인공지능track_추천시스템.ipynb

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      "outputs": [
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          "output_type": "stream",
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          "text": [
            "Mounted at /content/gdrive/\n"
          ]
        }
      ],
      "source": [
        "#드라이브-코랩 연동 \n",
        "from google.colab import drive \n",
        "drive.mount('/content/gdrive/')\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cd /content/gdrive/MyDrive/dataset/movie_data"
      ],
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        },
        "id": "AHaIcqAa2ZgD",
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      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/dataset/movie_data\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip movie_data.zip"
      ],
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        },
        "id": "65XBivRo2v_4",
        "outputId": "08cd4401-ac74-4f59-9538-5dae334025d3"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  movie_data.zip\n",
            "  inflating: tmdb_5000_credits.csv   \n",
            "  inflating: tmdb_5000_movies.csv    \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ls"
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        "outputId": "8a3686d3-f043-4b74-80be-e6e3738768fe"
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      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "movie_data.zip  tmdb_5000_credits.csv  tmdb_5000_movies.csv\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import tensorflow as tf\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "Adfx8ln_29lW"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1 = pd.read_csv('./tmdb_5000_credits.csv')\n",
        "df2 = pd.read_csv('./tmdb_5000_movies.csv')"
      ],
      "metadata": {
        "id": "aUV0rXiH45GE"
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      "execution_count": 9,
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              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
              "1       285  Pirates of the Caribbean: At World's End   \n",
              "2    206647                                   Spectre   \n",
              "\n",
              "                                                cast  \\\n",
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              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
              "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
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              "      budget                                             genres  \\\n",
              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
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              "                                       homepage      id  \\\n",
              "0                   http://www.avatarmovie.com/   19995   \n",
              "1  http://disney.go.com/disneypictures/pirates/     285   \n",
              "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
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              "                                            keywords original_language  \\\n",
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              "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
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              "                             original_title  \\\n",
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              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
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              "                                            overview  popularity  \\\n",
              "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
              "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
              "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
              "\n",
              "                                production_companies  \\\n",
              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
              "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
              "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   \n",
              "\n",
              "                                production_countries release_date     revenue  \\\n",
              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
              "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   961000000   \n",
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              "   runtime                                   spoken_languages    status  \\\n",
              "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
              "1    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
              "2    148.0  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
              "\n",
              "                                          tagline  \\\n",
              "0                     Enter the World of Pandora.   \n",
              "1  At the end of the world, the adventure begins.   \n",
              "2                           A Plan No One Escapes   \n",
              "\n",
              "                                      title  vote_average  vote_count  \n",
              "0                                    Avatar           7.2       11800  \n",
              "1  Pirates of the Caribbean: At World's End           6.9        4500  \n",
              "2                                   Spectre           6.3        4466  "
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-a45d6446-0916-4aa5-8eae-efd558f84c64 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a45d6446-0916-4aa5-8eae-efd558f84c64');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OXdkW2cJ9C0V",
        "outputId": "03b42cbd-8119-440f-e4b3-45a8aa837f13"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1.columns = ['id', 'title', 'cast', 'crew']\n",
        "df2 = df2.merge(df1, on = 'id') # 'id를 교집합으로 병합'\n",
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QzTIECFS80PS",
        "outputId": "2b9a1a30-605b-4043-d048-194d1f8426a6"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "ajL-7QlL9Sw-",
        "outputId": "d67a8443-961d-4c10-970c-0346d4f2d6ac"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      budget                                             genres  \\\n",
              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
              "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
              "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "\n",
              "                                       homepage      id  \\\n",
              "0                   http://www.avatarmovie.com/   19995   \n",
              "1  http://disney.go.com/disneypictures/pirates/     285   \n",
              "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
              "3            http://www.thedarkknightrises.com/   49026   \n",
              "4          http://movies.disney.com/john-carter   49529   \n",
              "\n",
              "                                            keywords original_language  \\\n",
              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
              "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
              "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
              "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
              "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
              "\n",
              "                             original_title  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                            overview  popularity  \\\n",
              "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
              "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
              "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
              "3  Following the death of District Attorney Harve...  112.312950   \n",
              "4  John Carter is a war-weary, former military ca...   43.926995   \n",
              "\n",
              "                                production_companies  ...  \\\n",
              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...  ...   \n",
              "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...  ...   \n",
              "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...  ...   \n",
              "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...  ...   \n",
              "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]  ...   \n",
              "\n",
              "                                          tagline  \\\n",
              "0                     Enter the World of Pandora.   \n",
              "1  At the end of the world, the adventure begins.   \n",
              "2                           A Plan No One Escapes   \n",
              "3                                 The Legend Ends   \n",
              "4            Lost in our world, found in another.   \n",
              "\n",
              "                                    title_x  vote_average  vote_count  \\\n",
              "0                                    Avatar           7.2       11800   \n",
              "1  Pirates of the Caribbean: At World's End           6.9        4500   \n",
              "2                                   Spectre           6.3        4466   \n",
              "3                     The Dark Knight Rises           7.6        9106   \n",
              "4                               John Carter           6.1        2124   \n",
              "\n",
              "                                    title_y  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                              cast_x  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
              "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
              "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
              "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
              "\n",
              "                                              crew_x  \\\n",
              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...   \n",
              "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...   \n",
              "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...   \n",
              "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...   \n",
              "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...   \n",
              "\n",
              "                                      title  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                              cast_y  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
              "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
              "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
              "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
              "\n",
              "                                              crew_y  \n",
              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
              "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
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              "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
              "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  \n",
              "\n",
              "[5 rows x 26 columns]"
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              "      <th></th>\n",
              "      <th>budget</th>\n",
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              "      <th>cast_y</th>\n",
              "      <th>crew_y</th>\n",
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              "      <th>0</th>\n",
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              "      <td>19995</td>\n",
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              "      <td>Avatar</td>\n",
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              "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>300000000</td>\n",
              "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
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              "      <td>6.9</td>\n",
              "      <td>4500</td>\n",
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              "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>245000000</td>\n",
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              "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
              "      <td>en</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
              "      <td>107.376788</td>\n",
              "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
              "      <td>...</td>\n",
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              "      <td>6.3</td>\n",
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              "      <td>Spectre</td>\n",
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              "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
              "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>250000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
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              "      <td>49026</td>\n",
              "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
              "      <td>en</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>Following the death of District Attorney Harve...</td>\n",
              "      <td>112.312950</td>\n",
              "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
              "      <td>...</td>\n",
              "      <td>The Legend Ends</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>7.6</td>\n",
              "      <td>9106</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
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              "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>260000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>http://movies.disney.com/john-carter</td>\n",
              "      <td>49529</td>\n",
              "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
              "      <td>en</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>John Carter is a war-weary, former military ca...</td>\n",
              "      <td>43.926995</td>\n",
              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
              "      <td>...</td>\n",
              "      <td>Lost in our world, found in another.</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>6.1</td>\n",
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              "      <td>John Carter</td>\n",
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              "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
              "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d7e21122-42ea-4e88-81b7-405a6b723beb')\"\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Demographic Filtering** -\n",
        "   Before getting started with this  -\n",
        "* we need a metric to score or rate movie \n",
        "* Calculate the score for every movie \n",
        "* Sort the scores and recommend the best rated movie to the users.\n",
        "\n",
        "We can use the average ratings of the movie as the score but using this won't be fair enough since a movie with 8.9 average rating and only 3 votes cannot be considered better than the movie with 7.8 as as average rating but 40 votes.\n",
        "So, I'll be using IMDB's weighted rating (wr) which is given as :-\n",
        "\n",
        "![](https://image.ibb.co/jYWZp9/wr.png)\n",
        "where,\n",
        "* v is the number of votes for the movie;\n",
        "* m is the minimum votes required to be listed in the chart;\n",
        "* R is the average rating of the movie; And\n",
        "* C is the mean vote across the whole report\n",
        "\n",
        "We already have v(**vote_count**) and R (**vote_average**) and C can be calculated as "
      ],
      "metadata": {
        "id": "DQQFzgnfGcBE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# vote_average, vote_count 투표 지표 \n",
        "C = df2['vote_average'].mean()\n",
        "C # 모든 영화에 대한 전체 평균 "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C6cS83VP9bi5",
        "outputId": "ea68bd97-9ca9-4598-ae7d-c5ced1e0efb1"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6.092171559442016"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 90% 이상의 투표를 받은 영화들만 추천함\n",
        "m = df2['vote_count'].quantile(0.9) \n",
        "m"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7jNUQAhr-ali",
        "outputId": "749ed38a-9e7e-45c1-ce86-4b1142f62418"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1838.4000000000015"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "q_movies = df2.copy().loc[df2['vote_count']>=m]"
      ],
      "metadata": {
        "id": "NMhkwaOO-0UM"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "q_movies.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHZCIbSP_Gfe",
        "outputId": "f8d8e6bb-420c-4a4d-bf80-c1a6f089470c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(481, 26)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def weighted_rating(x, m=m, C=C):\n",
        "  V = x['vote_count'] \n",
        "  R = x['vote_average']\n",
        "\n",
        "  return (V/(V+m)*R) + (m/(m+V)*C)\n"
      ],
      "metadata": {
        "id": "mISckYXy_RtE"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "q_movies['score'] = q_movies.apply(weighted_rating, axis = 1) # 각 데이터를 weighted 함수를 적용한 scroe 열 추가 \n",
        "q_movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 785
        },
        "id": "ZUJmWgGgAgnB",
        "outputId": "163d1704-5b16-40b4-dca9-0d4a20983ae2"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      budget                                             genres  \\\n",
              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
              "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
              "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "\n",
              "                                       homepage      id  \\\n",
              "0                   http://www.avatarmovie.com/   19995   \n",
              "1  http://disney.go.com/disneypictures/pirates/     285   \n",
              "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
              "3            http://www.thedarkknightrises.com/   49026   \n",
              "4          http://movies.disney.com/john-carter   49529   \n",
              "\n",
              "                                            keywords original_language  \\\n",
              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
              "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
              "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
              "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
              "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
              "\n",
              "                             original_title  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                            overview  popularity  \\\n",
              "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
              "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
              "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
              "3  Following the death of District Attorney Harve...  112.312950   \n",
              "4  John Carter is a war-weary, former military ca...   43.926995   \n",
              "\n",
              "                                production_companies  ...  \\\n",
              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...  ...   \n",
              "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...  ...   \n",
              "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...  ...   \n",
              "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...  ...   \n",
              "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]  ...   \n",
              "\n",
              "                                    title_x vote_average  vote_count  \\\n",
              "0                                    Avatar          7.2       11800   \n",
              "1  Pirates of the Caribbean: At World's End          6.9        4500   \n",
              "2                                   Spectre          6.3        4466   \n",
              "3                     The Dark Knight Rises          7.6        9106   \n",
              "4                               John Carter          6.1        2124   \n",
              "\n",
              "                                    title_y  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                              cast_x  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
              "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
              "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
              "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
              "\n",
              "                                              crew_x  \\\n",
              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...   \n",
              "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...   \n",
              "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...   \n",
              "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...   \n",
              "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...   \n",
              "\n",
              "                                      title  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                              cast_y  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
              "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
              "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
              "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
              "\n",
              "                                              crew_y     score  \n",
              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  7.050669  \n",
              "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  6.665696  \n",
              "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  6.239396  \n",
              "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  7.346721  \n",
              "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  6.096368  \n",
              "\n",
              "[5 rows x 27 columns]"
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              "\n",
              "  <div id=\"df-69044897-8287-4ead-a3d7-67023361eab2\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>budget</th>\n",
              "      <th>genres</th>\n",
              "      <th>homepage</th>\n",
              "      <th>id</th>\n",
              "      <th>keywords</th>\n",
              "      <th>original_language</th>\n",
              "      <th>original_title</th>\n",
              "      <th>overview</th>\n",
              "      <th>popularity</th>\n",
              "      <th>production_companies</th>\n",
              "      <th>...</th>\n",
              "      <th>title_x</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>title_y</th>\n",
              "      <th>cast_x</th>\n",
              "      <th>crew_x</th>\n",
              "      <th>title</th>\n",
              "      <th>cast_y</th>\n",
              "      <th>crew_y</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>237000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>http://www.avatarmovie.com/</td>\n",
              "      <td>19995</td>\n",
              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
              "      <td>en</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
              "      <td>150.437577</td>\n",
              "      <td>[{\"name\": \"Ingenious Film Partners\", \"id\": 289...</td>\n",
              "      <td>...</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>7.2</td>\n",
              "      <td>11800</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
              "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
              "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
              "      <td>7.050669</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>300000000</td>\n",
              "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
              "      <td>http://disney.go.com/disneypictures/pirates/</td>\n",
              "      <td>285</td>\n",
              "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
              "      <td>en</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>Captain Barbossa, long believed to be dead, ha...</td>\n",
              "      <td>139.082615</td>\n",
              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...</td>\n",
              "      <td>...</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>6.9</td>\n",
              "      <td>4500</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
              "      <td>6.665696</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>245000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>http://www.sonypictures.com/movies/spectre/</td>\n",
              "      <td>206647</td>\n",
              "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
              "      <td>en</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
              "      <td>107.376788</td>\n",
              "      <td>[{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...</td>\n",
              "      <td>...</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>6.3</td>\n",
              "      <td>4466</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
              "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
              "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
              "      <td>6.239396</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>250000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
              "      <td>http://www.thedarkknightrises.com/</td>\n",
              "      <td>49026</td>\n",
              "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
              "      <td>en</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>Following the death of District Attorney Harve...</td>\n",
              "      <td>112.312950</td>\n",
              "      <td>[{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...</td>\n",
              "      <td>...</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>7.6</td>\n",
              "      <td>9106</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
              "      <td>7.346721</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>260000000</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>http://movies.disney.com/john-carter</td>\n",
              "      <td>49529</td>\n",
              "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
              "      <td>en</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>John Carter is a war-weary, former military ca...</td>\n",
              "      <td>43.926995</td>\n",
              "      <td>[{\"name\": \"Walt Disney Pictures\", \"id\": 2}]</td>\n",
              "      <td>...</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2124</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
              "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
              "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
              "      <td>6.096368</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 27 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-69044897-8287-4ead-a3d7-67023361eab2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# score 항목 기준으로 내림차순 정렬\n",
        "q_movies = q_movies.sort_values('score', ascending= False)\n",
        "q_movies[['title_x','vote_count', 'vote_average', 'score']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "rBdhFQfoA-On",
        "outputId": "bf8b793a-d6e0-4e0c-f3fe-54b85df65291"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       title_x  vote_count  vote_average     score\n",
              "1881  The Shawshank Redemption        8205           8.5  8.059258\n",
              "662                 Fight Club        9413           8.3  7.939256\n",
              "65             The Dark Knight       12002           8.2  7.920020\n",
              "3232              Pulp Fiction        8428           8.3  7.904645\n",
              "96                   Inception       13752           8.1  7.863239"
            ],
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>title_x</th>\n",
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              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1881</th>\n",
              "      <td>The Shawshank Redemption</td>\n",
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              "      <td>8.5</td>\n",
              "      <td>8.059258</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>662</th>\n",
              "      <td>Fight Club</td>\n",
              "      <td>9413</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.939256</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>The Dark Knight</td>\n",
              "      <td>12002</td>\n",
              "      <td>8.2</td>\n",
              "      <td>7.920020</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3232</th>\n",
              "      <td>Pulp Fiction</td>\n",
              "      <td>8428</td>\n",
              "      <td>8.3</td>\n",
              "      <td>7.904645</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>96</th>\n",
              "      <td>Inception</td>\n",
              "      <td>13752</td>\n",
              "      <td>8.1</td>\n",
              "      <td>7.863239</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-fd60d0b4-1564-4c28-b955-51f0d553f76b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "<인기 높은 순으로 추천>"
      ],
      "metadata": {
        "id": "UTmtcwXcEly7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "pop = df2.sort_values('popularity', ascending = False)\n",
        "\n",
        "plt.figure(figsize = (12,4))\n",
        "plt.barh(pop['title_x'].head(6), pop['popularity'].head(6), align = 'center', color = 'skyblue') #수평막대 그리기 \n",
        "plt.gca().invert_yaxis()\n",
        "plt.xlabel('pop')\n",
        "plt.title(\"popular movies\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 313
        },
        "id": "Ng0sUtcgByOZ",
        "outputId": "ef7e1de4-e84f-4dba-d53c-b17a1a7cf429"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'popular movies')"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Content Based Filtering**\n",
        "In this recommender system the content of the movie (overview, cast, crew, keyword, tagline etc) is used to find its similarity with other movies. Then the movies that are most likely to be similar are recommended.\n",
        "\n",
        "![](https://image.ibb.co/f6mDXU/conten.png)"
      ],
      "metadata": {
        "id": "Mvg9kG8FGqzQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['overview'].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uf4zjt6gHIYr",
        "outputId": "c0a8d3b6-f3df-4184-c2e8-fa7263d91503"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0    In the 22nd century, a paraplegic Marine is di...\n",
              "1    Captain Barbossa, long believed to be dead, ha...\n",
              "2    A cryptic message from Bond’s past sends him o...\n",
              "3    Following the death of District Attorney Harve...\n",
              "4    John Carter is a war-weary, former military ca...\n",
              "Name: overview, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2['overview'].shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ke_waePaIPUM",
        "outputId": "0d7f4835-740c-4707-edf4-90f038100a09"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803,)"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer # 문서를 tf-idf의 단어 벡터로 변환(인코딩)\n",
        "\n",
        "tfidf = TfidfVectorizer(stop_words = 'english') # 불용어 지정 가능 \n",
        "\n",
        "df2['overview'] = df2['overview'].fillna('') # NaN -> '' 로 변경 \n",
        "\n",
        "tfidf.fit(df2['overview']) # 문장 데이터를 단어 집합으로 생성함 \n",
        "tfidf.vocabulary_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0qrpZi2GrlT",
        "outputId": "202e0ffd-0486-4508-cef4-cb518749fcf0"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'22nd': 225,\n",
              " 'century': 3232,\n",
              " 'paraplegic': 13680,\n",
              " 'marine': 11667,\n",
              " 'dispatched': 5519,\n",
              " 'moon': 12411,\n",
              " 'pandora': 13644,\n",
              " 'unique': 19671,\n",
              " 'mission': 12269,\n",
              " 'torn': 19054,\n",
              " 'following': 7414,\n",
              " 'orders': 13355,\n",
              " 'protecting': 14754,\n",
              " 'alien': 795,\n",
              " 'civilization': 3545,\n",
              " 'captain': 2999,\n",
              " 'barbossa': 1733,\n",
              " 'long': 11220,\n",
              " 'believed': 1951,\n",
              " 'dead': 4854,\n",
              " 'come': 3843,\n",
              " 'life': 11025,\n",
              " 'headed': 8674,\n",
              " 'edge': 6069,\n",
              " 'earth': 6016,\n",
              " 'turner': 19418,\n",
              " 'elizabeth': 6188,\n",
              " 'swann': 18376,\n",
              " 'quite': 14969,\n",
              " 'cryptic': 4588,\n",
              " 'message': 12051,\n",
              " 'bond': 2353,\n",
              " 'past': 13749,\n",
              " 'sends': 16695,\n",
              " 'trail': 19141,\n",
              " 'uncover': 19550,\n",
              " 'sinister': 17127,\n",
              " 'organization': 13364,\n",
              " 'battles': 1824,\n",
              " 'political': 14308,\n",
              " 'forces': 7442,\n",
              " 'secret': 16611,\n",
              " 'service': 16753,\n",
              " 'alive': 806,\n",
              " 'peels': 13845,\n",
              " 'layers': 10854,\n",
              " 'deceit': 4909,\n",
              " 'reveal': 15769,\n",
              " 'terrible': 18739,\n",
              " 'truth': 19371,\n",
              " 'spectre': 17578,\n",
              " 'death': 4878,\n",
              " 'district': 5573,\n",
              " 'attorney': 1439,\n",
              " 'harvey': 8608,\n",
              " 'dent': 5109,\n",
              " 'batman': 1814,\n",
              " 'assumes': 1365,\n",
              " 'responsibility': 15697,\n",
              " 'crimes': 4490,\n",
              " 'protect': 14752,\n",
              " 'late': 10804,\n",
              " 'reputation': 15625,\n",
              " 'subsequently': 18130,\n",
              " 'hunted': 9210,\n",
              " 'gotham': 8136,\n",
              " 'city': 3538,\n",
              " 'police': 14296,\n",
              " 'department': 5121,\n",
              " 'years': 20854,\n",
              " 'later': 10806,\n",
              " 'encounters': 6318,\n",
              " 'mysterious': 12658,\n",
              " 'selina': 16670,\n",
              " 'kyle': 10681,\n",
              " 'villainous': 20099,\n",
              " 'bane': 1693,\n",
              " 'new': 12891,\n",
              " 'terrorist': 18753,\n",
              " 'leader': 10863,\n",
              " 'overwhelms': 13535,\n",
              " 'finest': 7225,\n",
              " 'dark': 4791,\n",
              " 'knight': 10581,\n",
              " 'resurfaces': 15729,\n",
              " 'branded': 2506,\n",
              " 'enemy': 6351,\n",
              " 'john': 10204,\n",
              " 'carter': 3091,\n",
              " 'war': 20301,\n",
              " 'weary': 20411,\n",
              " 'military': 12159,\n",
              " 'inexplicably': 9579,\n",
              " 'transported': 19202,\n",
              " 'exotic': 6738,\n",
              " 'planet': 14182,\n",
              " 'barsoom': 1779,\n",
              " 'mars': 11714,\n",
              " 'reluctantly': 15497,\n",
              " 'embroiled': 6245,\n",
              " 'epic': 6476,\n",
              " 'conflict': 4038,\n",
              " 'world': 20733,\n",
              " 'brink': 2607,\n",
              " 'collapse': 3774,\n",
              " 'rediscovers': 15342,\n",
              " 'humanity': 9171,\n",
              " 'realizes': 15209,\n",
              " 'survival': 18326,\n",
              " 'people': 13889,\n",
              " 'rests': 15720,\n",
              " 'hands': 8506,\n",
              " 'seemingly': 16646,\n",
              " 'invincible': 9894,\n",
              " 'spider': 17602,\n",
              " 'man': 11555,\n",
              " 'goes': 8069,\n",
              " 'crop': 4529,\n",
              " 'villain': 20097,\n",
              " 'including': 9502,\n",
              " 'shape': 16847,\n",
              " 'shifting': 16923,\n",
              " 'sandman': 16347,\n",
              " 'superpowers': 18255,\n",
              " 'altered': 856,\n",
              " 'organism': 13363,\n",
              " 'alter': 854,\n",
              " 'ego': 6117,\n",
              " 'peter': 13991,\n",
              " 'parker': 13698,\n",
              " 'deals': 4871,\n",
              " 'nemesis': 12849,\n",
              " 'eddie': 6065,\n",
              " 'brock': 2624,\n",
              " 'gets': 7934,\n",
              " 'caught': 3157,\n",
              " 'love': 11284,\n",
              " 'triangle': 19275,\n",
              " 'kingdom': 10533,\n",
              " 'wanted': 20297,\n",
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              " 'bandersnatches': 1685,\n",
              " 'reason': 15221,\n",
              " 'conquer': 4082,\n",
              " 'horrific': 9071,\n",
              " 'jabberwocky': 10009,\n",
              " 'restore': 15711,\n",
              " 'throne': 18882,\n",
              " 'cure': 4638,\n",
              " 'treat': 19247,\n",
              " 'mutations': 12641,\n",
              " 'lines': 11084,\n",
              " 'drawn': 5813,\n",
              " 'brotherhood': 2654,\n",
              " 'band': 1684,\n",
              " 'powerful': 14444,\n",
              " 'mutants': 12635,\n",
              " 'organized': 13368,\n",
              " 'xavier': 20821,\n",
              " 'magneto': 11465,\n",
              " 'look': 11230,\n",
              " 'relationship': 15467,\n",
              " 'mike': 12142,\n",
              " 'sulley': 18196,\n",
              " 'days': 4845,\n",
              " 'monsters': 12389,\n",
              " 'university': 19682,\n",
              " 'weren': 20470,\n",
              " 'necessarily': 12802,\n",
              " 'friends': 7638,\n",
              " 'sam': 16314,\n",
              " 'witwicky': 20663,\n",
              " 'autobots': 1502,\n",
              " 'normal': 13015,\n",
              " 'mind': 12183,\n",
              " 'symbols': 18453,\n",
              " 'decepticons': 4916,\n",
              " 'target': 18570,\n",
              " 'dragged': 5788,\n",
              " 'transformers': 19180,\n",
              " 'picks': 14077,\n",
              " 'pieces': 14086,\n",
              " 'conclusion': 3995,\n",
              " 'vanished': 19933,\n",
              " 'group': 8305,\n",
              " 'ingenious': 9621,\n",
              " 'businessman': 2794,\n",
              " 'scientists': 16508,\n",
              " 'attempt': 1422,\n",
              " 'transformer': 19179,\n",
              " 'incursions': 9526,\n",
              " 'push': 14896,\n",
              " 'boundaries': 2443,\n",
              " 'technology': 18637,\n",
              " 'control': 4196,\n",
              " 'ancient': 959,\n",
              " 'menace': 12003,\n",
              " 'cross': 4533,\n",
              " 'hairs': 8452,\n",
              " 'oscar': 13403,\n",
              " 'diggs': 5359,\n",
              " 'small': 17294,\n",
              " 'circus': 3530,\n",
              " 'illusionist': 9358,\n",
              " 'whisked': 20514,\n",
              " 'kansas': 10359,\n",
              " 'land': 10747,\n",
              " 'oz': 13555,\n",
              " 'inhabitants': 9633,\n",
              " 'assume': 1363,\n",
              " 'great': 8214,\n",
              " 'wizard': 20667,\n",
              " 'prophecy': 14718,\n",
              " 'clutches': 3693,\n",
              " 'busy': 2806,\n",
              " 'bad': 1619,\n",
              " 'guys': 8408,\n",
              " 'spending': 17594,\n",
              " 'loves': 11298,\n",
              " 'gwen': 8411,\n",
              " 'stacy': 17731,\n",
              " 'school': 16489,\n",
              " 'graduation': 8163,\n",
              " 'forgotten': 7473,\n",
              " 'promise': 14694,\n",
              " 'staying': 17830,\n",
              " 'electro': 6153,\n",
              " 'osborn': 13402,\n",
              " 'uncovers': 19553,\n",
              " 'clues': 3689,\n",
              " 'tech': 18628,\n",
              " 'savvy': 16408,\n",
              " 'son': 17458,\n",
              " 'kevin': 10466,\n",
              " 'investigates': 9881,\n",
              " 'pulled': 14841,\n",
              " 'grid': 8252,\n",
              " 'quorra': 14974,\n",
              " 'quests': 14952,\n",
              " 'dictator': 5331,\n",
              " 'clu': 3683,\n",
              " 'crossing': 4539,\n",
              " 'real': 15198,\n",
              " 'star': 17779,\n",
              " 'race': 14983,\n",
              " 'car': 3014,\n",
              " 'lightning': 11045,\n",
              " 'mcqueen': 11901,\n",
              " 'pal': 13607,\n",
              " 'mater': 11777,\n",
              " 'head': 8672,\n",
              " 'overseas': 13511,\n",
              " 'compete': 3924,\n",
              " 'grand': 8174,\n",
              " 'prix': 14614,\n",
              " 'road': 15947,\n",
              " 'championship': 3267,\n",
              " 'rocky': 16006,\n",
              " 'intriguing': 9849,\n",
              " 'espionage': 6556,\n",
              " 'centuries': 3231,\n",
              " 'warriors': 20336,\n",
              " 'green': 8224,\n",
              " 'lantern': 10779,\n",
              " 'corps': 4296,\n",
              " 'sworn': 18443,\n",
              " 'intergalactic': 9792,\n",
              " 'order': 13352,\n",
              " 'wears': 20410,\n",
              " 'grants': 8195,\n",
              " 'parallax': 13667,\n",
              " 'destroy': 5217,\n",
              " 'power': 14441,\n",
              " 'lie': 11019,\n",
              " 'human': 9169,\n",
              " 'recruited': 15315,\n",
              " 'woody': 20715,\n",
              " 'buzz': 2825,\n",
              " 'rest': 15700,\n",
              " 'andy': 976,\n",
              " 'toys': 19107,\n",
              " 'haven': 8639,\n",
              " 'played': 14211,\n",
              " 'accidentally': 433,\n",
              " 'nefarious': 12823,\n",
              " 'care': 3021,\n",
              " 'center': 3219,\n",
              " 'escape': 6544,\n",
              " 'home': 8985,\n",
              " 'grown': 8312,\n",
              " 'post': 14408,\n",
              " 'apocalyptic': 1094,\n",
              " '2018': 205,\n",
              " 'connor': 4080,\n",
              " 'lead': 10862,\n",
              " 'resistance': 15667,\n",
              " 'humans': 9174,\n",
              " 'increasingly': 9518,\n",
              " 'dominating': 5680,\n",
              " 'militaristic': 12157,\n",
              " 'robots': 15985,\n",
              " 'marcus': 11643,\n",
              " 'wright': 20786,\n",
              " 'appears': 1111,\n",
              " 'confuses': 4052,\n",
              " 'determine': 5245,\n",
              " 'foe': 7394,\n",
              " 'deckard': 4933,\n",
              " 'shaw': 16879,\n",
              " 'seeks': 16645,\n",
              " 'dominic': 5684,\n",
              " 'toretto': 19046,\n",
              " 'comatose': 3832,\n",
              " 'united': 19675,\n",
              " 'nations': 12756,\n",
              " 'investigator': 9886,\n",
              " 'gerry': 7927,\n",
              " 'lane': 10763,\n",
              " 'content': 4158,\n",
              " 'suddenly': 18167,\n",
              " 'plagued': 14173,\n",
              " 'infection': 9593,\n",
              " 'turning': 19419,\n",
              " 'populations': 14358,\n",
              " 'rampaging': 15082,\n",
              " 'mindless': 12187,\n",
              " 'zombies': 20956,\n",
              " 'barely': 1737,\n",
              " 'escaping': 6547,\n",
              " 'chaos': 3287,\n",
              " 'persuaded': 13973,\n",
              " 'investigate': 9879,\n",
              " 'disease': 5477,\n",
              " 'follows': 7415,\n",
              " 'perilous': 13925,\n",
              " 'trek': 19255,\n",
              " 'brave': 2521,\n",
              " 'dangers': 4767,\n",
              " 'odds': 13184,\n",
              " 'answers': 1043,\n",
              " 'ensemble': 6408,\n",
              " 'species': 17571,\n",
              " 'periods': 13929,\n",
              " 'join': 10210,\n",
              " 'younger': 20882,\n",
              " 'selves': 16679,\n",
              " 'enterprise': 6427,\n",
              " 'unstoppable': 19768,\n",
              " 'terror': 18749,\n",
              " 'detonated': 5252,\n",
              " 'fleet': 7323,\n",
              " 'stands': 17771,\n",
              " 'state': 17809,\n",
              " 'score': 16520,\n",
              " 'settle': 16771,\n",
              " 'kirk': 10551,\n",
              " 'leads': 10867,\n",
              " 'manhunt': 11586,\n",
              " 'zone': 20957,\n",
              " 'capture': 3009,\n",
              " 'weapon': 20404,\n",
              " 'mass': 11747,\n",
              " 'destruction': 5223,\n",
              " ...}"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(tfidf.vocabulary_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iCnK58CpZaE9",
        "outputId": "8efd5a3a-3379-4977-c485-866d61629330"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20978"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf_matrix = tfidf.fit_transform(df2['overview'], ) # fit_transform: 데이터로 학습하여 단어 집합으로 인코딩한 뒤, 벡터 형태로 변환 \n",
        "tfidf_matrix.shape # 각 문장- 단어 집합 벡터 값 = (4803, 20978)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKXpLWlfYOdp",
        "outputId": "47446baf-1bb5-4db2-abfd-b8c21e7285d6"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 20978)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# 코사인 유사도로 벡터간의 유사도 측정 \n",
        "from sklearn.metrics.pairwise import linear_kernel, cosine_similarity # cosine_similrarities() 와 같은 역할 수행 \n",
        "\n",
        "#cosine_sim1 = cosine_similarity(tfidf_matrix, tfidf_matrix)\n",
        "#cosine_sim2 = cosine_similarity(tfidf_matrix) \n",
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"
      ],
      "metadata": {
        "id": "5O4kzHVoITwr"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#cosine_sim1.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lriar-2Mb4YZ",
        "outputId": "0493f6fb-630e-46f4-e6ef-12bdb6c3ddff"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 4803)"
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cosine_sim2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dZFgjteXcBPV",
        "outputId": "580cb5df-374b-4630-a416-372fdfc747ef"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4803, 4803)"
            ]
          },
          "metadata": {},
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#cosine_sim1 == cosine_sim2"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1PGNVNBYcEYE",
        "outputId": "026cf798-d2dc-41df-e76e-b5c1c127869e"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True,  True,  True, ...,  True,  True,  True],\n",
              "       [ True,  True,  True, ...,  True,  True,  True],\n",
              "       [ True,  True,  True, ...,  True,  True,  True],\n",
              "       ...,\n",
              "       [ True,  True,  True, ...,  True,  True,  True],\n",
              "       [ True,  True,  True, ...,  True,  True,  True],\n",
              "       [ True,  True,  True, ...,  True,  True,  True]])"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "indices = pd.Series(df2.index, index = df2['title_x']).drop_duplicates() # 중복데이터 제거 \n",
        "indices"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Kf292QmSJsne",
        "outputId": "f4187ff4-cd8d-44b1-82c6-dfc6a57d88c5"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "title_x\n",
              "Avatar                                         0\n",
              "Pirates of the Caribbean: At World's End       1\n",
              "Spectre                                        2\n",
              "The Dark Knight Rises                          3\n",
              "John Carter                                    4\n",
              "                                            ... \n",
              "El Mariachi                                 4798\n",
              "Newlyweds                                   4799\n",
              "Signed, Sealed, Delivered                   4800\n",
              "Shanghai Calling                            4801\n",
              "My Date with Drew                           4802\n",
              "Length: 4803, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that takes in movie title as input and outputs most similar movies\n",
        "def get_recommendations(title, cosine_sim=cosine_sim):\n",
        "    # Get the index of the movie that matches the title\n",
        "    idx = indices[title]\n",
        "\n",
        "    # Get the pairwsie similarity scores of all movies with that movie\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "    print(sim_scores)\n",
        "    # Sort the movies based on the similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get the scores of the 10 most similar movies\n",
        "    sim_scores = sim_scores[1:11]\n",
        "\n",
        "    # Get the movie indices\n",
        "    movie_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # Return the top 10 most similar movies\n",
        "    return df2['title_x'].iloc[movie_indices]"
      ],
      "metadata": {
        "id": "Xzw5RP6mKRkc"
      },
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('The Dark Knight Rises') #content-based recommandation "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RWf2mlazK-S3",
        "outputId": "ef30de76-48ee-4d20-ed58-1bffdcea5d27"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "65                              The Dark Knight\n",
              "299                              Batman Forever\n",
              "428                              Batman Returns\n",
              "1359                                     Batman\n",
              "3854    Batman: The Dark Knight Returns, Part 2\n",
              "119                               Batman Begins\n",
              "2507                                  Slow Burn\n",
              "9            Batman v Superman: Dawn of Justice\n",
              "1181                                        JFK\n",
              "210                              Batman & Robin\n",
              "Name: title_x, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    }
  ]
}