqkJk2aeM8PGTJEaWlpioiIUGhoqO6++27FxcXpggsukCT17t1bHTp00E033aRJkyYpNzdXDz30kFJSUo5bSTxRJHoAAAAAbGdZliwH99Hzx9xPPfWUXC6XBg4cqNLSUiUkJOiZZ57xXq9Xr56WLFmiO++8U3FxcWrUqJGSk5M1YcKEao+FRA8AAAAATsAHH3zg8zo4OFgzZ87UzJkzj3tPixYt9M477/g5Mr6jBwAAAADGoaIHAAAAwHa/3svOqflNRkUPAAAAAAxDogcAAAAAhqF1EwAAAIDtTmQvu+qe32RU9AAAAADAMFT0AAAAANju8GIsDu6j59jM9qCiBwAAAACGIdEDAAAAAMPQugkAAADAfpbk6HoohvduUtEDAAAAAMOQ6AEAAACAYWjdBAAAAGA765c/Ts5vMip6AAAAAGAYEj0AAAAAMAytmwAAAABsR+umf1HRAwAAAADDUNEDAAAAYD9Lzu5lZ3ZBj4oeAAAAAJiGRA8AAAAADEPrJgAAAADbsRiLf1HRAwAAAADDkOgBAAAAgGFo3QQAAABgO8uyZFkOtm46OLcdqOgBAAAAgGGo6AEAAACwHdvo+RcVPQAAAAAwDIkeAAAAABiG1k0AAAAAtmMfPf+iogcAAAAAhiHRAwAAAADD0LoJAAAAwHbso+dfVPQAAAAAwDAkegAAAABgGFo3AQAAANiODdP9i4oeAAAAABiGih4AAAAA27GPnn9R0QMAAAAAw5DoAQAAAIBhaN0EAAAAYDv20fMvKnoAAAAAYBgSPQAAAAAwDK2bAAAAAGzHPnr+RUUPAAAAAAxDRQ8AAACA7ViMxb+o6AEAAACAYUj0AAAAAMAwtG4CAAAAsJ31yx8n5zcZFT0AAAAAMAyJHgAAAAAYhtZNAAAAALajddO/qOgBAAAAgGGo6AEAAACwnWUdPpyc32RU9AAAAADAMCR6AAAAAGAYWjcBAAAA2I7FWPyLih4AAAAAGIZEDwAAAAAMQ+smAAAAANvRuulfJHqotWa+9R9NXvi6cvPz1anVGZqRkqrz27VzOiygTln9+ed6YuFCZX31lfbl5+vNhx9W4oUXeq+7EhKOed/jt92mEddcY1eYgPEOHPxJ/34rS59v/U5l5RWKbBqiW2+4UKc3bypJyvr0G33w0Zf6Zs//qfinMj084s9qflqEz3vMfS1TX+Ts08HCnxUUGKDWLf+kv17VVc2iwpx4JAAnydHWzfT0dJ133nkKCQlRZGSkEhMTlZOT84f3LVy4UO3atVNwcLA6duyod955x4ZoUZO89sH7Sntulh6+8SZtfGaWOrVqpYS/j9L+AwecDg2oU4pLSnROq1Z6OjX1mNf3/utfPsdLaWmyLEsDL77Y5kgBcxX/VKr0af9VvXouDb8jXhNHX6VrE7upYcMg75jSskNq0ypSf72q63Hfp0VsEw2+4SI9MnqA0u6Ml0fSlGcy5Ha7bXgKANXN0URv1apVSklJ0ccff6yMjAyVl5erd+/eKi4uPu49a9eu1fXXX68hQ4Zo06ZNSkxMVGJiojZv3mxj5HDalDfe0NC+/TQ4oY86tGihWcOGq2FQkF5evszp0IA6pe955+mRW27RXy666JjXoyMifI63MjPVq1MntWrWzOZIAXP9973NighvpFuTLlKrFk31pyYhOrtdjCKbhnjHXHjeGbqqTyd1OPP4n70eF56ptq2j1LTJKWoR20R/6ddF+Qd/0g/5x/+9DDgp1v82TXfiMLxz09nWzWXLfH8pnzNnjiIjI5WVlaVLL730mPdMmzZNffr00YgRIyRJEydOVEZGhp5++mnNmjXL7zHDeWXl5cr66kuNHnS995zL5VJ8l3OVufULByMD8HvyDhzQ0vXrNef++50OBTBK9uZvdXa7GD0ze5W+3J6n8LAG6nVxW/W48MwTfs/S0nJ9tG67mjY5RRHhDasxWgB2qVHf0SsoKJAkRUREHHdMZmam0tLSfM4lJCRo8eLFxxxfWlqq0tJS7+vCwsKTDxSO+qGwQBVut6IaN/Y5H9W4sbbt2eNQVAD+yCsZGQpp0EBX07YJVKvv/+9Hvf9Rjnr37KD+V5ytXbv/T/968xMFBNTTReefUaX3Wrlmm/791kaVlh1SdGSo7rvrCgUE1PNT5IDTZTWzS3o1ZnsFt9ut4cOH66KLLtLZZ5993HG5ubmKioryORcVFaXc3Nxjjk9PT1dYWJj3iI2Nrda4AQCVM3v5ct1w2WUKDgx0OhTAKB6P1OK0Jhp45blqcVoT9bjwTF0a10YffPTH6x781gXdWunhEX/WA3cnKCoyVLNmr1J5eYUfogbgbzUm0UtJSdHmzZu1YMGCan3f0aNHq6CgwHvsoeJT6zUNDVM9l0t5v1l4Je/AAUVHND7OXQCctObzz5Xz7be6rU8fp0MBjBMW2kAx0b4rYzaLClP+gap/t65hg0BFRYaqbeso3TW4h/btL9TGz3ZXV6gAbFQjEr3U1FQtWbJE77//vk477bTfHRsdHa28vDyfc3l5eYqOjj7m+KCgIIWGhvocqN0C69dX1zZnakX2Ru85t9utFdmbFNe+g4ORATiel5cvV9c2bdTpjKq1kQH4Y21a/km5+32/mpK3v1BNGp9yUu/rkSSPR+WHqOjBPyzLcvwwmaOJnsfjUWpqqhYtWqSVK1eqZcuWf3hPXFycVqxY4XMuIyNDcXFx/goTNVDawIF64Z139Mq772rr7m905/RpKi4p0eAEqgWAnYp+/lnZO3Yoe8cOSdLO3Fxl79ih3fv3e8cUFhdr4erVGkI1D/CLK3p20Ne7vtfSdz9X3veF+njD11qV+ZUuu6Std0xRcal2f5uvvbkHJUm5+wu0+9t8FRT+LEn6/ocftTTjc+3a83/6v/wibd+5X8/OXqX69evpnA6nOvFYAE6So4uxpKSkaP78+frPf/6jkJAQ7/fswsLC1KBBA0nSzTffrFNPPVXp6emSpGHDhqlHjx568skn1b9/fy1YsEAbNmzQ888/79hzwH7X9eyl7wsKNHbuHOUeOKDOrc7QskfTj1qgBYB/bfjyS132wAPe1/c995wkKfmKKzT7l9U1F6xaJY+k63v1ciJEwHgtWzRVypBeemPJRr21/FP9qUmIBv2lmy7o1so7JnvzHs2ev9b7+rlX1kiSrupzjgb07ayA+vX01Y79eu+DrSr+uUyhIcE684wo/X14X4WGNLD9mQCcPMvj8Xgcm/w45dLZs2frlltukST17NlTp59+uubMmeO9vnDhQj300EPatWuX2rRpo0mTJqlfv36VmrOwsFBhYWEqKNim0NCQP74BgG08HtqDgJpo9rZ5TocA4Dd+LipR6vnjVVBQUOu+mnTk9/Fvvn9NoaHObd9RWPiTWvzpulr5M6wMRyt6lckxP/jgg6POXXPNNbrmmmv8EBEAAAAA1H41ah89AAAAAHWD9csfJ+c3WY1YdRMAAAAAUH1I9AAAAADAMLRuAgAAALCd03vZsY8eAAAAAKBWIdEDAAAAAMPQugkAAADAdtYvh5Pzm4yKHgAAAAAYhkQPAAAAAAxD6yYAAAAA27Fhun9R0QMAAAAAw5DoAQAAALCdZf1vLz1njqrFm56ervPOO08hISGKjIxUYmKicnJyfMaUlJQoJSVFTZo00SmnnKKBAwcqLy/PZ8zu3bvVv39/NWzYUJGRkRoxYoQOHTp0sj/Oo5DoAQAAAMAfWLVqlVJSUvTxxx8rIyND5eXl6t27t4qLi71j7r33Xr399ttauHChVq1apb179+rqq6/2Xq+oqFD//v1VVlamtWvX6pVXXtGcOXM0duzYao+X7+gBAAAAwB9YtmyZz+s5c+YoMjJSWVlZuvTSS1VQUKCXXnpJ8+fP12WXXSZJmj17ttq3b6+PP/5YF1xwgd5991198cUXeu+99xQVFaXOnTtr4sSJGjlypMaNG6fAwMBqi5eKHgAAAADbWTXgz8koKCiQJEVEREiSsrKyVF5ervj4eO+Ydu3aqXnz5srMzJQkZWZmqmPHjoqKivKOSUhIUGFhobZs2XJS8fwWFT0AAAAAdVZhYaHP66CgIAUFBf3uPW63W8OHD9dFF12ks88+W5KUm5urwMBAhYeH+4yNiopSbm6ud8yvk7wj149cq05U9AAAAADUWbGxsQoLC/Me6enpf3hPSkqKNm/erAULFtgQ4YmhogcAAADAdodX3XR2fknas2ePQkNDvef/qJqXmpqqJUuWaPXq1TrttNO856Ojo1VWVqaDBw/6VPXy8vIUHR3tHbN+/Xqf9zuyKueRMdWFih4AAACAOis0NNTnOF6i5/F4lJqaqkWLFmnlypVq2bKlz/WuXbuqfv36WrFihfdcTk6Odu/erbi4OElSXFycPv/8c+3fv987JiMjQ6GhoerQoUO1PhcVPQAAAAC2q44FUU52/qpISUnR/Pnz9Z///EchISHe79SFhYWpQYMGCgsL05AhQ5SWlqaIiAiFhobq7rvvVlxcnC644AJJUu/evdWhQwfddNNNmjRpknJzc/XQQw8pJSXlDyuJVUWiBwAAAAB/4Nlnn5Uk9ezZ0+f87Nmzdcstt0iSnnrqKblcLg0cOFClpaVKSEjQM8884x1br149LVmyRHfeeafi4uLUqFEjJScna8KECdUeL4keAAAAAPwBj8fzh2OCg4M1c+ZMzZw587hjWrRooXfeeac6QzsmEj0AAAAADrB+OZyc31wsxgIAAAAAhiHRAwAAAADD0LoJAAAAwAEuOVt3MrvmZfbTAQAAAEAdRKIHAAAAAIahdRMAAACA7Wrbhum1DRU9AAAAADAMFT0AAAAADmAfPX+iogcAAAAAhiHRAwAAAADD0LoJAAAAwAHso+dPZj8dAAAAANRBJHoAAAAAYBhaNwEAAADYz7IOH07ObzAqegAAAABgGCp6AAAAAGxn/fLHyflNRkUPAAAAAAxDogcAAAAAhqF1EwAAAIAD2EfPn8x+OgAAAACog0j0AAAAAMAwtG4CAAAAcID1y+Hk/OaiogcAAAAAhqGiBwAAAMABLMbiT2Y/HQAAAADUQSR6AAAAAGAYWjcBAAAA2M765Y+T85uMih4AAAAAGIZEDwAAAAAMQ+smAAAAAIeY3T7pJCp6AAAAAGAYEj0AAAAAMAytmwAAAAAcwIbp/mT20wEAAABAHURFDwAAAIADLDm7GIvZC8FQ0QMAAAAAw5DoAQAAAIBhaN0EAAAAYDtLLlkO1p2cnNsOZj8dAAAAANRBJHoAAAAAYBhaNwEAAAA4gFU3/YmKHgAAAAAYhooeAAAAAPtZ1uHDyfkNRkUPAAAAAAxDogcAAAAAhqF1EwAAAIADXHK27mR2zcvspwMAAACAOohEDwAAAAAMQ+smAAAAANtZv/xxcn6TUdEDAAAAAMOQ6AEAAACAYWjdBAAAAOAA65fDyfnNRUUPAAAAAAxDRQ8AAACAA9hHz5/MfjoAAAAAqINI9AAAAADAMLRuAgAAAHAAi7H4ExU9AAAAADAMiR4AAAAAGIbWTQAAAAC2s+SS5WDdycm57WD20wEAAABAHURFDwAAAIADWIzFn6joAQAAAIBhSPQAAAAAwDC0bgIAAABwiNntk06iogcAAAAAhiHRAwAAAADD0LoJAAAAwAEuOVt3MrvmZfbTAQAAAEAdREUPAAAAgO0sy5JlObcYi5Nz24GKHgAAAAAYhkQPAAAAAAxD6yYAAAAAB1hydh89WjcBAAAAALUIiR4AAAAAGIbWTQAAAAAOYB89fzL76QAAAACgDiLRAwAAAADD0LoJAAAAwAGsuulPVPQAAAAAwDBU9AAAAADYzpJLloN1JyfntoPZTwcAAAAA1WjmzJk6/fTTFRwcrO7du2v9+vVOh3RMJHoAAAAAUAmvvfaa0tLS9PDDD2vjxo3q1KmTEhIStH//fqdDOwqJHgAAAAAHWDXgqJopU6Zo6NChGjx4sDp06KBZs2apYcOGevnll0/g+f2LRA8AAAAA/kBZWZmysrIUHx/vPedyuRQfH6/MzEwHIzu2OrcYi8fjkSQVFhY5HAmA3/J4KpwOAcAx/FxU4nQIAH7j56JSSf/73bY2Kiz8sUbMX1hY6HM+KChIQUFBR43/4YcfVFFRoaioKJ/zUVFR2rZtm/8CPUF1LtH78cfD/4PGxnZzOBIAAADg5Pz4448KCwtzOowqCQwMVHR0tGJjz3M6FJ1yyimKjY31Offwww9r3LhxzgRUjepcohcTE6M9e/YoJCRElmX2Jol1QWFhoWJjY7Vnzx6FhoY6HQ6AX/DZBGomPpvm8Hg8+vHHHxUTE+N0KFUWHBysnTt3qqyszOlQ5PF4jsoJjlXNk6SmTZuqXr16ysvL8zmfl5en6Ohov8V4oupcoudyuXTaaac5HQaqWWhoKP9gATUQn02gZuKzaYbaVsn7teDgYAUHBzsdRpUEBgaqa9euWrFihRITEyVJbrdbK1asUGpqqrPBHUOdS/QAAAAA4ESkpaUpOTlZ3bp10/nnn6+pU6equLhYgwcPdjq0o5DoAQAAAEAlXHfddfr+++81duxY5ebmqnPnzlq2bNlRC7TUBCR6qNWCgoL08MMPH7eXGoAz+GwCNROfTeDkpaam1shWzd+yPLV5TVYAAAAAwFHYMB0AAAAADEOiBwAAAACGIdEDAAAAAMOQ6AEAAACAYUj0UOPNnDlTp59+uoKDg9W9e3etX7/+d8cvXLhQ7dq1U3BwsDp27Kh33nnHpkiBumH16tW68sorFRMTI8uytHjx4j+854MPPtC5556roKAgtW7dWnPmzPF7nEBdk56ervPOO08hISGKjIxUYmKicnJy/vA+/t0EzESihxrttddeU1pamh5++GFt3LhRnTp1UkJCgvbv33/M8WvXrtX111+vIUOGaNOmTUpMTFRiYqI2b95sc+SAuYqLi9WpUyfNnDmzUuN37typ/v37q1evXsrOztbw4cN12223afny5X6OFKhbVq1apZSUFH388cfKyMhQeXm5evfureLi4uPew7+bgLnYXgE1Wvfu3XXeeefp6aefliS53W7Fxsbq7rvv1qhRo44af91116m4uFhLlizxnrvgggvUuXNnzZo1y7a4gbrCsiwtWrRIiYmJxx0zcuRILV261OcXx0GDBungwYNatmyZDVECddP333+vyMhIrVq1Spdeeukxx/DvJmAuKnqoscrKypSVlaX4+HjvOZfLpfj4eGVmZh7znszMTJ/xkpSQkHDc8QD8j88l4IyCggJJUkRExHHH8PkEzEWihxrrhx9+UEVFhaKionzOR0VFKTc395j35ObmVmk8AP873ueysLBQP//8s0NRAWZzu90aPny4LrroIp199tnHHce/m4C5ApwOAAAAANUrJSVFmzdv1ocffuh0KAAcQqKHGqtp06aqV6+e8vLyfM7n5eUpOjr6mPdER0dXaTwA/zve5zI0NFQNGjRwKCrAXKmpqVqyZIlWr16t00477XfH8u8mYC5aN1FjBQYGqmvXrlqxYoX3nNvt1ooVKxQXF3fMe+Li4nzGS1JGRsZxxwPwPz6XgD08Ho9SU1O1aNEirVy5Ui1btvzDe/h8AuYi0UONlpaWphdeeEGvvPKKtm7dqjvvvFPFxcUaPHiwJOnmm2/W6NGjveOHDRumZcuW6cknn9S2bds0btw4bdiwQampqU49AmCcoqIiZWdnKzs7W9Lh7ROys7O1e/duSdLo0aN18803e8ffcccd+vrrr/XAAw9o27ZteuaZZ/T666/r3nvvdSJ8wFgpKSn65z//qfnz5yskJES5ubnKzc31+S4s/24CdYgHqOFmzJjhad68uScwMNBz/vnnez7++GPvtR49eniSk5N9xr/++uueM8880xMYGOg566yzPEuXLrU5YsBs77//vkfSUceRz2JycrKnR48eR93TuXNnT2BgoKdVq1ae2bNn2x43YLpjfS4l+Xze+HcTqDvYRw8AAAAADEPrJgAAAAAYhkQPAAAAAAxDogcAAAAAhiHRAwAAAADDkOgBAAAAgGFI9AAAAADAMCR6AAAAAGAYEj0AqKNuueUWJSYmel/37NlTw4cPtz2ODz74QJZl6eDBg8cdY1mWFi9eXOn3HDdunDp37nxSce3atUuWZSk7O/uk3gcAACeQ6AFADXLLLbfIsixZlqXAwEC1bt1aEyZM0KFDh/w+95tvvqmJEydWamxlkjMAAOCcAKcDAAD46tOnj2bPnq3S0lK98847SklJUf369TV69OijxpaVlSkwMLBa5o2IiKiW9wEAAM6jogcANUxQUJCio6PVokUL3XnnnYqPj9dbb70l6X/tlo8++qhiYmLUtm1bSdKePXt07bXXKjw8XBERERowYIB27drlfc+KigqlpaUpPDxcTZo00QMPPCCPx+Mz729bN0tLSzVy5EjFxsYqKChIrVu31ksvvaRdu3apV69ekqTGjRvLsizdcsstkiS326309HS1bNlSDRo0UKdOnfTvf//bZ5533nlHZ555pho0aKBevXr5xFlZI0eO1JlnnqmGDRuqVatWGjNmjMrLy48a99xzzyk2NlYNGzbUtddeq4KCAp/rL774otq3b6/g4GC1a9dOzzzzTJVjAQCgJiLRA4AarkGDBiorK/O+XrFihXJycpSRkaElS5aovLxcCQkJCgkJ0Zo1a/TRRx/plFNOUZ8+fbz3Pfnkk5ozZ45efvllffjhh8rPz9eiRYt+d96bb75Z//rXvzR9+nRt3bpVzz33nE455RTFxsbqjTfekCTl5ORo3759mjZtmiQpPT1dc+fO1axZs7Rlyxbde++9uvHGG7Vq1SpJhxPSq6++WldeeaWys7N12223adSoUVX+mYSEhGjOnDn64osvNG3aNL3wwgt66qmnfMZs375dr7/+ut5++20tW7ZMmzZt0l133eW9Pm/ePI0dO1aPPvqotm7dqscee0xjxozRK6+8UuV4AACocTwAgBojOTnZM2DAAI/H4/G43W5PRkaGJygoyHP//fd7r0dFRXlKS0u997z66quetm3betxut/dcaWmpp0GDBp7ly5d7PB6Pp1mzZp5JkyZ5r5eXl3tOO+0071wej8fTo0cPz7Bhwzwej8eTk5PjkeTJyMg4Zpzvv/++R5LnwIED3nMlJSWehg0betauXeszdsiQIZ7rr7/e4/F4PKNHj/Z06NDB5/rIkSOPeq/fkuRZtGjRca9PnjzZ07VrV+/rhx9+2FOvXj3Pt99+6z333//+1+NyuTz79u3zeDwezxlnnOGZP3++z/tMnDjRExcX5/F4PJ6dO3d6JHk2bdp03HkBAKip+I4eANQwS5Ys0SmnnKLy8nK53W7dcMMNGjdunPd6x44dfb6X9+mnn2r79u0KCQnxeZ+SkhLt2LFDBQUF2rdvn7p37+69FhAQoG7duh3VvnlEdna26tWrpx49elQ67u3bt+unn37SFVdc4XO+rKxMXbp0kSRt3brVJw5JiouLq/QcR7z22muaPn26duzYoaKiIh06dEihoaE+Y5o3b65TTz3VZx63262cnByFhIRox44dGjJkiIYOHeodc+jQIYWFhVU5HgAAahoSPQCoYXr16qVnn31WgYGBiomJUUCA7/9VN2rUyOd1UVGRunbtqnnz5h31Xn/6059OKIYGDRpU+Z6ioiJJ0tKlS30SLOnw9w6rS2ZmppKSkjR+/HglJCQoLCxMCxYs0JNPPlnlWF944YWjEs969epVW6wAADiFRA8AaphGjRqpdevWlR5/7rnn6rXXXlNkZORRVa0jmjVrpnXr1unSSy+VdLhylZWVpXPPPfeY4zt27Ci3261Vq1YpPj7+qOtHKooVFRXecx06dFBQUJB279593Epg+/btvQvLHPHxxx//8UP+ytq1a9WiRQs9+OCD3nPffPPNUeN2796tvXv3KiYmxjuPy+VS27ZtFRUVpZiYGH399ddKSkqq0vwAANQGLMYCALVcUlKSmjZtqgEDBmjNmjXauXOnPvjgA91zzz369ttvJUnDhg3TP/7xDy1evFjbtm3TXXfd9bt74J1++ulKTk7WrbfeqsWLF3vf8/XXX5cktWjRQpZlacmSJfr+++9VVFSkkJAQ3X///br33nv1yiuvaMeOHdq4caNmzJjhXeDkjjvu0FdffaURI0YoJydH8+fP15w5c6r0vG3atNHu3bu1YMEC7dixQ9OnTz/mwjLBwcFKTk7Wp59+qjVr1uiee+7Rtddeq+joaEnS+PHjlZ6erunTp+vLL7/U559/rtmzZ2vKlClVigcAgJqIRA8AarmGDRtq9erVat68ua6++mq1b99eQ4YMUUlJibfCd9999+mmm25ScnKy4uLiFBISor/85S+/+77PPvus/vrXv+quu+5Su3btNHToUBUXF0uSTj31VI0fP16jRo1SVFSUUlNTJUkTJ07UmDFjlJ6ervbt26tPnz5aunSpWrZsKenw9+beeOMNLV68WJ06ddKsWbP02GOPVel5r7rqKt17771KTU1V586dtXbtWo0ZM+aoca1bt9bVV1+tfv36qXfv3jrnnHN8tk+47bbb9OKLL2r27Nnq2LGjevTooTlz5nhjBQCgNrM8x/smPgAAAACgVqKiBwAAAACGIdEDAAAAAMOQ6AEAAACAYUj0AAAAAMAwJHoAAAAAYBgSPQAAAAAwDIkeAAAAABiGRA8AAAAADEOiBwAAAACGIdEDAAAAAMOQ6AEAAACAYUj0AAAAAMAw/w+JLwKVCoQzGQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Instantiate\n",
    "tree = DecisionTreeClassifier(random_state=2)\n",
    "\n",
    "# Train\n",
    "tree.fit(X_train, y_train)\n",
    "\n",
    "# Predict\n",
    "y_predict = tree.predict(X_test)\n",
    "\n",
    "# Print accuracy\n",
    "print(accuracy_score(y_test, y_predict))\n",
    "print(classification_report(y_test, y_predict))\n",
    "\n",
    "# Print confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_confusion_matrix(tree, X_test, y_test, cmap = 'YlGn', ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualize the decision logic of the decision tree model\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_tree(tree, ax=ax)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Looks like the number of leaf nodes constraint isn't related to the number of final classes we have\n",
    "\n",
    "# Random Forest Classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9908930669800236\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00      1488\n",
      "         1.0       0.99      0.99      0.99      1286\n",
      "         2.0       0.98      0.97      0.98       630\n",
      "\n",
      "    accuracy                           0.99      3404\n",
      "   macro avg       0.99      0.99      0.99      3404\n",
      "weighted avg       0.99      0.99      0.99      3404\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17fe57d30>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate\n",
    "rf = RandomForestClassifier(random_state=2)\n",
    "\n",
    "# Train\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Predict\n",
    "y_predict = rf.predict(X_test)\n",
    "\n",
    "# Print accuracy score and classification report\n",
    "print(accuracy_score(y_test, y_predict))\n",
    "print(classification_report(y_test, y_predict))\n",
    "\n",
    "# Plot Confusion Matrix\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_confusion_matrix(rf, X_test, y_test, cmap = 'YlGn', ax=ax)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing feature importances\n",
    "- Let's see if we can improve the performance of this model by eliminating features that don't contribute to the classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "runtime = 0.0061295693480302215\n",
      "tomatometer_rating = 0.4751025864067028\n",
      "tomatometer_count = 0.09895425717250429\n",
      "audience_rating = 0.05389101976402856\n",
      "audience_count = 0.016289896938110955\n",
      "tomatometer_top_critics_count = 0.03605739578773787\n",
      "tomatometer_fresh_critics_count = 0.14158242877024638\n",
      "tomatometer_rotten_critics_count = 0.1314030539462201\n",
      "G = 0.00029824734631526663\n",
      "NC17 = 1.7090610669550172e-05\n",
      "NR = 0.008710731204885382\n",
      "PG = 0.0005762363713202383\n",
      "PG-13 = 0.002531466583040527\n",
      "R = 0.0022318936876928337\n",
      "audience_status = 0.026224126062495294\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get feature importance\n",
    "feature_importance = rf.feature_importances_\n",
    "\n",
    "# Print feature importance\n",
    "for i, feature in enumerate(X_train.columns):\n",
    "    print(f'{feature} = {feature_importance[i]}')\n",
    "\n",
    "# Visualize feature from the most important to least important\n",
    "indices = np.argsort(feature_importance)\n",
    "\n",
    "plt.figure(figsize=(12,9))\n",
    "plt.title('Feature Importances')\n",
    "plt.barh(range(len(indices)), feature_importance[indices], color='g', align='center')\n",
    "plt.yticks(range(len(indices)), [X_train.columns[i] for i in indices])\n",
    "plt.xlabel('Relative Importance')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can see that runtime and movie category rating (NR, PG-13 etc.) are not important\n",
    "- So, we'll drop them and build a Random Forest Classifier\n",
    "\n",
    "\n",
    "# Random Forest Classifier with Feature Selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training data is (13613, 8) and size of test data is (3404, 8)\n"
     ]
    }
   ],
   "source": [
    "# Do feature selection and split data into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(df_feature.drop(['tomatometer_status', 'NR', 'runtime', 'PG-13', 'R', \\\n",
    "    'PG', 'G', 'NC17'], axis=1), df_feature['tomatometer_status'], test_size=0.2, random_state=42)\n",
    "\n",
    "print(f'Size of training data is {X_train.shape} and size of test data is {X_test.shape}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9917743830787309\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00      1488\n",
      "         1.0       0.99      0.99      0.99      1286\n",
      "         2.0       0.98      0.97      0.98       630\n",
      "\n",
      "    accuracy                           0.99      3404\n",
      "   macro avg       0.99      0.99      0.99      3404\n",
      "weighted avg       0.99      0.99      0.99      3404\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x17fe54a90>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate\n",
    "rf = RandomForestClassifier(random_state=2)\n",
    "\n",
    "# Train\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# Predict\n",
    "y_predict = rf.predict(X_test)\n",
    "\n",
    "# Print accuracy and classification report\n",
    "print(accuracy_score(y_test, y_predict))\n",
    "print(classification_report(y_test, y_predict))\n",
    "\n",
    "# Plot the confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_confusion_matrix(rf, X_test, y_test, cmap = 'YlGn', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Weighted Random Forest Classifier with Feature Selection\n",
    "\n",
    "- We will weight the classes according to the number of their occurences"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0.7691299435028248, 1: 0.8760360360360361, 2: 1.7910746237238186}"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate class weights\n",
    "class_weight = compute_class_weight(class_weight='balanced', classes = np.unique(df_feature['tomatometer_status']), \\\n",
    "    y = df_feature['tomatometer_status'].values)\n",
    "\n",
    "class_weight_dict = dict(zip(range(len(class_weight.tolist())), class_weight.tolist()))\n",
    "class_weight_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9926556991774383\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         0.0       1.00      1.00      1.00      1488\n",
      "         1.0       0.99      0.99      0.99      1286\n",
      "         2.0       0.98      0.98      0.98       630\n",
      "\n",
      "    accuracy                           0.99      3404\n",
      "   macro avg       0.99      0.99      0.99      3404\n",
      "weighted avg       0.99      0.99      0.99      3404\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x28925e620>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate\n",
    "rf_weighted = RandomForestClassifier(random_state=2, class_weight=class_weight_dict)\n",
    "\n",
    "# Train\n",
    "rf_weighted.fit(X_train, y_train)\n",
    "\n",
    "# Predict\n",
    "y_predict = rf_weighted.predict(X_test)\n",
    "\n",
    "# Print accuracy score and classification report\n",
    "print(accuracy_score(y_test, y_predict))\n",
    "print(classification_report(y_test, y_predict))\n",
    "\n",
    "# Plot confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_confusion_matrix(rf_weighted, X_test, y_test, cmap = 'YlGn', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predicting Movie Status using Review sentiment\n",
    "- We will merge both datasets.\n",
    "- Also, we'll use sentiment analysis.\n",
    "- We will work with text data as input and use Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>rotten_tomatoes_link</th>\n",
       "      <th>critic_name</th>\n",
       "      <th>top_critic</th>\n",
       "      <th>publisher_name</th>\n",
       "      <th>review_type</th>\n",
       "      <th>review_score</th>\n",
       "      <th>review_date</th>\n",
       "      <th>review_content</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Andrew L. Urban</td>\n",
       "      <td>False</td>\n",
       "      <td>Urban Cinefile</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-02-06</td>\n",
       "      <td>A fantasy adventure that fuses Greek mythology...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Louise Keller</td>\n",
       "      <td>False</td>\n",
       "      <td>Urban Cinefile</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-02-06</td>\n",
       "      <td>Uma Thurman as Medusa, the gorgon with a coiff...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>NaN</td>\n",
       "      <td>False</td>\n",
       "      <td>FILMINK (Australia)</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>With a top-notch cast and dazzling special eff...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Ben McEachen</td>\n",
       "      <td>False</td>\n",
       "      <td>Sunday Mail (Australia)</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>3.5/5</td>\n",
       "      <td>2010-02-09</td>\n",
       "      <td>Whether audiences will get behind The Lightnin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Ethan Alter</td>\n",
       "      <td>True</td>\n",
       "      <td>Hollywood Reporter</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2010-02-10</td>\n",
       "      <td>What's really lacking in The Lightning Thief i...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rotten_tomatoes_link      critic_name  top_critic           publisher_name  \\\n",
       "0            m/0814255  Andrew L. Urban       False           Urban Cinefile   \n",
       "1            m/0814255    Louise Keller       False           Urban Cinefile   \n",
       "2            m/0814255              NaN       False      FILMINK (Australia)   \n",
       "3            m/0814255     Ben McEachen       False  Sunday Mail (Australia)   \n",
       "4            m/0814255      Ethan Alter        True       Hollywood Reporter   \n",
       "\n",
       "  review_type review_score review_date  \\\n",
       "0       Fresh          NaN  2010-02-06   \n",
       "1       Fresh          NaN  2010-02-06   \n",
       "2       Fresh          NaN  2010-02-09   \n",
       "3       Fresh        3.5/5  2010-02-09   \n",
       "4      Rotten          NaN  2010-02-10   \n",
       "\n",
       "                                      review_content  \n",
       "0  A fantasy adventure that fuses Greek mythology...  \n",
       "1  Uma Thurman as Medusa, the gorgon with a coiff...  \n",
       "2  With a top-notch cast and dazzling special eff...  \n",
       "3  Whether audiences will get behind The Lightnin...  \n",
       "4  What's really lacking in The Lightning Thief i...  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Re-read movie data\n",
    "df_movie = pd.read_csv('rotten_tomatoes_movies.csv')\n",
    "\n",
    "# Read the critics data\n",
    "df_critics = pd.read_csv('rotten_tomatoes_critic_reviews_50k.csv')\n",
    "\n",
    "df_critics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>review_content</th>\n",
       "      <th>review_type</th>\n",
       "      <th>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>A fantasy adventure that fuses Greek mythology...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Uma Thurman as Medusa, the gorgon with a coiff...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>With a top-notch cast and dazzling special eff...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>Whether audiences will get behind The Lightnin...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>m/0814255</td>\n",
       "      <td>Percy Jackson &amp; the Olympians: The Lightning T...</td>\n",
       "      <td>What's really lacking in The Lightning Thief i...</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  rotten_tomatoes_link                                        movie_title  \\\n",
       "0            m/0814255  Percy Jackson & the Olympians: The Lightning T...   \n",
       "1            m/0814255  Percy Jackson & the Olympians: The Lightning T...   \n",
       "2            m/0814255  Percy Jackson & the Olympians: The Lightning T...   \n",
       "3            m/0814255  Percy Jackson & the Olympians: The Lightning T...   \n",
       "4            m/0814255  Percy Jackson & the Olympians: The Lightning T...   \n",
       "\n",
       "                                      review_content review_type  \\\n",
       "0  A fantasy adventure that fuses Greek mythology...       Fresh   \n",
       "1  Uma Thurman as Medusa, the gorgon with a coiff...       Fresh   \n",
       "2  With a top-notch cast and dazzling special eff...       Fresh   \n",
       "3  Whether audiences will get behind The Lightnin...       Fresh   \n",
       "4  What's really lacking in The Lightning Thief i...      Rotten   \n",
       "\n",
       "  tomatometer_status  \n",
       "0             Rotten  \n",
       "1             Rotten  \n",
       "2             Rotten  \n",
       "3             Rotten  \n",
       "4             Rotten  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merge the two datasets\n",
    "df_merged = df_critics.merge(df_movie, how='inner', on='rotten_tomatoes_link')\n",
    "df_merged = df_merged[['rotten_tomatoes_link', 'movie_title', 'review_content', 'review_type', 'tomatometer_status']]\n",
    "df_merged.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, '25276'), Text(0, 0, '15919')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Drop entities with missing reviews\n",
    "df_merged = df_merged.dropna(subset=['review_content']) # subset allows you to specify the column in which you can delete NULLs\n",
    "\n",
    "# Plot distribution of the review\n",
    "ax = df_merged.review_type.value_counts().plot(kind='bar', figsize=(12,9))\n",
    "ax.bar_label(ax.containers[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There is a class imbalance problem here too\n",
    "- We need to add class weights while using the Random Forest algorithm\n",
    "- Also, we will be using only 5000 rows for training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_content</th>\n",
       "      <th>review_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A fantasy adventure that fuses Greek mythology...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Uma Thurman as Medusa, the gorgon with a coiff...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>With a top-notch cast and dazzling special eff...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Whether audiences will get behind The Lightnin...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>What's really lacking in The Lightning Thief i...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                      review_content  review_type\n",
       "0  A fantasy adventure that fuses Greek mythology...            1\n",
       "1  Uma Thurman as Medusa, the gorgon with a coiff...            1\n",
       "2  With a top-notch cast and dazzling special eff...            1\n",
       "3  Whether audiences will get behind The Lightnin...            1\n",
       "4  What's really lacking in The Lightning Thief i...            0"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pick only 5000 entries from the dataset\n",
    "df_sub = df_merged[0:10000]\n",
    "\n",
    "# Encode labels (since they're ordinal, we will use ordinal encoding)\n",
    "review_type = pd.DataFrame(df_sub['review_type'].replace(['Rotten', 'Fresh'], [0,1]))\n",
    "\n",
    "# Build final dataframe\n",
    "df_feature_critics = pd.concat([df_sub[['review_content']], review_type], axis=1).dropna()\n",
    "df_feature_critics.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split data into training and test data\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    df_feature_critics['review_content'], df_feature_critics['review_type'], test_size=0.2, \\\n",
    "        random_state=42\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Default Random Forest\n",
    "\n",
    "- We have the reviews in the form of strings (which are a set of words).\n",
    "- We need to convert these strings into a format that can be used by a ML algorithm.\n",
    "- This process is called Tokenization in NLP\n",
    "### Tokenization\n",
    "- We transform text or words into n-dimensional vectors and then use these vector representations as training data for our ML algorithm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.72      0.73      0.72       989\n",
      "           1       0.73      0.72      0.72      1011\n",
      "\n",
      "    accuracy                           0.72      2000\n",
      "   macro avg       0.72      0.72      0.72      2000\n",
      "weighted avg       0.72      0.72      0.72      2000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x281ba32b0>"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate vectorizer class\n",
    "vectorizer = CountVectorizer(min_df=1)\n",
    "\n",
    "# Train\n",
    "X_train_vec = vectorizer.fit_transform(X_train).toarray()\n",
    "\n",
    "# Predict\n",
    "rf = RandomForestClassifier(random_state=2)\n",
    "rf.fit(X_train_vec, y_train)\n",
    "\n",
    "# Print accuracy and classification report\n",
    "y_predicted = rf.predict(vectorizer.transform(X_test).toarray())\n",
    "\n",
    "print(classification_report(y_test, y_predicted))\n",
    "\n",
    "# Plot the confusion matrix\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_confusion_matrix(rf, vectorizer.transform(X_test).toarray(), y_test, cmap = 'YlGn', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Accuracy is 69%.\n",
    "- This is expected as we're training with only 6k examples.\n",
    "- We can improve the model performance by assigning class weights.\n",
    "\n",
    "### Weighted Random Forest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 1.0201999591920017, 1: 0.9805844283192783}"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculate class weights\n",
    "class_weight = compute_class_weight(class_weight='balanced', classes = np.unique(df_feature_critics['review_type']), \\\n",
    "    y = df_feature_critics['review_type'].values)\n",
    "class_weight_dict = dict(zip(range(len(class_weight.tolist())), class_weight.tolist()))\n",
    "class_weight_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.74      0.72       989\n",
      "           1       0.73      0.70      0.71      1011\n",
      "\n",
      "    accuracy                           0.72      2000\n",
      "   macro avg       0.72      0.72      0.72      2000\n",
      "weighted avg       0.72      0.72      0.72      2000\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.10/lib/python3.10/site-packages/sklearn/utils/deprecation.py:87: FutureWarning: Function plot_confusion_matrix is deprecated; Function `plot_confusion_matrix` is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: ConfusionMatrixDisplay.from_predictions or ConfusionMatrixDisplay.from_estimator.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<sklearn.metrics._plot.confusion_matrix.ConfusionMatrixDisplay at 0x2813fead0>"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x900 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Instantiate vectorizer class\n",
    "vectorizer = CountVectorizer(min_df=1)\n",
    "\n",
    "# Transform our text data into a vector\n",
    "X_train_vec = vectorizer.fit_transform(X_train).toarray()\n",
    "\n",
    "# Initialize random forest and train it\n",
    "rf_weighted = RandomForestClassifier(random_state=2, class_weight=class_weight_dict)\n",
    "rf_weighted.fit(X_train_vec, y_train)\n",
    "\n",
    "# Predict\n",
    "y_predicted = rf_weighted.predict(vectorizer.transform(X_test).toarray())\n",
    "\n",
    "# Classification Report and Confusion Matrix\n",
    "print(classification_report(y_test, y_predicted))\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(12,9))\n",
    "plot_confusion_matrix(rf_weighted, vectorizer.transform(X_test).toarray(), y_test, cmap = 'YlGn', ax=ax)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Movie status prediction\n",
    "- Gather all reviews from each movie\n",
    "- Use our random forest model to predict the status of each review (i.e., 'Fresh' or 'Rotten')\n",
    "- Apply the rule-based system described on the Rotten Tomatoes website to classify the final status of a movie, based on the overall review status.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define a function to predict movie status based on the overall sentiment\n",
    "def predict_movie_status(prediction):\n",
    "    \"\"\"Assign label (Fresh/Rotten) based on an array of predictions.\"\"\"\n",
    "    positive_percentage = (prediction == 1).sum() / len(prediction) * 100\n",
    "\n",
    "    prediction = 'Fresh' if positive_percentage >= 60 else 'Rotten'\n",
    "\n",
    "    print(f'Positive review: {positive_percentage}%')\n",
    "    print(f'Movie status: {prediction}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'Body of Lies' Prediction"
   ]
  },
  {
   "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>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>review_content</th>\n",
       "      <th>review_type</th>\n",
       "      <th>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7382</th>\n",
       "      <td>m/10009225-body_of_lies</td>\n",
       "      <td>Body of Lies</td>\n",
       "      <td>DiCaprio (not Crowe) is in top form in Scott's...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7383</th>\n",
       "      <td>m/10009225-body_of_lies</td>\n",
       "      <td>Body of Lies</td>\n",
       "      <td>Neither the location-based verisimilitude of R...</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7384</th>\n",
       "      <td>m/10009225-body_of_lies</td>\n",
       "      <td>Body of Lies</td>\n",
       "      <td>DiCaprio gives the role plenty of brio, while ...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7385</th>\n",
       "      <td>m/10009225-body_of_lies</td>\n",
       "      <td>Body of Lies</td>\n",
       "      <td>Convincing and gripping, Body of Lies is at th...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7386</th>\n",
       "      <td>m/10009225-body_of_lies</td>\n",
       "      <td>Body of Lies</td>\n",
       "      <td>Lies and consequences form the heart of this b...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Rotten</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         rotten_tomatoes_link   movie_title  \\\n",
       "7382  m/10009225-body_of_lies  Body of Lies   \n",
       "7383  m/10009225-body_of_lies  Body of Lies   \n",
       "7384  m/10009225-body_of_lies  Body of Lies   \n",
       "7385  m/10009225-body_of_lies  Body of Lies   \n",
       "7386  m/10009225-body_of_lies  Body of Lies   \n",
       "\n",
       "                                         review_content review_type  \\\n",
       "7382  DiCaprio (not Crowe) is in top form in Scott's...       Fresh   \n",
       "7383  Neither the location-based verisimilitude of R...      Rotten   \n",
       "7384  DiCaprio gives the role plenty of brio, while ...       Fresh   \n",
       "7385  Convincing and gripping, Body of Lies is at th...       Fresh   \n",
       "7386  Lies and consequences form the heart of this b...       Fresh   \n",
       "\n",
       "     tomatometer_status  \n",
       "7382             Rotten  \n",
       "7383             Rotten  \n",
       "7384             Rotten  \n",
       "7385             Rotten  \n",
       "7386             Rotten  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gather all reviews of Body of Lies movie\n",
    "df_bol = df_merged.loc[df_merged['movie_title'] == 'Body of Lies']\n",
    "df_bol.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive review: 54.41176470588235%\n",
      "Movie status: Rotten\n"
     ]
    }
   ],
   "source": [
    "# Get the prediction\n",
    "y_predicted_bol = rf_weighted.predict(vectorizer.transform(df_bol['review_content']).toarray())\n",
    "predict_movie_status(y_predicted_bol)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Rotten'], dtype=object)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the true label\n",
    "df_merged['tomatometer_status'].loc[df_merged['movie_title'] == 'Body of Lies'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'Angel Heart' Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>review_content</th>\n",
       "      <th>review_type</th>\n",
       "      <th>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>9240</th>\n",
       "      <td>m/1000959-angel_heart</td>\n",
       "      <td>Angel Heart</td>\n",
       "      <td>Angel Heart is a thriller and a horror movie, ...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9245</th>\n",
       "      <td>m/1000959-angel_heart</td>\n",
       "      <td>Angel Heart</td>\n",
       "      <td>Alan Parker's pulp sensibilities are beautiful...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9246</th>\n",
       "      <td>m/1000959-angel_heart</td>\n",
       "      <td>Angel Heart</td>\n",
       "      <td>One of the few truly great modern horror films</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9248</th>\n",
       "      <td>m/1000959-angel_heart</td>\n",
       "      <td>Angel Heart</td>\n",
       "      <td>Erotic, twisty and incredibly disturbed. Splen...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9249</th>\n",
       "      <td>m/1000959-angel_heart</td>\n",
       "      <td>Angel Heart</td>\n",
       "      <td>Este excepcional noir conta com maravilhosa fo...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       rotten_tomatoes_link  movie_title  \\\n",
       "9240  m/1000959-angel_heart  Angel Heart   \n",
       "9245  m/1000959-angel_heart  Angel Heart   \n",
       "9246  m/1000959-angel_heart  Angel Heart   \n",
       "9248  m/1000959-angel_heart  Angel Heart   \n",
       "9249  m/1000959-angel_heart  Angel Heart   \n",
       "\n",
       "                                         review_content review_type  \\\n",
       "9240  Angel Heart is a thriller and a horror movie, ...       Fresh   \n",
       "9245  Alan Parker's pulp sensibilities are beautiful...       Fresh   \n",
       "9246     One of the few truly great modern horror films       Fresh   \n",
       "9248  Erotic, twisty and incredibly disturbed. Splen...       Fresh   \n",
       "9249  Este excepcional noir conta com maravilhosa fo...       Fresh   \n",
       "\n",
       "     tomatometer_status  \n",
       "9240              Fresh  \n",
       "9245              Fresh  \n",
       "9246              Fresh  \n",
       "9248              Fresh  \n",
       "9249              Fresh  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gather all reviews of Body of Lies movie\n",
    "df_ah = df_merged.loc[df_merged['movie_title'] == 'Angel Heart']\n",
    "df_ah.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive review: 83.33333333333334%\n",
      "Movie status: Fresh\n"
     ]
    }
   ],
   "source": [
    "# Get the prediction\n",
    "y_predicted_ah = rf_weighted.predict(vectorizer.transform(df_ah['review_content']).toarray())\n",
    "predict_movie_status(y_predicted_ah)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fresh'], dtype=object)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the true label\n",
    "df_merged['tomatometer_status'].loc[df_merged['movie_title'] == 'Angel Heart'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 'The Duchess' Prediction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>rotten_tomatoes_link</th>\n",
       "      <th>movie_title</th>\n",
       "      <th>review_content</th>\n",
       "      <th>review_type</th>\n",
       "      <th>tomatometer_status</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8558</th>\n",
       "      <td>m/10009493-duchess</td>\n",
       "      <td>The Duchess</td>\n",
       "      <td>For all its luscious period trappings and Keir...</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8559</th>\n",
       "      <td>m/10009493-duchess</td>\n",
       "      <td>The Duchess</td>\n",
       "      <td>Solid but distant melodrama about the flamboya...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8560</th>\n",
       "      <td>m/10009493-duchess</td>\n",
       "      <td>The Duchess</td>\n",
       "      <td>It could have been an intriguing, poignant sag...</td>\n",
       "      <td>Rotten</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8561</th>\n",
       "      <td>m/10009493-duchess</td>\n",
       "      <td>The Duchess</td>\n",
       "      <td>Has riveting parallels with much more recent B...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8562</th>\n",
       "      <td>m/10009493-duchess</td>\n",
       "      <td>The Duchess</td>\n",
       "      <td>Ralph Fiennes steals it as the dour duke in a ...</td>\n",
       "      <td>Fresh</td>\n",
       "      <td>Fresh</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     rotten_tomatoes_link  movie_title  \\\n",
       "8558   m/10009493-duchess  The Duchess   \n",
       "8559   m/10009493-duchess  The Duchess   \n",
       "8560   m/10009493-duchess  The Duchess   \n",
       "8561   m/10009493-duchess  The Duchess   \n",
       "8562   m/10009493-duchess  The Duchess   \n",
       "\n",
       "                                         review_content review_type  \\\n",
       "8558  For all its luscious period trappings and Keir...      Rotten   \n",
       "8559  Solid but distant melodrama about the flamboya...       Fresh   \n",
       "8560  It could have been an intriguing, poignant sag...      Rotten   \n",
       "8561  Has riveting parallels with much more recent B...       Fresh   \n",
       "8562  Ralph Fiennes steals it as the dour duke in a ...       Fresh   \n",
       "\n",
       "     tomatometer_status  \n",
       "8558              Fresh  \n",
       "8559              Fresh  \n",
       "8560              Fresh  \n",
       "8561              Fresh  \n",
       "8562              Fresh  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Gather all reviews of Body of Lies movie\n",
    "df_td = df_merged.loc[df_merged['movie_title'] == 'The Duchess']\n",
    "df_td.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Positive review: 61.963190184049076%\n",
      "Movie status: Fresh\n"
     ]
    }
   ],
   "source": [
    "# Get the prediction\n",
    "y_predicted_td = rf_weighted.predict(vectorizer.transform(df_td['review_content']).toarray())\n",
    "predict_movie_status(y_predicted_td)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Fresh'], dtype=object)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get the true label\n",
    "df_merged['tomatometer_status'].loc[df_merged['movie_title'] == 'The Duchess'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model Improvement steps\n",
    "- Train the RF classifier with a larger dataset\n",
    "- Adjust the hyperparameters of the model.\n",
    "- Experiment with different ML algorithms.\n",
    "- Modify the method used to represent our text data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
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