Movie_recommender.ipynb

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      "execution_count": null,
      "metadata": {
        "id": "vy_ga1EsGSuO"
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      "source": [
        "!pip install opendatasets --quiet"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import opendatasets as od"
      ],
      "metadata": {
        "id": "zj42r7LRGZm6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "Ka7wmKt4HSiK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "url = 'https://www.kaggle.com/datasets/tmdb/tmdb-movie-metadata?select=tmdb_5000_movies.csv'"
      ],
      "metadata": {
        "id": "j_Tj7IkVGd6N"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "od.download(url)\n",
        "#user_name = manishkumar7432698\n",
        "#api = f60d7dd73290d421e95c6d0342be26d3"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Please provide your Kaggle credentials to download this dataset. Learn more: http://bit.ly/kaggle-creds\n",
            "Your Kaggle username: manishkumar7432698\n",
            "Your Kaggle Key: ··········\n",
            "Downloading tmdb-movie-metadata.zip to ./tmdb-movie-metadata\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 8.89M/8.89M [00:00<00:00, 119MB/s]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        },
        {
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          "name": "stderr",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movie_df = pd.read_csv('/content/tmdb-movie-metadata/tmdb_5000_movies.csv')\n",
        "credits_df = pd.read_csv('/content/tmdb-movie-metadata/tmdb_5000_credits.csv')"
      ],
      "metadata": {
        "id": "s5bSc7QIGh9x"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movie_df.sample(5)"
      ],
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          "height": 591
        },
        "id": "hjmwHg21He5z",
        "outputId": "03caa77e-9ae1-4efc-b3a6-d80efe42bd91"
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      "execution_count": null,
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              "         budget                                             genres  \\\n",
              "4509     550000  [{\"id\": 18, \"name\": \"Drama\"}, {\"id\": 53, \"name...   \n",
              "1135   42000000  [{\"id\": 80, \"name\": \"Crime\"}, {\"id\": 18, \"name...   \n",
              "253   110000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 10752, \"...   \n",
              "3881    3200000  [{\"id\": 18, \"name\": \"Drama\"}, {\"id\": 10749, \"n...   \n",
              "3266    8000000                     [{\"id\": 35, \"name\": \"Comedy\"}]   \n",
              "\n",
              "                                               homepage     id  \\\n",
              "4509                                                NaN  75986   \n",
              "1135                                                NaN   1830   \n",
              "253                         http://www.300themovie.com/  53182   \n",
              "3881                 http://focusfeatures.com/beginners  55347   \n",
              "3266  http://disney.go.com/disneypictures/prom/index...  51588   \n",
              "\n",
              "                                               keywords original_language  \\\n",
              "4509  [{\"id\": 1523, \"name\": \"obsession\"}, {\"id\": 213...                en   \n",
              "1135  [{\"id\": 2106, \"name\": \"cold war\"}, {\"id\": 6110...                en   \n",
              "253   [{\"id\": 18712, \"name\": \"based on graphic novel...                en   \n",
              "3881  [{\"id\": 237, \"name\": \"gay\"}, {\"id\": 936, \"name...                en   \n",
              "3266  [{\"id\": 641, \"name\": \"single parent\"}, {\"id\": ...                en   \n",
              "\n",
              "              original_title  \\\n",
              "4509            Love Letters   \n",
              "1135             Lord of War   \n",
              "253   300: Rise of an Empire   \n",
              "3881               Beginners   \n",
              "3266                    Prom   \n",
              "\n",
              "                                               overview  popularity  \\\n",
              "4509  A story of love and obsession. A young radio p...    0.001586   \n",
              "1135  Yuri Orlov is a globetrotting arms dealer and,...   34.828241   \n",
              "253   Based on Frank Miller's latest graphic novel X...   71.510596   \n",
              "3881  A young man is rocked by two announcements fro...   11.933449   \n",
              "3266  At “Prom,” every couple has a story and no two...   11.837067   \n",
              "\n",
              "                                   production_companies  \\\n",
              "4509  [{\"name\": \"New World Pictures\", \"id\": 1950}, {...   \n",
              "1135  [{\"name\": \"Saturn Films\", \"id\": 831}, {\"name\":...   \n",
              "253   [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   \n",
              "3881  [{\"name\": \"Parts and Labor\", \"id\": 6531}, {\"na...   \n",
              "3266  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
              "\n",
              "                                   production_countries release_date  \\\n",
              "4509  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   1983-04-01   \n",
              "1135  [{\"iso_3166_1\": \"FR\", \"name\": \"France\"}, {\"iso...   2005-09-16   \n",
              "253   [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2014-03-05   \n",
              "3881  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2010-09-11   \n",
              "3266  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2011-04-29   \n",
              "\n",
              "        revenue  runtime                                   spoken_languages  \\\n",
              "4509          0     98.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "1135   24127895    122.0  [{\"iso_639_1\": \"ar\", \"name\": \"\\u0627\\u0644\\u06...   \n",
              "253   337580051    102.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "3881    5332926    105.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...   \n",
              "3266   10130000    104.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "\n",
              "        status                                            tagline  \\\n",
              "4509  Released        Sometimes It's Right To Do the Wrong Thing.   \n",
              "1135  Released             Where there's a will, there's a weapon   \n",
              "253   Released                                  Seize your glory!   \n",
              "3881  Released                      This is what love feels like.   \n",
              "3266  Released  There are hundreds of nights in high school, b...   \n",
              "\n",
              "                       title  vote_average  vote_count  \n",
              "4509            Love Letters           4.6           4  \n",
              "1135             Lord of War           7.0        1335  \n",
              "253   300: Rise of an Empire           6.1        2397  \n",
              "3881               Beginners           6.8         347  \n",
              "3266                    Prom           5.7         134  "
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e233916b-a5fe-4cf7-8513-990486299f73 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The data is cluttered. It has lot of information but not organized well and it make difficult for us to analyze it. If we directly provide this data to the model, model will get confused in giving weights to attributes.Hence, We need preprocessing for extracting important and useful information."
      ],
      "metadata": {
        "id": "KERfKK-TIxvw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "credits_df.sample(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "_jEzPAFbJmS7",
        "outputId": "9e512789-2842-45ab-91ca-74131ea89112"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      movie_id                    title  \\\n",
              "3981     66942      Griff the Invisible   \n",
              "2753       492     Being John Malkovich   \n",
              "2042     19366  Josie and the Pussycats   \n",
              "\n",
              "                                                   cast  \\\n",
              "3981  [{\"cast_id\": 3, \"character\": \"Griff\", \"credit_...   \n",
              "2753  [{\"cast_id\": 5, \"character\": \"Craig Schwartz\",...   \n",
              "2042  [{\"cast_id\": 8, \"character\": \"Josie McCoy\", \"c...   \n",
              "\n",
              "                                                   crew  \n",
              "3981  [{\"credit_id\": \"52fe4751c3a368484e0c0541\", \"de...  \n",
              "2753  [{\"credit_id\": \"52fe4249c3a36847f80128f7\", \"de...  \n",
              "2042  [{\"credit_id\": \"5645da789251410a3f008cb3\", \"de...  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_id</th>\n",
              "      <th>title</th>\n",
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              "      <th>crew</th>\n",
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              "  </thead>\n",
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              "      <th>3981</th>\n",
              "      <td>66942</td>\n",
              "      <td>Griff the Invisible</td>\n",
              "      <td>[{\"cast_id\": 3, \"character\": \"Griff\", \"credit_...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4751c3a368484e0c0541\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2753</th>\n",
              "      <td>492</td>\n",
              "      <td>Being John Malkovich</td>\n",
              "      <td>[{\"cast_id\": 5, \"character\": \"Craig Schwartz\",...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4249c3a36847f80128f7\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2042</th>\n",
              "      <td>19366</td>\n",
              "      <td>Josie and the Pussycats</td>\n",
              "      <td>[{\"cast_id\": 8, \"character\": \"Josie McCoy\", \"c...</td>\n",
              "      <td>[{\"credit_id\": \"5645da789251410a3f008cb3\", \"de...</td>\n",
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              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-6dae812b-1ef1-4227-9996-488e2a65d183 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-6dae812b-1ef1-4227-9996-488e2a65d183');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-57195e8d-b86d-4278-8b60-7cff31097931\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-57195e8d-b86d-4278-8b60-7cff31097931')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-57195e8d-b86d-4278-8b60-7cff31097931 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "credits_df['crew'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "XV_idXxzJYEX",
        "outputId": "f5f3b8d0-ecbe-456f-8e73-de810382fcc3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[{\"credit_id\": \"52fe48009251416c750aca23\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1721, \"job\": \"Editor\", \"name\": \"Stephen E. 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Hernandez\"}, {\"credit_id\": \"5495a0ddc3a3686ae10046fe\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1401815, \"job\": \"Digital Intermediate\", \"name\": \"Marvin Hall\"}, {\"credit_id\": \"5495a1f7c3a3686ae3004443\", \"department\": \"Production\", \"gender\": 0, \"id\": 1401816, \"job\": \"Publicist\", \"name\": \"Judy Alley\"}, {\"credit_id\": \"5592b29fc3a36869d100002f\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1418381, \"job\": \"CG Supervisor\", \"name\": \"Mike Perry\"}, {\"credit_id\": \"5592b23a9251415df8001081\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1426854, \"job\": \"CG Supervisor\", \"name\": \"Andrew Morley\"}, {\"credit_id\": \"55491e1192514104c40002d8\", \"department\": \"Art\", \"gender\": 0, \"id\": 1438901, \"job\": \"Conceptual Design\", \"name\": \"Seth Engstrom\"}, {\"credit_id\": \"5525d5809251417276002b06\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1447362, \"job\": \"Visual Effects Art Director\", \"name\": \"Eric Oliver\"}, {\"credit_id\": \"554427ca925141586500312a\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1447503, \"job\": \"Modeling\", \"name\": \"Matsune Suzuki\"}, {\"credit_id\": \"551906889251415aab001c88\", \"department\": \"Art\", \"gender\": 0, \"id\": 1447524, \"job\": \"Art Department Manager\", \"name\": \"Paul Tobin\"}, {\"credit_id\": \"5592af8492514152cc0010de\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1452643, \"job\": \"Hairstylist\", \"name\": \"Roxane Griffin\"}, {\"credit_id\": \"553d3c109251415852001318\", \"department\": \"Lighting\", \"gender\": 0, \"id\": 1453938, \"job\": \"Lighting Artist\", \"name\": \"Arun Ram-Mohan\"}, {\"credit_id\": \"5592af4692514152d5001355\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1457305, \"job\": \"Makeup Artist\", \"name\": \"Georgia Lockhart-Adams\"}, {\"credit_id\": \"5592b2eac3a36877470012a5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1466035, \"job\": \"CG Supervisor\", \"name\": \"Thrain Shadbolt\"}, {\"credit_id\": \"5592b032c3a36877450015f1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483220, \"job\": \"CG Supervisor\", \"name\": \"Brad Alexander\"}, {\"credit_id\": \"5592b05592514152d80012f6\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483221, \"job\": \"CG Supervisor\", \"name\": \"Shadi Almassizadeh\"}, {\"credit_id\": \"5592b090c3a36877570010b5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483222, \"job\": \"CG Supervisor\", \"name\": \"Simon Clutterbuck\"}, {\"credit_id\": \"5592b0dbc3a368774b00112c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483223, \"job\": \"CG Supervisor\", \"name\": \"Graeme Demmocks\"}, {\"credit_id\": \"5592b0fe92514152db0010c1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483224, \"job\": \"CG Supervisor\", \"name\": \"Adrian Fernandes\"}, {\"credit_id\": \"5592b11f9251415df8001059\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483225, \"job\": \"CG Supervisor\", \"name\": \"Mitch Gates\"}, {\"credit_id\": \"5592b15dc3a3687745001645\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483226, \"job\": \"CG Supervisor\", \"name\": \"Jerry Kung\"}, {\"credit_id\": \"5592b18e925141645a0004ae\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483227, \"job\": \"CG Supervisor\", \"name\": \"Andy Lomas\"}, {\"credit_id\": \"5592b1bfc3a368775d0010e7\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483228, \"job\": \"CG Supervisor\", \"name\": \"Sebastian Marino\"}, {\"credit_id\": \"5592b2049251415df8001078\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483229, \"job\": \"CG Supervisor\", \"name\": \"Matthias Menz\"}, {\"credit_id\": \"5592b27b92514152d800136a\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483230, \"job\": \"CG Supervisor\", \"name\": \"Sergei Nevshupov\"}, {\"credit_id\": \"5592b2c3c3a36869e800003c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483231, \"job\": \"CG Supervisor\", \"name\": \"Philippe Rebours\"}, {\"credit_id\": \"5592b317c3a36877470012af\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483232, \"job\": \"CG Supervisor\", \"name\": \"Michael Takarangi\"}, {\"credit_id\": \"5592b345c3a36877470012bb\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483233, \"job\": \"CG Supervisor\", \"name\": \"David Weitzberg\"}, {\"credit_id\": \"5592b37cc3a368775100113b\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483234, \"job\": \"CG Supervisor\", \"name\": \"Ben White\"}, {\"credit_id\": \"573c8e2f9251413f5d000094\", \"department\": \"Crew\", \"gender\": 1, \"id\": 1621932, \"job\": \"Stunts\", \"name\": \"Min Windle\"}]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our metadata dataset is also cluttered and diffiult to analyze.\n",
        "Preprocesssing is required."
      ],
      "metadata": {
        "id": "MPNTujtOJlVa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "The first thing is we should merge the two datasets."
      ],
      "metadata": {
        "id": "7Lf0mN5AOIvB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movie = movie_df.merge(credits_df, on = 'title')"
      ],
      "metadata": {
        "id": "ePOPmpyWKrN6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movie.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jHR3TNpjQ0QD",
        "outputId": "8285db7b-1213-4f72-cc86-9683b6cf3e59"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['budget', 'genres', 'homepage', 'id', 'keywords', 'original_language',\n",
              "       'original_title', 'overview', 'popularity', 'production_companies',\n",
              "       'production_countries', 'release_date', 'revenue', 'runtime',\n",
              "       'spoken_languages', 'status', 'tagline', 'title', 'vote_average',\n",
              "       'vote_count', 'movie_id', 'cast', 'crew'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movie.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MmwAQXHJO6mG",
        "outputId": "af0b2fb4-b453-4a89-cf9a-04de252f6e42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4809, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our data has lot of information and attributes and in some of the attributes also we have lot of data. Therefore,we need some relaxation from too much information. We will filter out some data according to our requirement means the data which help us in recommendation system.\n",
        "\n",
        "Like no-one search a movie by its camera man name or any secondary crew. Hence, we will remove this data from our data set.\n",
        "\n",
        "So, here is the list which is required for recommending a movie to a user which is based on user searches and personal experience.\n",
        "\n",
        "\n",
        "1.   Genre\n",
        "2.   id ( not for recommendation but for setting up a website it is important\n",
        "3.   keywords\n",
        "4.   title\n",
        "5.   overview\n",
        "6.   production_company( will use it later currently it will create complexity)\n",
        "7.   release_date(currently not using but an important factor)\n",
        "8.   cast\n",
        "9.   crew(due to director)"
      ],
      "metadata": {
        "id": "0Xdvhz3qPQOe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies = movie[['movie_id','title','genres','keywords','overview','cast','crew']]"
      ],
      "metadata": {
        "id": "3IbejDFaOYno"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "To create a recommendation system, I will create a new dataframe in which we will have only 3 attributes\n",
        "1.   movie_id\n",
        "2.   title\n",
        "3.   Tag\n",
        "\n",
        "In tag, we will have combination of all other columns apart from movie_id and title from movies dataset. But for this purpose, first I need to clean different columns and extract only required keywords from those attributes.\n",
        "\n"
      ],
      "metadata": {
        "id": "cNNvp7qPnhh2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Data Preprocessing**"
      ],
      "metadata": {
        "id": "N7-er2OJooC8"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's do the above mentioned but to perform that, we need to clean and reduce cluterness of data."
      ],
      "metadata": {
        "id": "m9CZWJ06oWfC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Handling Null values**"
      ],
      "metadata": {
        "id": "b8p1BlcPohOk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nkx-tnWITjzV",
        "outputId": "3dc043a5-f5db-45e0-be27-cb0ea7634dfd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movie_id    0\n",
              "title       0\n",
              "genres      0\n",
              "keywords    0\n",
              "overview    3\n",
              "cast        0\n",
              "crew        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies = movies.dropna()"
      ],
      "metadata": {
        "id": "cHKsvnLCo2Ai"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aGt3--6Ao_MJ",
        "outputId": "7b738cf9-3632-4a3c-9854-f740351aeccd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movie_id    0\n",
              "title       0\n",
              "genres      0\n",
              "keywords    0\n",
              "overview    0\n",
              "cast        0\n",
              "crew        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Checking** **duplicates**"
      ],
      "metadata": {
        "id": "gneHIAfFstue"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "477mUgYao4HP",
        "outputId": "0e59aa91-8a68-491c-c635-c9153db5c8ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Formatting of attributes**"
      ],
      "metadata": {
        "id": "uk7UuT0Vs6QZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head(1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 98
        },
        "id": "9OdkBEkRtqOA",
        "outputId": "575445b8-7c89-4a76-bd49-893966eedd09"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movie_id   title                                             genres  \\\n",
              "0     19995  Avatar  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "\n",
              "                                            keywords  \\\n",
              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
              "\n",
              "                                            overview  \\\n",
              "0  In the 22nd century, a paraplegic Marine is di...   \n",
              "\n",
              "                                                cast  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "\n",
              "                                                crew  \n",
              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-baff7cf5-a32e-40e9-abfd-d9eaa2ecc1c1\" class=\"colab-df-container\">\n",
              "    <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>movie_id</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>keywords</th>\n",
              "      <th>overview</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19995</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
              "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
              "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
              "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-baff7cf5-a32e-40e9-abfd-d9eaa2ecc1c1')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-baff7cf5-a32e-40e9-abfd-d9eaa2ecc1c1 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-baff7cf5-a32e-40e9-abfd-d9eaa2ecc1c1');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import ast"
      ],
      "metadata": {
        "id": "xZsuN6o6uiaS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "ast module converts string to list."
      ],
      "metadata": {
        "id": "R8JFbgVQuwF6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract(obj):\n",
        "  l = []\n",
        "  for i in ast.literal_eval(obj):\n",
        "    l.append(i['name'])\n",
        "  return l"
      ],
      "metadata": {
        "id": "s_WN3Xmqt2TH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies['genres'] = movies.genres.apply(extract)"
      ],
      "metadata": {
        "id": "ibFNmXJ8stW_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies['genres']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuFncrjZVaOe",
        "outputId": "b740c065-0dba-4bba-b3bc-ae9300bf7a8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       [Action, Adventure, Fantasy, Science Fiction]\n",
              "1                        [Adventure, Fantasy, Action]\n",
              "2                          [Action, Adventure, Crime]\n",
              "3                    [Action, Crime, Drama, Thriller]\n",
              "4                [Action, Adventure, Science Fiction]\n",
              "                            ...                      \n",
              "4804                        [Action, Crime, Thriller]\n",
              "4805                                [Comedy, Romance]\n",
              "4806               [Comedy, Drama, Romance, TV Movie]\n",
              "4807                                               []\n",
              "4808                                    [Documentary]\n",
              "Name: genres, Length: 4806, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies['keywords'] = movies.keywords.apply(extract)"
      ],
      "metadata": {
        "id": "wT7EcuufvDqm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_actor(obj):\n",
        "  l = []\n",
        "  count = 0\n",
        "  for i in ast.literal_eval(obj):\n",
        "    if count < 3:\n",
        "      l.append(i['name'])\n",
        "      count+=1\n",
        "    else:\n",
        "       return l"
      ],
      "metadata": {
        "id": "X4bxxt1sWlPL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.cast = movies.cast.apply(extract_actor)"
      ],
      "metadata": {
        "id": "sINpu5Sf0peS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.crew[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        },
        "id": "QwwqRgGG01VR",
        "outputId": "cc2e54e3-8083-490e-b9e8-89256d1860d0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
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Hernandez\"}, {\"credit_id\": \"5495a0ddc3a3686ae10046fe\", \"department\": \"Editing\", \"gender\": 0, \"id\": 1401815, \"job\": \"Digital Intermediate\", \"name\": \"Marvin Hall\"}, {\"credit_id\": \"5495a1f7c3a3686ae3004443\", \"department\": \"Production\", \"gender\": 0, \"id\": 1401816, \"job\": \"Publicist\", \"name\": \"Judy Alley\"}, {\"credit_id\": \"5592b29fc3a36869d100002f\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1418381, \"job\": \"CG Supervisor\", \"name\": \"Mike Perry\"}, {\"credit_id\": \"5592b23a9251415df8001081\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1426854, \"job\": \"CG Supervisor\", \"name\": \"Andrew Morley\"}, {\"credit_id\": \"55491e1192514104c40002d8\", \"department\": \"Art\", \"gender\": 0, \"id\": 1438901, \"job\": \"Conceptual Design\", \"name\": \"Seth Engstrom\"}, {\"credit_id\": \"5525d5809251417276002b06\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1447362, \"job\": \"Visual Effects Art Director\", \"name\": \"Eric Oliver\"}, {\"credit_id\": \"554427ca925141586500312a\", \"department\": \"Visual Effects\", \"gender\": 0, \"id\": 1447503, \"job\": \"Modeling\", \"name\": \"Matsune Suzuki\"}, {\"credit_id\": \"551906889251415aab001c88\", \"department\": \"Art\", \"gender\": 0, \"id\": 1447524, \"job\": \"Art Department Manager\", \"name\": \"Paul Tobin\"}, {\"credit_id\": \"5592af8492514152cc0010de\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1452643, \"job\": \"Hairstylist\", \"name\": \"Roxane Griffin\"}, {\"credit_id\": \"553d3c109251415852001318\", \"department\": \"Lighting\", \"gender\": 0, \"id\": 1453938, \"job\": \"Lighting Artist\", \"name\": \"Arun Ram-Mohan\"}, {\"credit_id\": \"5592af4692514152d5001355\", \"department\": \"Costume & Make-Up\", \"gender\": 0, \"id\": 1457305, \"job\": \"Makeup Artist\", \"name\": \"Georgia Lockhart-Adams\"}, {\"credit_id\": \"5592b2eac3a36877470012a5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1466035, \"job\": \"CG Supervisor\", \"name\": \"Thrain Shadbolt\"}, {\"credit_id\": \"5592b032c3a36877450015f1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483220, \"job\": \"CG Supervisor\", \"name\": \"Brad Alexander\"}, {\"credit_id\": \"5592b05592514152d80012f6\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483221, \"job\": \"CG Supervisor\", \"name\": \"Shadi Almassizadeh\"}, {\"credit_id\": \"5592b090c3a36877570010b5\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483222, \"job\": \"CG Supervisor\", \"name\": \"Simon Clutterbuck\"}, {\"credit_id\": \"5592b0dbc3a368774b00112c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483223, \"job\": \"CG Supervisor\", \"name\": \"Graeme Demmocks\"}, {\"credit_id\": \"5592b0fe92514152db0010c1\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483224, \"job\": \"CG Supervisor\", \"name\": \"Adrian Fernandes\"}, {\"credit_id\": \"5592b11f9251415df8001059\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483225, \"job\": \"CG Supervisor\", \"name\": \"Mitch Gates\"}, {\"credit_id\": \"5592b15dc3a3687745001645\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483226, \"job\": \"CG Supervisor\", \"name\": \"Jerry Kung\"}, {\"credit_id\": \"5592b18e925141645a0004ae\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483227, \"job\": \"CG Supervisor\", \"name\": \"Andy Lomas\"}, {\"credit_id\": \"5592b1bfc3a368775d0010e7\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483228, \"job\": \"CG Supervisor\", \"name\": \"Sebastian Marino\"}, {\"credit_id\": \"5592b2049251415df8001078\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483229, \"job\": \"CG Supervisor\", \"name\": \"Matthias Menz\"}, {\"credit_id\": \"5592b27b92514152d800136a\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483230, \"job\": \"CG Supervisor\", \"name\": \"Sergei Nevshupov\"}, {\"credit_id\": \"5592b2c3c3a36869e800003c\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483231, \"job\": \"CG Supervisor\", \"name\": \"Philippe Rebours\"}, {\"credit_id\": \"5592b317c3a36877470012af\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483232, \"job\": \"CG Supervisor\", \"name\": \"Michael Takarangi\"}, {\"credit_id\": \"5592b345c3a36877470012bb\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483233, \"job\": \"CG Supervisor\", \"name\": \"David Weitzberg\"}, {\"credit_id\": \"5592b37cc3a368775100113b\", \"department\": \"Crew\", \"gender\": 0, \"id\": 1483234, \"job\": \"CG Supervisor\", \"name\": \"Ben White\"}, {\"credit_id\": \"573c8e2f9251413f5d000094\", \"department\": \"Crew\", \"gender\": 1, \"id\": 1621932, \"job\": \"Stunts\", \"name\": \"Min Windle\"}]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "I have to only extract the name of director."
      ],
      "metadata": {
        "id": "N4ykF6Hg1oZn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def extract_director(obj):\n",
        "  l = []\n",
        "  for i in ast.literal_eval(obj):\n",
        "    if i['job'] == 'Director':\n",
        "      l.append(i['name'])\n",
        "      break\n",
        "  return l"
      ],
      "metadata": {
        "id": "xgZp4nFR1rth"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies['crew'] = movies['crew'].apply(extract_director)"
      ],
      "metadata": {
        "id": "72Vwa4T_11Tk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.overview = movies.overview.apply(lambda x:x.split())"
      ],
      "metadata": {
        "id": "xBzVD55W2jyB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "ezwM4vzd7CWP",
        "outputId": "09c7ad01-78b3-4afd-cd5f-4f2777eb276f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
              "1       285  Pirates of the Caribbean: At World's End   \n",
              "2    206647                                   Spectre   \n",
              "3     49026                     The Dark Knight Rises   \n",
              "4     49529                               John Carter   \n",
              "\n",
              "                                          genres  \\\n",
              "0  [Action, Adventure, Fantasy, Science Fiction]   \n",
              "1                   [Adventure, Fantasy, Action]   \n",
              "2                     [Action, Adventure, Crime]   \n",
              "3               [Action, Crime, Drama, Thriller]   \n",
              "4           [Action, Adventure, Science Fiction]   \n",
              "\n",
              "                                            keywords  \\\n",
              "0  [culture clash, future, space war, space colon...   \n",
              "1  [ocean, drug abuse, exotic island, east india ...   \n",
              "2  [spy, based on novel, secret agent, sequel, mi...   \n",
              "3  [dc comics, crime fighter, terrorist, secret i...   \n",
              "4  [based on novel, mars, medallion, space travel...   \n",
              "\n",
              "                                            overview  \\\n",
              "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
              "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
              "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
              "3  [Following, the, death, of, District, Attorney...   \n",
              "4  [John, Carter, is, a, war-weary,, former, mili...   \n",
              "\n",
              "                                               cast                 crew  \n",
              "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]      [James Cameron]  \n",
              "1     [Johnny Depp, Orlando Bloom, Keira Knightley]     [Gore Verbinski]  \n",
              "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]         [Sam Mendes]  \n",
              "3      [Christian Bale, Michael Caine, Gary Oldman]  [Christopher Nolan]  \n",
              "4    [Taylor Kitsch, Lynn Collins, Samantha Morton]     [Andrew Stanton]  "
            ],
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_id</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>keywords</th>\n",
              "      <th>overview</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19995</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>[Action, Adventure, Fantasy, Science Fiction]</td>\n",
              "      <td>[culture clash, future, space war, space colon...</td>\n",
              "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
              "      <td>[Sam Worthington, Zoe Saldana, Sigourney Weaver]</td>\n",
              "      <td>[James Cameron]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>285</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>[Adventure, Fantasy, Action]</td>\n",
              "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
              "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
              "      <td>[Johnny Depp, Orlando Bloom, Keira Knightley]</td>\n",
              "      <td>[Gore Verbinski]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>206647</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>[Action, Adventure, Crime]</td>\n",
              "      <td>[spy, based on novel, secret agent, sequel, mi...</td>\n",
              "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
              "      <td>[Daniel Craig, Christoph Waltz, Léa Seydoux]</td>\n",
              "      <td>[Sam Mendes]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49026</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>[Action, Crime, Drama, Thriller]</td>\n",
              "      <td>[dc comics, crime fighter, terrorist, secret i...</td>\n",
              "      <td>[Following, the, death, of, District, Attorney...</td>\n",
              "      <td>[Christian Bale, Michael Caine, Gary Oldman]</td>\n",
              "      <td>[Christopher Nolan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49529</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>[Action, Adventure, Science Fiction]</td>\n",
              "      <td>[based on novel, mars, medallion, space travel...</td>\n",
              "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
              "      <td>[Taylor Kitsch, Lynn Collins, Samantha Morton]</td>\n",
              "      <td>[Andrew Stanton]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dd05bebf-7cc7-4586-b3c8-be54d3c1a27c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-dd05bebf-7cc7-4586-b3c8-be54d3c1a27c 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-dd05bebf-7cc7-4586-b3c8-be54d3c1a27c');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
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              "\n",
              "\n",
              "<div id=\"df-9c394708-fe53-46eb-9d1d-be7f588b3f20\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-9c394708-fe53-46eb-9d1d-be7f588b3f20')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-9c394708-fe53-46eb-9d1d-be7f588b3f20 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Removing White space**\n",
        "One more thing, I have to do here is to remove white spaces between the elements of any list in our dataset.\n",
        "\n",
        "The reason why I am doing this is that to remove confusion to our model. Let's take the very first actor name which is `sam worthington` and the name of director of movie spectre is is `sam mendes`. If someone puts sam worthington in our recommender system then our system will search firstly for sam and also recommend a movie which is not of our consumers use. So to cure it, we will remove white spaces."
      ],
      "metadata": {
        "id": "Vv3jY0KP-cD9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def remove_space(columns):\n",
        "  for column in columns:\n",
        "    movies[column] = movies[column].apply(lambda x: [i.replace(\" \",'') for i in x])"
      ],
      "metadata": {
        "id": "dLFiBmET7Oda"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "columns = ['genres','keywords','cast','crew']\n",
        "remove_space(columns)"
      ],
      "metadata": {
        "id": "1NuLW8xSBDJR",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 303
        },
        "outputId": "7f8bd677-2884-4dab-fda2-d5512a800dd3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-44-f8c1edad4462>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mcolumns\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m'genres'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'keywords'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'cast'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'crew'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mremove_space\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-43-7791dbc099f4>\u001b[0m in \u001b[0;36mremove_space\u001b[0;34m(columns)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mremove_space\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mmovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[1;32m   4769\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4770\u001b[0m         \"\"\"\n\u001b[0;32m-> 4771\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSeriesApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4773\u001b[0m     def _reduce(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;31m# self.f is Callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1123\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1125\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1172\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m                 mapped = lib.map_infer(\n\u001b[0m\u001b[1;32m   1175\u001b[0m                     \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m                     \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-43-7791dbc099f4>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mremove_space\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m   \u001b[0;32mfor\u001b[0m \u001b[0mcolumn\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m     \u001b[0mmovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmovies\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcolumn\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\" \"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m''\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: 'NoneType' object is not iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "uS1wuJ4MtTw0",
        "outputId": "3e02bbcb-50d7-460b-cca8-04cef2c28970"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
              "1       285  Pirates of the Caribbean: At World's End   \n",
              "2    206647                                   Spectre   \n",
              "3     49026                     The Dark Knight Rises   \n",
              "4     49529                               John Carter   \n",
              "\n",
              "                                         genres  \\\n",
              "0  [Action, Adventure, Fantasy, ScienceFiction]   \n",
              "1                  [Adventure, Fantasy, Action]   \n",
              "2                    [Action, Adventure, Crime]   \n",
              "3              [Action, Crime, Drama, Thriller]   \n",
              "4           [Action, Adventure, ScienceFiction]   \n",
              "\n",
              "                                            keywords  \\\n",
              "0  [cultureclash, future, spacewar, spacecolony, ...   \n",
              "1  [ocean, drugabuse, exoticisland, eastindiatrad...   \n",
              "2  [spy, basedonnovel, secretagent, sequel, mi6, ...   \n",
              "3  [dccomics, crimefighter, terrorist, secretiden...   \n",
              "4  [basedonnovel, mars, medallion, spacetravel, p...   \n",
              "\n",
              "                                            overview  \\\n",
              "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
              "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
              "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
              "3  [Following, the, death, of, District, Attorney...   \n",
              "4  [John, Carter, is, a, war-weary,, former, mili...   \n",
              "\n",
              "                                               cast                 crew  \n",
              "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]      [James Cameron]  \n",
              "1     [Johnny Depp, Orlando Bloom, Keira Knightley]     [Gore Verbinski]  \n",
              "2      [Daniel Craig, Christoph Waltz, Léa Seydoux]         [Sam Mendes]  \n",
              "3      [Christian Bale, Michael Caine, Gary Oldman]  [Christopher Nolan]  \n",
              "4    [Taylor Kitsch, Lynn Collins, Samantha Morton]     [Andrew Stanton]  "
            ],
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              "        document.querySelector('#df-7e614abf-125a-4430-bb4e-665c6e0ef07e 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-7e614abf-125a-4430-bb4e-665c6e0ef07e');\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",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-34ec02c9-b960-491d-a8be-457e44e8ff7c\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-34ec02c9-b960-491d-a8be-457e44e8ff7c')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-34ec02c9-b960-491d-a8be-457e44e8ff7c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "17N23FQXBN0Q",
        "outputId": "875f0c88-e982-4bce-f715-091486b3e280"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
              "1       285  Pirates of the Caribbean: At World's End   \n",
              "2    206647                                   Spectre   \n",
              "3     49026                     The Dark Knight Rises   \n",
              "4     49529                               John Carter   \n",
              "\n",
              "                                         genres  \\\n",
              "0  [Action, Adventure, Fantasy, ScienceFiction]   \n",
              "1                  [Adventure, Fantasy, Action]   \n",
              "2                    [Action, Adventure, Crime]   \n",
              "3              [Action, Crime, Drama, Thriller]   \n",
              "4           [Action, Adventure, ScienceFiction]   \n",
              "\n",
              "                                            keywords  \\\n",
              "0  [cultureclash, future, spacewar, spacecolony, ...   \n",
              "1  [ocean, drugabuse, exoticisland, eastindiatrad...   \n",
              "2  [spy, basedonnovel, secretagent, sequel, mi6, ...   \n",
              "3  [dccomics, crimefighter, terrorist, secretiden...   \n",
              "4  [basedonnovel, mars, medallion, spacetravel, p...   \n",
              "\n",
              "                                            overview  \\\n",
              "0  [In, the, 22nd, century,, a, paraplegic, Marin...   \n",
              "1  [Captain, Barbossa,, long, believed, to, be, d...   \n",
              "2  [A, cryptic, message, from, Bond’s, past, send...   \n",
              "3  [Following, the, death, of, District, Attorney...   \n",
              "4  [John, Carter, is, a, war-weary,, former, mili...   \n",
              "\n",
              "                                            cast                crew  \n",
              "0  [SamWorthington, ZoeSaldana, SigourneyWeaver]      [JamesCameron]  \n",
              "1     [JohnnyDepp, OrlandoBloom, KeiraKnightley]     [GoreVerbinski]  \n",
              "2      [DanielCraig, ChristophWaltz, LéaSeydoux]         [SamMendes]  \n",
              "3      [ChristianBale, MichaelCaine, GaryOldman]  [ChristopherNolan]  \n",
              "4    [TaylorKitsch, LynnCollins, SamanthaMorton]     [AndrewStanton]  "
            ],
            "text/html": [
              "\n",
              "\n",
              "  <div id=\"df-1bb4d7bf-ec07-410f-b551-025b4a0350c7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_id</th>\n",
              "      <th>title</th>\n",
              "      <th>genres</th>\n",
              "      <th>keywords</th>\n",
              "      <th>overview</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19995</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>[Action, Adventure, Fantasy, ScienceFiction]</td>\n",
              "      <td>[cultureclash, future, spacewar, spacecolony, ...</td>\n",
              "      <td>[In, the, 22nd, century,, a, paraplegic, Marin...</td>\n",
              "      <td>[SamWorthington, ZoeSaldana, SigourneyWeaver]</td>\n",
              "      <td>[JamesCameron]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>285</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
              "      <td>[Adventure, Fantasy, Action]</td>\n",
              "      <td>[ocean, drugabuse, exoticisland, eastindiatrad...</td>\n",
              "      <td>[Captain, Barbossa,, long, believed, to, be, d...</td>\n",
              "      <td>[JohnnyDepp, OrlandoBloom, KeiraKnightley]</td>\n",
              "      <td>[GoreVerbinski]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>206647</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>[Action, Adventure, Crime]</td>\n",
              "      <td>[spy, basedonnovel, secretagent, sequel, mi6, ...</td>\n",
              "      <td>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
              "      <td>[DanielCraig, ChristophWaltz, LéaSeydoux]</td>\n",
              "      <td>[SamMendes]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49026</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>[Action, Crime, Drama, Thriller]</td>\n",
              "      <td>[dccomics, crimefighter, terrorist, secretiden...</td>\n",
              "      <td>[Following, the, death, of, District, Attorney...</td>\n",
              "      <td>[ChristianBale, MichaelCaine, GaryOldman]</td>\n",
              "      <td>[ChristopherNolan]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49529</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>[Action, Adventure, ScienceFiction]</td>\n",
              "      <td>[basedonnovel, mars, medallion, spacetravel, p...</td>\n",
              "      <td>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
              "      <td>[TaylorKitsch, LynnCollins, SamanthaMorton]</td>\n",
              "      <td>[AndrewStanton]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1bb4d7bf-ec07-410f-b551-025b4a0350c7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "\n",
              "\n",
              "\n",
              "    <div id=\"df-bdd743bb-e52a-4fcf-a315-e657b958dc3b\">\n",
              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-bdd743bb-e52a-4fcf-a315-e657b958dc3b')\"\n",
              "              title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    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",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-bdd743bb-e52a-4fcf-a315-e657b958dc3b button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1bb4d7bf-ec07-410f-b551-025b4a0350c7 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-1bb4d7bf-ec07-410f-b551-025b4a0350c7');\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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Creation of Tag column**\n",
        "Last thing to do for processing is to create a tag column which is combination genres, keywords, overview, cast, crew. The tag column will be in small cap and in form of string."
      ],
      "metadata": {
        "id": "aKf7RYX_CIjw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies['tags'] = movies['overview'] + movies['genres'] + movies['keywords'] + movies['cast'] + movies['crew']"
      ],
      "metadata": {
        "id": "QLWJvrE4B35D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df = movies[['movie_id','title','tags']]"
      ],
      "metadata": {
        "id": "jEpdJkDmCwmu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['tags'] = new_df['tags'].apply(lambda x: \" \".join(x))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "kz2q28BvEwmd",
        "outputId": "4399e287-e820-417b-b747-feacc7d04e1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-47-e498c0e361a2>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[1;32m   4769\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4770\u001b[0m         \"\"\"\n\u001b[0;32m-> 4771\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSeriesApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4773\u001b[0m     def _reduce(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;31m# self.f is Callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1123\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1125\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1172\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m                 mapped = lib.map_infer(\n\u001b[0m\u001b[1;32m   1175\u001b[0m                     \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m                     \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-47-e498c0e361a2>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m\" \"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m: can only join an iterable"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['tags'] = new_df['tags'].apply(lambda x: x.lower())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        },
        "id": "NbMXRC2XGMsG",
        "outputId": "60ccb00b-5206-488a-bfeb-989c28b34905"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-48-db70cfe1859e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/series.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self, func, convert_dtype, args, **kwargs)\u001b[0m\n\u001b[1;32m   4769\u001b[0m         \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mfloat64\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4770\u001b[0m         \"\"\"\n\u001b[0;32m-> 4771\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mSeriesApply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_dtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   4772\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   4773\u001b[0m     def _reduce(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1121\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1122\u001b[0m         \u001b[0;31m# self.f is Callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1123\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply_standard\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1124\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1125\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/apply.py\u001b[0m in \u001b[0;36mapply_standard\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1172\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1173\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1174\u001b[0;31m                 mapped = lib.map_infer(\n\u001b[0m\u001b[1;32m   1175\u001b[0m                     \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1176\u001b[0m                     \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m<ipython-input-48-db70cfe1859e>\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x)\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnew_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'tags'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlower\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m: 'list' object has no attribute 'lower'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "8ImBdBIhHKh6",
        "outputId": "a1b96a5e-2e47-40ea-95bd-5423fdc5cab4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
              "1       285  Pirates of the Caribbean: At World's End   \n",
              "2    206647                                   Spectre   \n",
              "3     49026                     The Dark Knight Rises   \n",
              "4     49529                               John Carter   \n",
              "\n",
              "                                                tags  \n",
              "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...  "
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              "\n",
              "  <div id=\"df-7d8e6e68-7b59-4aba-a9af-5f00002e34c7\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>movie_id</th>\n",
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              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    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",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f96594fb-1b50-410b-b98c-f81f2ccc2191 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7d8e6e68-7b59-4aba-a9af-5f00002e34c7 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-7d8e6e68-7b59-4aba-a9af-5f00002e34c7');\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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here, we need to do stemming to avoid repetition of words in different forms in most common words. It will improve the accuracy very much."
      ],
      "metadata": {
        "id": "ktDSuXwvTAfp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import nltk"
      ],
      "metadata": {
        "id": "WPjJerviRDCm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from nltk.stem.porter import PorterStemmer\n",
        "ps = PorterStemmer()"
      ],
      "metadata": {
        "id": "rn21lHUkREha"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def stem(text):\n",
        "  y = []\n",
        "\n",
        "  for i in text.split():\n",
        "    y.append(ps.stem(i))\n",
        "  return ' '.join(y)"
      ],
      "metadata": {
        "id": "ArxaoBL_RR25"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "new_df['tags'] = new_df.tags.apply(stem)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E2-vMpakRR0z",
        "outputId": "ca44bc2f-3cd2-46a0-be97-442fe8583cf3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-49-33874492b6a7>:1: 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",
            "  new_df['tags'] = new_df.tags.apply(stem)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "new_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YQF8oDGhS-lV",
        "outputId": "66ce68e9-bab9-4054-e738-9234e0ec7fe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      movie_id                                     title  \\\n",
              "0        19995                                    Avatar   \n",
              "1          285  Pirates of the Caribbean: At World's End   \n",
              "2       206647                                   Spectre   \n",
              "3        49026                     The Dark Knight Rises   \n",
              "4        49529                               John Carter   \n",
              "...        ...                                       ...   \n",
              "4804      9367                               El Mariachi   \n",
              "4805     72766                                 Newlyweds   \n",
              "4806    231617                 Signed, Sealed, Delivered   \n",
              "4807    126186                          Shanghai Calling   \n",
              "4808     25975                         My Date with Drew   \n",
              "\n",
              "                                                   tags  \n",
              "0     in the 22nd century, a parapleg marin is dispa...  \n",
              "1     captain barbossa, long believ to be dead, ha c...  \n",
              "2     a cryptic messag from bond’ past send him on a...  \n",
              "3     follow the death of district attorney harvey d...  \n",
              "4     john carter is a war-weary, former militari ca...  \n",
              "...                                                 ...  \n",
              "4804  el mariachi just want to play hi guitar and ca...  \n",
              "4805  a newlyw couple' honeymoon is upend by the arr...  \n",
              "4806  \"signed, sealed, delivered\" introduc a dedic q...  \n",
              "4807  when ambiti new york attorney sam is sent to s...  \n",
              "4808  ever sinc the second grade when he first saw h...  \n",
              "\n",
              "[4806 rows x 3 columns]"
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "      <td>49529</td>\n",
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              "      <td>Signed, Sealed, Delivered</td>\n",
              "      <td>\"signed, sealed, delivered\" introduc a dedic q...</td>\n",
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              "      <th>4807</th>\n",
              "      <td>126186</td>\n",
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              "<p>4806 rows × 3 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eeb79f65-36ed-4b3f-b8ef-559f28626bcc')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "              title=\"Suggest charts.\"\n",
              "              style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "      </button>\n",
              "    </div>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "    background-color: #E8F0FE;\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: #1967D2;\n",
              "    height: 32px;\n",
              "    padding: 0 0 0 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: #E2EBFA;\n",
              "    box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: #174EA6;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "    background-color: #3B4455;\n",
              "    fill: #D2E3FC;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart:hover {\n",
              "    background-color: #434B5C;\n",
              "    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",
              "    fill: #FFFFFF;\n",
              "  }\n",
              "</style>\n",
              "\n",
              "    <script>\n",
              "      async function quickchart(key) {\n",
              "        const containerElement = document.querySelector('#' + key);\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      }\n",
              "    </script>\n",
              "\n",
              "      <script>\n",
              "\n",
              "function displayQuickchartButton(domScope) {\n",
              "  let quickchartButtonEl =\n",
              "    domScope.querySelector('#df-f1c97c90-926f-4769-a03b-b65ee01b6d61 button.colab-df-quickchart');\n",
              "  quickchartButtonEl.style.display =\n",
              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "}\n",
              "\n",
              "        displayQuickchartButton(document);\n",
              "      </script>\n",
              "      <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-eeb79f65-36ed-4b3f-b8ef-559f28626bcc 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-eeb79f65-36ed-4b3f-b8ef-559f28626bcc');\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",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Modelling**\n",
        "How our model will work -\n",
        "\n",
        "1. Create a bag of words using all available text.\n",
        "2. Extract the most common words to be used as the vectorizer.\n",
        "3. Pass the tags column to the vectorizer.\n",
        "4. Convert the defined number of text (5000) in the vectorizer into column headings, with the movie names as rows.\n",
        "5. Analyze the movies and assign a value of 1 to each column heading that is present in the movie tag text.\n",
        "6. Plot a graph based on the analysis.\n",
        "7. If someone searches for a word, suggest the 5 or 10 closest points on the graph to the searched word."
      ],
      "metadata": {
        "id": "4LdsXmjOLJta"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer"
      ],
      "metadata": {
        "id": "sGCgW51sHN08"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cv = CountVectorizer(max_features = 5000, stop_words = 'english')"
      ],
      "metadata": {
        "id": "nfUs-ALGKAbX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectors = cv.fit_transform(new_df['tags']).toarray()"
      ],
      "metadata": {
        "id": "q7vMafFOKFQs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "vectors"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LpxsV4mfK7NF",
        "outputId": "a4b8a20e-d466-4da3-8605-5af618d9dffa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       ...,\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0],\n",
              "       [0, 0, 0, ..., 0, 0, 0]])"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is our points which we will plot on the graph, every row of this matrix represent the matched words in the first movie and the most common words in our bag of words."
      ],
      "metadata": {
        "id": "i_yl5Bd2M9bZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# cv.get_feature_names()"
      ],
      "metadata": {
        "id": "lRewI2DWM8c_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Improvement on 1st run - Now, We will apply stemming to remove or merge similar words. Like in our data words such as actor and actors are treated differently but they are same. So to remove this we do stemming.So, we have to go back for now to remove these words from the root column which was nltk."
      ],
      "metadata": {
        "id": "CY0wa4RoOCtn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "vectors.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jneu9h7qOU8S",
        "outputId": "96412a34-9f58-4d77-ac11-00c040cc04f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4806, 5000)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this shape, 4806 is representing the number of movies available to the machine and 5000 is representing the most common 5000 words from the bag of words\n",
        "."
      ],
      "metadata": {
        "id": "zyKgZGG_T9Rj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Distance Calculation**\n",
        "After vectorization is calculating the distance between the movies on this 5000*5000 dimensional graph. It will help us to suggest the similar movies. Here, we will use cosine distane not the eucleadian distance because the more the dimension, the bad the eucleadean distance for the method."
      ],
      "metadata": {
        "id": "Na4OoSd5UKjL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "yJiyYHZKULXT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "similarity = cosine_similarity(vectors)"
      ],
      "metadata": {
        "id": "KXSnrNs8ULUU"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "This array is representing the distance(dissimilarity) of movies with each other. Its shape is 4806X4806 which is verifying the statement."
      ],
      "metadata": {
        "id": "wtPs3KxLVqKC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "similarity"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d2m6vnv3ULRq",
        "outputId": "1bd1b41f-d4c3-41fc-9040-0bf256099ad9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.08346223, 0.0860309 , ..., 0.04499213, 0.        ,\n",
              "        0.        ],\n",
              "       [0.08346223, 1.        , 0.06063391, ..., 0.02378257, 0.        ,\n",
              "        0.02615329],\n",
              "       [0.0860309 , 0.06063391, 1.        , ..., 0.02451452, 0.        ,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.04499213, 0.02378257, 0.02451452, ..., 1.        , 0.03962144,\n",
              "        0.04229549],\n",
              "       [0.        , 0.        , 0.        , ..., 0.03962144, 1.        ,\n",
              "        0.08714204],\n",
              "       [0.        , 0.02615329, 0.        , ..., 0.04229549, 0.08714204,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The similarity of a movie with itself is obviously one and that is represented by the diagonals."
      ],
      "metadata": {
        "id": "hcF0NAACWzm9"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Recommendation Function**"
      ],
      "metadata": {
        "id": "ef47NM_YXP9R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def recommend(movie):\n",
        "  movie_index = new_df[new_df['title'] == movie].index[0]\n",
        "  similar = sorted(list(enumerate(similarity[movie_index])),reverse = True,key = lambda x: x[1])[1:6]\n",
        "  for i in similar:\n",
        "    print(new_df.iloc[i[0]].title)"
      ],
      "metadata": {
        "id": "BraRz22gULNv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "recommend('Avatar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FsQhKlkfULJr",
        "outputId": "aa3d74a1-2ddf-4dfa-da8f-000647a5e519"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aliens vs Predator: Requiem\n",
            "Aliens\n",
            "Falcon Rising\n",
            "Independence Day\n",
            "Titan A.E.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib\n",
        "import gzip"
      ],
      "metadata": {
        "id": "rCJuTAFLULEV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# with gzip.open('similarity.pkl', 'wb') as f:\n",
        "#     pickle.dump(similarity, f)"
      ],
      "metadata": {
        "id": "HEDpCVaECgd6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(new_df,open('movies.joblib','wb'))"
      ],
      "metadata": {
        "id": "UFRQkDn_ULAY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(similarity,open('similarity.pkl','wb'))"
      ],
      "metadata": {
        "id": "VYc9ZWADUK-F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import bz2"
      ],
      "metadata": {
        "id": "bnKcMCxWDlEK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "with bz2.open('similarity.pkl', 'wb') as f:\n",
        "    pickle.dump(similarity, f)"
      ],
      "metadata": {
        "id": "jCyIkK0BljZ-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import joblib"
      ],
      "metadata": {
        "id": "FWBjOFOCDVHi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(similarity, 'similarity.pkl', compress = 8)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvrFniDaEW9T",
        "outputId": "c6e44b45-5850-4f48-b5cb-7706ac8ab620"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['similarity.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "joblib.dump(similarity, 'similarity5.pkl', compress = 9)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "80wZnK3TE8AC",
        "outputId": "33cadc16-a13f-48de-f52c-ba8387cd6f14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['similarity5.pkl']"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    }
  ]
}