2. TMDB Data Scraper & Analysis - Data Analysis.ipynb

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    "# TMDB Movie Analysis\n",
    "\n",
    "Author: Hilton Lam (2024)\n",
    "\n",
    "By leveraging their public API, I use datasets from TMDB, a popular movie and TV review platform, to explore key trends in the film industry. Using Python, I clean and preprocess the data before analysing patterns such as production volume and popularity by country, genre-based popularity, and the relationship between budget and popularity. Additionally, I leverage the **Gemini LLM API** to identify common themes among top-performing movies.\n",
    "\n",
    "### Notebook 2: Data Analysis\n",
    "\n",
    "This notebook is focused on cleaning, processing and analysing the dataset extracted from Notebook 1. \n",
    "\n",
    "Key trends shown in this notebook:\n",
    "- Production volume and popularity by country\n",
    "- Popularity distribution by genre\n",
    "- Corelation between budget and popularity\n",
    "- Common themes across top performing movies (using Gemini LLM)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69de2379-7cb1-4940-bb76-2f2dc6f0948d",
   "metadata": {},
   "source": [
    "### Import data from Notebook 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "096190a8-13d9-4d8b-b511-320d3494c71c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import ast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "b0a419d7-4051-4129-9a6e-85270bd3645d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use this step to retrieve the saved file, generated by Notebook 1\n",
    "df_movie_details = pd.read_csv('movie_details_backup.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5e4c232-3c97-4ac9-803d-ece6f101142e",
   "metadata": {},
   "source": [
    "### Transforming the dataframe for analysis\n",
    "\n",
    "Preparing the dataframe with the required fields for analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "fee232b5-a192-43ea-868c-66c54b2fc7eb",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>original_title</th>\n",
       "      <th>title</th>\n",
       "      <th>adult</th>\n",
       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
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       "      <th>release_date</th>\n",
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       "      <th>vote_count</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1184918</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>False</td>\n",
       "      <td>78000000</td>\n",
       "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 878, ...</td>\n",
       "      <td>['US']</td>\n",
       "      <td>en</td>\n",
       "      <td>After a shipwreck, an intelligent robot called...</td>\n",
       "      <td>2024-09-12</td>\n",
       "      <td>Discover your true nature.</td>\n",
       "      <td>8.600</td>\n",
       "      <td>1444</td>\n",
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       "      <th>1</th>\n",
       "      <td>912649</td>\n",
       "      <td>Venom: The Last Dance</td>\n",
       "      <td>Venom: The Last Dance</td>\n",
       "      <td>False</td>\n",
       "      <td>110000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 878, 'na...</td>\n",
       "      <td>['US']</td>\n",
       "      <td>en</td>\n",
       "      <td>Eddie and Venom are on the run. Hunted by both...</td>\n",
       "      <td>2024-10-22</td>\n",
       "      <td>'Til death do they part.</td>\n",
       "      <td>6.493</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>933260</td>\n",
       "      <td>The Substance</td>\n",
       "      <td>The Substance</td>\n",
       "      <td>False</td>\n",
       "      <td>17500000</td>\n",
       "      <td>[{'id': 27, 'name': 'Horror'}, {'id': 18, 'nam...</td>\n",
       "      <td>['GB']</td>\n",
       "      <td>en</td>\n",
       "      <td>A fading celebrity decides to use a black mark...</td>\n",
       "      <td>2024-09-07</td>\n",
       "      <td>If you follow the instructions, what could go ...</td>\n",
       "      <td>7.300</td>\n",
       "      <td>867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1029235</td>\n",
       "      <td>Azrael</td>\n",
       "      <td>Azrael</td>\n",
       "      <td>False</td>\n",
       "      <td>12000000</td>\n",
       "      <td>[{'id': 28, 'name': 'Action'}, {'id': 27, 'nam...</td>\n",
       "      <td>['US']</td>\n",
       "      <td>en</td>\n",
       "      <td>In a world where no one speaks, a devout femal...</td>\n",
       "      <td>2024-09-27</td>\n",
       "      <td>Many years after the rapture… Among the surviv...</td>\n",
       "      <td>6.111</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1034541</td>\n",
       "      <td>Terrifier 3</td>\n",
       "      <td>Terrifier 3</td>\n",
       "      <td>False</td>\n",
       "      <td>2000000</td>\n",
       "      <td>[{'id': 27, 'name': 'Horror'}, {'id': 53, 'nam...</td>\n",
       "      <td>['US']</td>\n",
       "      <td>en</td>\n",
       "      <td>Five years after surviving Art the Clown's Hal...</td>\n",
       "      <td>2024-10-09</td>\n",
       "      <td>You better not shout, you better not cry.</td>\n",
       "      <td>7.200</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "        id         original_title                  title  adult     budget  \\\n",
       "0  1184918         The Wild Robot         The Wild Robot  False   78000000   \n",
       "1   912649  Venom: The Last Dance  Venom: The Last Dance  False  110000000   \n",
       "2   933260          The Substance          The Substance  False   17500000   \n",
       "3  1029235                 Azrael                 Azrael  False   12000000   \n",
       "4  1034541            Terrifier 3            Terrifier 3  False    2000000   \n",
       "\n",
       "                                              genres origin_country  \\\n",
       "0  [{'id': 16, 'name': 'Animation'}, {'id': 878, ...         ['US']   \n",
       "1  [{'id': 28, 'name': 'Action'}, {'id': 878, 'na...         ['US']   \n",
       "2  [{'id': 27, 'name': 'Horror'}, {'id': 18, 'nam...         ['GB']   \n",
       "3  [{'id': 28, 'name': 'Action'}, {'id': 27, 'nam...         ['US']   \n",
       "4  [{'id': 27, 'name': 'Horror'}, {'id': 53, 'nam...         ['US']   \n",
       "\n",
       "  original_language                                           overview  \\\n",
       "0                en  After a shipwreck, an intelligent robot called...   \n",
       "1                en  Eddie and Venom are on the run. Hunted by both...   \n",
       "2                en  A fading celebrity decides to use a black mark...   \n",
       "3                en  In a world where no one speaks, a devout femal...   \n",
       "4                en  Five years after surviving Art the Clown's Hal...   \n",
       "\n",
       "  release_date                                            tagline  \\\n",
       "0   2024-09-12                         Discover your true nature.   \n",
       "1   2024-10-22                           'Til death do they part.   \n",
       "2   2024-09-07  If you follow the instructions, what could go ...   \n",
       "3   2024-09-27  Many years after the rapture… Among the surviv...   \n",
       "4   2024-10-09          You better not shout, you better not cry.   \n",
       "\n",
       "   vote_average  vote_count  \n",
       "0         8.600        1444  \n",
       "1         6.493          75  \n",
       "2         7.300         867  \n",
       "3         6.111          90  \n",
       "4         7.200         140  "
      ]
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   "source": [
    "# Create a dataframe with the selected columns only\n",
    "movie_fields = ['id','original_title','title', 'adult','budget','genres','origin_country','original_language','overview','release_date','tagline','vote_average','vote_count']\n",
    "df = df_movie_details[movie_fields] \n",
    "df.head()"
   ]
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   "cell_type": "code",
   "execution_count": 7,
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       "      <th></th>\n",
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       "      <th>count</th>\n",
       "      <td>4732.000</td>\n",
       "      <td>4732.000</td>\n",
       "      <td>4732.000</td>\n",
       "      <td>4732.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1276317.148</td>\n",
       "      <td>774129.673</td>\n",
       "      <td>1.394</td>\n",
       "      <td>90.081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>203942.250</td>\n",
       "      <td>7420232.658</td>\n",
       "      <td>2.931</td>\n",
       "      <td>1250.617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1280686.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1358219.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
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       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1366972.250</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1376826.000</td>\n",
       "      <td>195000000.000</td>\n",
       "      <td>10.000</td>\n",
       "      <td>35422.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               id        budget  vote_average  vote_count\n",
       "count    4732.000      4732.000      4732.000    4732.000\n",
       "mean  1276317.148    774129.673         1.394      90.081\n",
       "std    203942.250   7420232.658         2.931    1250.617\n",
       "min        81.000         0.000         0.000       0.000\n",
       "25%   1280686.250         0.000         0.000       0.000\n",
       "50%   1358219.500         0.000         0.000       0.000\n",
       "75%   1366972.250         0.000         0.000       0.000\n",
       "max   1376826.000 195000000.000        10.000   35422.000"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# Understand the dataset \n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x) # disable scientific numbers\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02626b5e-b3a5-4a2d-bfc2-63885bf45837",
   "metadata": {},
   "source": [
    "This shows that many movies (at least 75%+) have no budget indicated in the dataset and have 0 votes.\n",
    "\n",
    "Therefore, I will **remove those movies with limited votes and data in order to create a cleaner and representative dataset for analysis.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2e3c8fc4-3f2f-46c1-a1a7-edf9261da24f",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>budget</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>count</th>\n",
       "      <td>427.000</td>\n",
       "      <td>427.000</td>\n",
       "      <td>427.000</td>\n",
       "      <td>427.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>871705.265</td>\n",
       "      <td>8218876.768</td>\n",
       "      <td>6.679</td>\n",
       "      <td>996.419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>406330.368</td>\n",
       "      <td>23350609.610</td>\n",
       "      <td>0.993</td>\n",
       "      <td>4057.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>81.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>2.400</td>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>804833.500</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.192</td>\n",
       "      <td>13.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>995926.000</td>\n",
       "      <td>0.000</td>\n",
       "      <td>6.800</td>\n",
       "      <td>42.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1137206.000</td>\n",
       "      <td>3000000.000</td>\n",
       "      <td>7.300</td>\n",
       "      <td>208.500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1371727.000</td>\n",
       "      <td>195000000.000</td>\n",
       "      <td>9.200</td>\n",
       "      <td>35422.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               id        budget  vote_average  vote_count\n",
       "count     427.000       427.000       427.000     427.000\n",
       "mean   871705.265   8218876.768         6.679     996.419\n",
       "std    406330.368  23350609.610         0.993    4057.665\n",
       "min        81.000         0.000         2.400       5.000\n",
       "25%    804833.500         0.000         6.192      13.500\n",
       "50%    995926.000         0.000         6.800      42.000\n",
       "75%   1137206.000   3000000.000         7.300     208.500\n",
       "max   1371727.000 195000000.000         9.200   35422.000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Remove the movies that have less than 5 votes \n",
    "df_w_votes = df[(df['vote_count'] >= 5)]\n",
    "df_w_votes.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "79872c62-0fc7-49c6-b8ba-5d5dd5f1d60c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 427 entries, 0 to 4658\n",
      "Data columns (total 13 columns):\n",
      " #   Column             Non-Null Count  Dtype  \n",
      "---  ------             --------------  -----  \n",
      " 0   id                 427 non-null    int64  \n",
      " 1   original_title     427 non-null    object \n",
      " 2   title              427 non-null    object \n",
      " 3   adult              427 non-null    bool   \n",
      " 4   budget             427 non-null    int64  \n",
      " 5   genres             427 non-null    object \n",
      " 6   origin_country     427 non-null    object \n",
      " 7   original_language  427 non-null    object \n",
      " 8   overview           422 non-null    object \n",
      " 9   release_date       427 non-null    object \n",
      " 10  tagline            230 non-null    object \n",
      " 11  vote_average       427 non-null    float64\n",
      " 12  vote_count         427 non-null    int64  \n",
      "dtypes: bool(1), float64(1), int64(3), object(8)\n",
      "memory usage: 43.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df_w_votes.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "39e3fd65-abca-49be-ab44-7dc899793343",
   "metadata": {},
   "source": [
    "This indicates that only 427 movies have been rated at least 5 times, a very small subset of the total list of 4732 movies. This indiciates that **most movies have never been rated in this website**. Also, more than half of these movies do not have budget listed."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e0d02640-13c8-4b8f-99d8-0beea8f19dac",
   "metadata": {},
   "source": [
    "### Unpacking the arrays and joining with useful labels"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1952d13d-6073-4b3c-be5b-8d86732180aa",
   "metadata": {},
   "source": [
    "The columns \"origin_country\" and \"genre_name\" are both arrays. I am unpacking them here for analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9e6e057b-6a75-4c0c-942a-b3cadbd02259",
   "metadata": {},
   "outputs": [
    {
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       "      <th>budget</th>\n",
       "      <th>genres</th>\n",
       "      <th>origin_country</th>\n",
       "      <th>original_language</th>\n",
       "      <th>overview</th>\n",
       "      <th>release_date</th>\n",
       "      <th>tagline</th>\n",
       "      <th>vote_average</th>\n",
       "      <th>vote_count</th>\n",
       "      <th>genre_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1184918</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>False</td>\n",
       "      <td>78000000</td>\n",
       "      <td>{'id': 16, 'name': 'Animation'}</td>\n",
       "      <td>US</td>\n",
       "      <td>en</td>\n",
       "      <td>After a shipwreck, an intelligent robot called...</td>\n",
       "      <td>2024-09-12</td>\n",
       "      <td>Discover your true nature.</td>\n",
       "      <td>8.600</td>\n",
       "      <td>1444</td>\n",
       "      <td>Animation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1184918</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>False</td>\n",
       "      <td>78000000</td>\n",
       "      <td>{'id': 878, 'name': 'Science Fiction'}</td>\n",
       "      <td>US</td>\n",
       "      <td>en</td>\n",
       "      <td>After a shipwreck, an intelligent robot called...</td>\n",
       "      <td>2024-09-12</td>\n",
       "      <td>Discover your true nature.</td>\n",
       "      <td>8.600</td>\n",
       "      <td>1444</td>\n",
       "      <td>Science Fiction</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1184918</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>The Wild Robot</td>\n",
       "      <td>False</td>\n",
       "      <td>78000000</td>\n",
       "      <td>{'id': 10751, 'name': 'Family'}</td>\n",
       "      <td>US</td>\n",
       "      <td>en</td>\n",
       "      <td>After a shipwreck, an intelligent robot called...</td>\n",
       "      <td>2024-09-12</td>\n",
       "      <td>Discover your true nature.</td>\n",
       "      <td>8.600</td>\n",
       "      <td>1444</td>\n",
       "      <td>Family</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>912649</td>\n",
       "      <td>Venom: The Last Dance</td>\n",
       "      <td>Venom: The Last Dance</td>\n",
       "      <td>False</td>\n",
       "      <td>110000000</td>\n",
       "      <td>{'id': 28, 'name': 'Action'}</td>\n",
       "      <td>US</td>\n",
       "      <td>en</td>\n",
       "      <td>Eddie and Venom are on the run. Hunted by both...</td>\n",
       "      <td>2024-10-22</td>\n",
       "      <td>'Til death do they part.</td>\n",
       "      <td>6.493</td>\n",
       "      <td>75</td>\n",
       "      <td>Action</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>912649</td>\n",
       "      <td>Venom: The Last Dance</td>\n",
       "      <td>Venom: The Last Dance</td>\n",
       "      <td>False</td>\n",
       "      <td>110000000</td>\n",
       "      <td>{'id': 878, 'name': 'Science Fiction'}</td>\n",
       "      <td>US</td>\n",
       "      <td>en</td>\n",
       "      <td>Eddie and Venom are on the run. Hunted by both...</td>\n",
       "      <td>2024-10-22</td>\n",
       "      <td>'Til death do they part.</td>\n",
       "      <td>6.493</td>\n",
       "      <td>75</td>\n",
       "      <td>Science Fiction</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id         original_title                  title  adult     budget  \\\n",
       "0  1184918         The Wild Robot         The Wild Robot  False   78000000   \n",
       "0  1184918         The Wild Robot         The Wild Robot  False   78000000   \n",
       "0  1184918         The Wild Robot         The Wild Robot  False   78000000   \n",
       "1   912649  Venom: The Last Dance  Venom: The Last Dance  False  110000000   \n",
       "1   912649  Venom: The Last Dance  Venom: The Last Dance  False  110000000   \n",
       "\n",
       "                                   genres origin_country original_language  \\\n",
       "0         {'id': 16, 'name': 'Animation'}             US                en   \n",
       "0  {'id': 878, 'name': 'Science Fiction'}             US                en   \n",
       "0         {'id': 10751, 'name': 'Family'}             US                en   \n",
       "1            {'id': 28, 'name': 'Action'}             US                en   \n",
       "1  {'id': 878, 'name': 'Science Fiction'}             US                en   \n",
       "\n",
       "                                            overview release_date  \\\n",
       "0  After a shipwreck, an intelligent robot called...   2024-09-12   \n",
       "0  After a shipwreck, an intelligent robot called...   2024-09-12   \n",
       "0  After a shipwreck, an intelligent robot called...   2024-09-12   \n",
       "1  Eddie and Venom are on the run. Hunted by both...   2024-10-22   \n",
       "1  Eddie and Venom are on the run. Hunted by both...   2024-10-22   \n",
       "\n",
       "                      tagline  vote_average  vote_count       genre_name  \n",
       "0  Discover your true nature.         8.600        1444        Animation  \n",
       "0  Discover your true nature.         8.600        1444  Science Fiction  \n",
       "0  Discover your true nature.         8.600        1444           Family  \n",
       "1    'Til death do they part.         6.493          75           Action  \n",
       "1    'Til death do they part.         6.493          75  Science Fiction  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unnested = df_w_votes.copy() # Create a copy of the dataset\n",
    "\n",
    "# Convert the \"origin_country\" column from string to Python list & unnest the arrays\n",
    "df_unnested['origin_country'] = df_unnested['origin_country'].apply(ast.literal_eval)\n",
    "df_unnested = df_unnested.explode('origin_country') # Unnest the arrays\n",
    "\n",
    "# Repeat the same steps for \"genres\"\n",
    "df_unnested['genres'] = df_unnested['genres'].apply(ast.literal_eval)\n",
    "df_unnested = df_unnested.explode('genres') # Unnest the arrays\n",
    "\n",
    "# Unpack the dictionary \"name\" value in the column \"genres\"\n",
    "df_unnested['genre_name'] = [i['name'] for i in df_unnested['genres']]\n",
    "\n",
    "df_unnested.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "87c56398-0607-4ce4-bbf2-17b645c6832e",
   "metadata": {},
   "source": [
    "Map country names based on the country code using an external package"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "efb2d981-610b-4bc7-a7eb-a66eaf87733c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip3 install pycountry\n",
    "import pycountry\n",
    "\n",
    "country_codes = {code.alpha_2: code.name for code in pycountry.countries} # Create a dictionary mapping two-digit country codes to country names\n",
    "df_unnested['origin_country_name'] = df_unnested['origin_country'].map(country_codes) # Apply the mapping to the \"origin_country\" column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "e4887284-a606-465c-8412-79da7a79d5eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Check if any movies are mapped to no country name due to mapping gaps\n",
    "# print(\"No. of movies with unidentified country code:\",df_unnested['origin_country_name'].isnull().sum())\n",
    "# df_unnested[df_unnested['origin_country_name'].isnull()]\n",
    "\n",
    "# Two rows are identified as they are mapped to SU (Soviet Union), which we can exclude for now."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6005525a-93c7-43b3-aeec-0233fd298857",
   "metadata": {},
   "source": [
    "### Analysis by Country"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "9ed8a2ce-c423-4cf4-95c9-6aae59969646",
   "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>origin_country_name</th>\n",
       "      <th>median_vote_average</th>\n",
       "      <th>median_budget</th>\n",
       "      <th>movie_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>United States</td>\n",
       "      <td>6.700</td>\n",
       "      <td>140000.000</td>\n",
       "      <td>154</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>France</td>\n",
       "      <td>6.600</td>\n",
       "      <td>0.000</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Japan</td>\n",
       "      <td>7.400</td>\n",
       "      <td>0.000</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>6.900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Germany</td>\n",
       "      <td>6.900</td>\n",
       "      <td>0.000</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   origin_country_name  median_vote_average  median_budget  movie_count\n",
       "53       United States                6.700     140000.000          154\n",
       "13              France                6.600          0.000           71\n",
       "23               Japan                7.400          0.000           34\n",
       "52      United Kingdom                6.900          0.000           29\n",
       "15             Germany                6.900          0.000           22"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Group by 'origin_country_name' and calculate the median vote_average and budget\n",
    "df_country_stats = df_unnested.groupby('origin_country_name')[['vote_average', 'budget']].median().reset_index()\n",
    "\n",
    "# Calculate the count of unique \"id\" values for each country\n",
    "movie_count_series = df_unnested.groupby('origin_country_name')['id'].nunique().reset_index()\n",
    "\n",
    "# Rename the column for clarity\n",
    "movie_count_series.columns = ['origin_country_name', 'movie_count']\n",
    "\n",
    "# Merge the movie_count into df_country_stats\n",
    "df_country_stats = df_country_stats.merge(movie_count_series, on='origin_country_name', how='left')\n",
    "\n",
    "# Rename columns\n",
    "df_country_stats = df_country_stats.rename(columns={'vote_average': 'median_vote_average', 'budget': 'median_budget'})\n",
    "\n",
    "# Print the resulting DataFrame\n",
    "df_country_stats.sort_values(by=['movie_count'],ascending = False).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a232a38c-3efe-444f-b0ee-a21f6be69497",
   "metadata": {},
   "source": [
    "Most countries only have one movie listed with more than 5 votes, of which many of them do not have their budget listed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "7fcc990d-6a66-4478-8e4d-c9cf80ccf18e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# !pip install adjustText\n",
    "import matplotlib.pyplot as plt\n",
    "from adjustText import adjust_text\n",
    "\n",
    "# Create the scatter plot\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(df_country_stats['median_vote_average'], df_country_stats['movie_count'], color='blue')\n",
    "\n",
    "# Add title and axis labels\n",
    "plt.title('Movie Count vs Median Vote by Country')\n",
    "plt.xlabel('Median Vote')\n",
    "plt.ylabel('Movie Count')\n",
    "\n",
    "# Adding non-overlapping labels\n",
    "texts = []\n",
    "for i, row in df_country_stats.iterrows():\n",
    "    texts.append(plt.text(row['median_vote_average'], row['movie_count'], row['origin_country_name'], fontsize=8))\n",
    "adjust_text(texts, only_move={'points': 'y', 'texts': 'y'}, arrowprops=dict(arrowstyle=\"-\", color='gray', lw=0.5))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13f1778-a81a-4dd2-92a1-9371cdaec0e2",
   "metadata": {},
   "source": [
    "A few interesting trends can be seen here:\n",
    "1. The US is the powerhouse of movie production based on this dataset. They have produced a lot more movies than most other countries, but their quality does not seem to be higher than others accordingly. This may be caused by how TMDB collected data (i.e. focused on US movies).\n",
    "2. The median votes for most countries concerate around 6-8, especially when they have higher movie count. This may indicate a degree of randomness. When they produce a high volume movies (or a high number of them being on TMDB), the median score is about 6-7.\n",
    "\n",
    "I must highlight that the dataset only shows the users' opinions on TMDB, which is an English-based, American-centric platform. It represents what the voters on this platform think, rather than the reality of global movie production. Also, since the users are US-centric, they are more likely to watch and vote US movies, which may artificially inflate the number of movies produced in this analysis than the actual numbers in the world.\n",
    "\n",
    "Also, most countries have a small sample size in this dataset (<5 movies). Hence, it will not be wise to draw any overarching conclusions on movie quality at the country level based on this data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "32c257be-68a0-4de5-8180-9e825d977053",
   "metadata": {},
   "source": [
    "### Analysis by Genre"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d563b096-941f-4143-ae42-c97c728687ee",
   "metadata": {},
   "source": [
    "How do users rate different genres?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "ed96b9d1-6385-4376-a1a7-13a2dd3e37d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a dataframe for box plot\n",
    "df_genre_vote_average = df_unnested[['genre_name','vote_average']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "1283988e-5f75-4953-974a-f939a33e8bb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/n8/2y0_qtx54657nrc1941528_w0000gn/T/ipykernel_51210/4206429331.py:9: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df_genre_vote_average['genre_name'] = pd.Categorical(\n"
     ]
    }
   ],
   "source": [
    "# Get the median vote for each genre\n",
    "genre_median = df_genre_vote_average.groupby('genre_name')['vote_average'].median().reset_index()\n",
    "df_genre_sorted = genre_median.sort_values(by='vote_average',ascending=False)\n",
    "\n",
    "# Get the sorting order from df_genre_sorted\n",
    "genre_order = df_genre_sorted['genre_name']\n",
    "\n",
    "# Sort df \"df_genre_vote_average\" based on the order of \"genre_order\"\n",
    "df_genre_vote_average['genre_name'] = pd.Categorical(\n",
    "    df_genre_vote_average['genre_name'],\n",
    "    categories=genre_order,\n",
    "    ordered=True\n",
    ")\n",
    "# Sort df_genre_vote_average based on the new categorical order of genre_name\n",
    "df_genre_vote_average = df_genre_vote_average.sort_values('genre_name').reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "daa6ad14-278e-413d-b3be-06697c738464",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Adjust the figure size to make it wider\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Create the box plot\n",
    "plt.boxplot(df_genre_vote_average['vote_average'].groupby(df_genre_vote_average['genre_name'], observed = True).apply(list))\n",
    "\n",
    "# Set the title and labels\n",
    "plt.title('Distribution of Votes by Genre')\n",
    "plt.xlabel('Genre')\n",
    "plt.ylabel('Vote Score')\n",
    "\n",
    "# Rotate the x-axis labels for better readability (if needed)\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "# Set the x-ticks to match the unique genre names\n",
    "plt.xticks(range(1, len(df_genre_vote_average['genre_name'].unique()) + 1), df_genre_vote_average['genre_name'].unique())\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "86514fb6-d1d5-4cff-844d-f2695dda828f",
   "metadata": {},
   "source": [
    "Key takeaways:\n",
    "1. The difference in median vote across genres is very small. All genres have median vote between 6.5 and 7.5.\n",
    "2. However the variance of votes differs significantly by genres, i.e. some genres can be really good and really bad, whereas others can consistently good.\n",
    "3. Documentary, TV Movie and War movies tend to be consistently good with a high median vote and low variance.\n",
    "4. Science Fiction, Fantasy and Adventure have high median vote but also a high variance, meaning that you may sometimes get a very bad or very good movie in these genres.\n",
    "5. Horror movies have the lowest median vote, high variance and a low Q1 (lower quartile) score. This means that horror movies are often bad but sometimes you get some good ones."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9d997acf-9e7a-4303-af75-c98e774f8bea",
   "metadata": {},
   "source": [
    "## Corelation Analysis\n",
    "\n",
    "Let's now look at whether these movie characteristics have any corelations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "0dcad80b-e5bf-4b0d-bd3e-aee791745643",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              budget  vote_average  vote_count\n",
      "budget         1.000         0.149       0.464\n",
      "vote_average   0.149         1.000       0.472\n",
      "vote_count     0.464         0.472       1.000\n"
     ]
    }
   ],
   "source": [
    "# Create a df including only movies with at least 5 votes and budget indicated\n",
    "df_w_votes_budgets = df[(df['vote_count'] >= 5) & (df['budget'] > 0)] \n",
    "\n",
    "# Select only the numeric columns\n",
    "numeric_df = df_w_votes_budgets.select_dtypes(include=['float64', 'int64'])\n",
    "numeric_df.drop(columns=['id'], inplace = True)\n",
    "\n",
    "# Compute the correlation matrix for the numeric columns\n",
    "correlation_matrix = numeric_df.corr()\n",
    "\n",
    "# Display the correlation matrix\n",
    "print(correlation_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ef39cc4d-a0c9-43cf-9c04-b8ff5dfc637b",
   "metadata": {},
   "source": [
    "The Pearson Coefficients above indicate there are positive corelations as follows (in descending order):\n",
    "1. **Vote average and vote count (0.472)**: Average vote increases with vote count as well. This is interesting and unexpected. These could be reasons why that happened - a) users who like a movie are more likely to cast their vote on the platform, or/and b) movies that are more liked attract more votes (attention/viewership) due to words of mouth or viral effect.\n",
    "\n",
    "2. **Budget and vote count (0.464)**: The strong relationship shows that vote count increases with budget, which may be caused by the higher awareness of the film thanks to its bigger marketing efforts. As more people know and watch the movie, they are more likely to share their opinions on the website.\n",
    "\n",
    "3. **Budget and average vote (0.149)**: There is a weak positive relationship between budget and average vote, indicating that big budgets may not always lead to higher movie ratings."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4ce60b1-1793-4b21-80fb-61bcd2af3d55",
   "metadata": {},
   "source": [
    "#### Scatterplot showing relationship between budget and vote count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "d0d2b204-67ae-4dd8-9da4-cb50d53e6944",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.ticker import FuncFormatter\n",
    "\n",
    "# Scatter plot to show correlation between 'Popularity' and 'Average Vote'\n",
    "plt.figure(figsize=(5, 3))  \n",
    "plt.scatter(df_w_votes_budgets['budget'], df_w_votes_budgets['vote_count'], color='blue')\n",
    "\n",
    "# Titles and labels\n",
    "plt.title('Correlation between Budget and Vote Count')\n",
    "plt.xlabel('Budget')\n",
    "plt.ylabel('Vote Count')\n",
    "\n",
    "# Function to format numbers with K/M \n",
    "def millions(x, pos):\n",
    "    if x >= 1_000_000:\n",
    "        return f'{x/1_000_000:.0f}M'\n",
    "    elif x >= 1_000:\n",
    "        return f'{x/1_000:.0f}K'\n",
    "    else:\n",
    "        return f'{x:.0f}'\n",
    "\n",
    "# Apply custom formatter to axis\n",
    "plt.gca().xaxis.set_major_formatter(FuncFormatter(millions))\n",
    "plt.gca().yaxis.set_major_formatter(FuncFormatter(millions))\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e107916e-f640-4501-b628-2acab94b8dd5",
   "metadata": {},
   "source": [
    "## How do top movies differ from worst movies? A textual analysis using LLM.\n",
    "\n",
    "Here I would like to collate descriptions from each movie and put it through Google's Gemini Large Language Model (LLM). In this step, we can see whether we can identify any trends from the top movies' textual information.\n",
    "\n",
    "Information included in the text:\n",
    "1. Movie title\n",
    "2. Release date\n",
    "3. Original language\n",
    "4. Adult movie classification\n",
    "5. Movie overview\n",
    "6. Movie tagline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9f8e6d5f-19fe-4628-85a0-35e5eb8af1db",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a list of top movie id based on vote_average, limited to movies with at least 5 votes\n",
    "df_top_movies = df_w_votes.sort_values(by='vote_average', ascending = False).head(20) # top 20 movies bvy average vote\n",
    "df_worst_movies = df_w_votes.sort_values(by='vote_average', ascending = True).head(20) # worst 20 movies bvy average vote"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "ab710671-e118-4002-92ea-2eded8744f16",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create a function to concatenate the information for each movie and put them all in one paragraph\n",
    "top_movies_d = ''\n",
    "\n",
    "def movie_descriptor (df,text):\n",
    "    movie_description = ''\n",
    "    for i in range(len(df)):\n",
    "        movie_description = movie_description + f\"The ranking of this movie is {i+1} from the {text} based on user ratings. \"\n",
    "        movie_description = movie_description + \"The title of the movie is: \" + df.iloc[i]['title']\n",
    "        movie_description = movie_description + \", released on \" + df.iloc[i]['release_date']\n",
    "        movie_description = movie_description + \" and the original language is \" + df.iloc[i]['original_language']\n",
    "        if df.iloc[i]['adult'] == True:\n",
    "            movie_description = movie_description + \". It is an adult movie. \"\n",
    "        else: \n",
    "            movie_description = movie_description + \". It is not an adult movie. \"\n",
    "        movie_description = movie_description + \"Here is the overview of the movie: \" + df.iloc[i]['overview']\n",
    "        if pd.notna(df.iloc[i]['tagline']):\n",
    "            movie_description = movie_description + \" The tagline of the movie is: \" + str(df.iloc[i]['tagline'])\n",
    "        else:\n",
    "            pass\n",
    "        movie_description = movie_description + \"\\n\"\n",
    "    return movie_description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "d647eaa8-100c-4bc6-a22a-991ef62d6bd8",
   "metadata": {},
   "outputs": [],
   "source": [
    "top_movies_d = movie_descriptor(df_top_movies,'top')\n",
    "worst_movies_d = movie_descriptor(df_worst_movies,'bottom')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "3709a1b3-658e-4cab-9fb4-f31a8568e6a2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ! pip3 install google-generativeai\n",
    "\n",
    "import os\n",
    "import google.generativeai as genai\n",
    "import pathlib\n",
    "\n",
    "# Set the environment variable for the current session\n",
    "os.environ['API_KEY'] = 'insert_your_API_key_here' ## replace with your own API key\n",
    "genai.configure(api_key=os.environ[\"API_KEY\"])\n",
    "model = genai.GenerativeModel(\"gemini-1.5-flash\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6256f46f-9e25-4b08-90f1-7aa9ae4e58fe",
   "metadata": {},
   "source": [
    "### Provide the prompt to Gemini\n",
    "Ask Gemini to identify trends among the top 20 movies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "1c67d535-858b-430b-940a-574e76876a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = 'Here are the details of the most liked 20 movies in TMDB, a popular online movie rating website. Can you analyse the information and tell me what is special about these 20 popular movies? What is common among them? Please do not use external information, only the text given here.'\n",
    "prompt = prompt + top_movies_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b3f46011-e792-4c47-8cb0-de445f788799",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "These 20 popular movies, while diverse in their genres and time periods, share some common themes:\n",
      "\n",
      "* **Storytelling and Connection:** Many movies focus on the power of storytelling and its ability to connect people. \"The Door-to-Door Bookstore\" highlights the impact of books on individuals and communities. \"Flow\" uses a post-apocalyptic setting to explore themes of resilience and the importance of connection in a new world.  \"The Wild Robot\" emphasizes the role of storytelling in fostering empathy and understanding.\n",
      "\n",
      "* **Family and Relationships:**  Several movies explore the dynamics of family and relationships. \"Dying\" focuses on a family reuniting in the face of death. \"The Wild Robot\" showcases the bonds formed between a robot and the animal world. \"Grave of the Fireflies\" presents a poignant portrayal of sibling love and the challenges of survival. \"We Live in Time\" depicts a love story spanning decades.\n",
      "\n",
      "* **Overcoming Challenges and Resilience:**  Many stories depict characters overcoming adversity and challenges. \"The Count of Monte-Cristo\" showcases a man seeking revenge after years of imprisonment. \"The Lord of the Rings: The Return of the King\" features a battle against evil and the struggle for freedom. \"Interstellar\" focuses on space exploration and the challenges of human survival. \"10 Lives\" highlights a cat's journey of transformation and self-discovery.\n",
      "\n",
      "* **Personal Growth and Transformation:** Some movies highlight the journey of personal growth and transformation. \"The Door-to-Door Bookstore\" shows a man coming out of his shell and re-evaluating his life. \"The Wild Robot\" explores a robot's development of self-awareness and empathy. \"Ikiru\" depicts a man searching for meaning in the face of mortality.\n",
      "\n",
      "* **Themes of Justice and Redemption:**  Several movies explore themes of justice, redemption, and the consequences of actions. \"The Count of Monte-Cristo\" deals with revenge and the quest for justice. \"Pulp Fiction\" delves into the dark side of human nature and the search for redemption. \"The Silence of the Lambs\" explores the mind of a killer and the struggle to understand evil. \"High and Low\" examines the moral dilemmas of a powerful executive in a kidnapping scenario.\n",
      "\n",
      "Overall, these popular movies resonate with audiences because they offer relatable stories with universal themes of love, loss, hope, redemption, and the enduring power of human connection. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "response = model.generate_content(prompt)\n",
    "print(response.candidates[0].content.parts[0].text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "4aa6bee0-f371-4d6b-b79b-fe83e2d3759d",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = 'Here are the descriptions of the top 20 movies and the worst 20 movies, based on user ratings. Based on the information here (not externally), can you share any insights on why the top movies are better than the worst movies? '\n",
    "prompt = prompt + \"Description of top movies: \" + top_movies_d + \"\\n Description of worst movies: \" + worst_movies_d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3aece819-83ac-456f-bda5-9ed61ff40097",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the provided descriptions, here are some insights into why the top-rated movies are generally considered better than the bottom-rated ones:\n",
      "\n",
      "**Themes and Storytelling:**\n",
      "\n",
      "* **Top movies:**  Often explore complex themes like human connection, loss, the search for meaning, redemption, and the power of stories. They feature compelling narratives with well-developed characters and thought-provoking situations.  \n",
      "* **Bottom movies:** Tend towards simpler plots, relying on shock value, gore, or unoriginal premises. Characters often feel underdeveloped, and the story may lack emotional depth or a clear message.\n",
      "\n",
      "**Genre and Execution:**\n",
      "\n",
      "* **Top movies:** Represent a diverse range of genres (drama, fantasy, sci-fi, etc.) but are often praised for their unique and masterful execution within those genres.  \n",
      "* **Bottom movies:** Seem to rely on overly familiar, formulaic tropes within their respective genres.  They might lack originality or effective storytelling techniques.\n",
      "\n",
      "**Visuals and Technical Aspects:**\n",
      "\n",
      "* **Top movies:** Often boast impressive visuals, cinematography, sound design, and special effects that enhance the storytelling and immerse the viewer.\n",
      "* **Bottom movies:** May suffer from budget constraints or less experienced filmmaking, resulting in subpar visuals, editing, or sound.\n",
      "\n",
      "**Emotional Impact:**\n",
      "\n",
      "* **Top movies:** Aim to evoke a strong emotional response from the audience. They often leave a lasting impact, prompting reflection and discussion.\n",
      "* **Bottom movies:**  May rely on cheap scares or superficial thrills, failing to connect with the audience on an emotional level.\n",
      "\n",
      "**Critical Reception:**\n",
      "\n",
      "* **Top movies:** Frequently receive acclaim from critics, leading to awards and recognition within the film industry.\n",
      "* **Bottom movies:**  Often receive negative reviews from critics, highlighting their flaws and lack of originality.\n",
      "\n",
      "**However, it's important to remember that taste is subjective.** While the user ratings suggest a general consensus,  some viewers might prefer the bottom-rated movies for their specific tastes. Some viewers may enjoy action-packed thrillers or comedies, while others seek deeper emotional experiences. \n",
      "\n"
     ]
    }
   ],
   "source": [
    "response = model.generate_content(prompt)\n",
    "print(response.candidates[0].content.parts[0].text)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c2c575f-4967-4ba3-be7e-71858dd2997d",
   "metadata": {},
   "source": [
    "It is interesting to see that **LLM can be a useful tool for analysing film characteristics beyond the structured data in the tables.** Arguably, the plot of a film is critical in determining the success of a film, and we can only understand the plot by looking at the textual description of the movies. It will be very time consuming and inefficient for humans to analyse the plots in a movie database.\n",
    "\n",
    "While the results above are helpful and can provide a useful starting point for humans to do further analysis, **it will be better if we can provide much more information per film than the few lines of text currently offered to Gemini.** For example, we can provide more detailed plot descriptions, film transcripts and user reviews to give the LLM more context to analyse."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77ab1a64-fb21-46f2-91ec-7582a88daabd",
   "metadata": {},
   "source": [
    "## Conclusion "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0265bbc-c40d-49d4-a928-8dc1e1f834f3",
   "metadata": {},
   "source": [
    "**Key findings**\n",
    "1. The TMDB dataset is US-centric, as there are many more films from the US than from any other country. However, US films do not appear to have higher median ratings than productions from other countries.\n",
    "2. The median vote is very close across genres (6.5-7.5).\n",
    "3. The consistency (variance) in quality differs widely by genre, i.e. some genres such as documentaries, TV movies and war movies tend to be consistently good, while the quality in science fiction, fantasy, adventure and horror is not so consistent.\n",
    "4. Highly rated films tend to get more votes.\n",
    "5. High budget films tend to get more votes.\n",
    "6. There is a weak positive relationship between film budget and user ratings.\n",
    "7. Large Language Model (LLM) can be a helpful tool to understand why some movies perform better than others using textual information, but we should probably provide more information than a few lines of text per movie.\n",
    "\n",
    "**Challenges**\n",
    "The main challenge in this analysis is the size of the data - only a small fraction of the films (427 films out of 4732 currently playing movies) have at least 5 votes on the platform. Many of them have missing information such as budget. Therefore, when we break the data down by category, such as country, we are left with very few data points per category, making it very difficult to draw confident conclusions at the category level.\n",
    "\n",
    "This is partly because the TMDB API has not created an easy way for developers to pull all movies on the platform that are not in one of these categories 1) now playing, 2) popular, 3) upcoming or 4) top-rated movies. Hence, it is challenging to create a larger dataset than what we currently have.\n",
    "\n",
    "**Ideas for the future**\n",
    "1. For this analysis, I have only pulled now playing films. In the future, we could consider pulling more movies on the platform (now playing or not) to create a larger, more representative dataset. We can also consider using other movie review platforms with a larger dataset.\n",
    "2. To make LLM more useful, we should provide more textual information. We can consider pulling user reviews, detailed plots, or even transcripts for more comprehensive analysis.\n",
    "3. It will be interesting to see how film production and user preferences have changed over time. We may be able to pull more time series data to analyse trends over time."
   ]
  }
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