movie_recomender_system.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Movie recommmender System"
      ],
      "metadata": {
        "id": "rjsNEqrMYimc"
      },
      "id": "rjsNEqrMYimc"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset link -  https://www.kaggle.com/tmdb/tmdb-movie-metadata?select=tmdb_5000_movies.csv\n",
        "\n",
        "Steps:\n",
        "\n",
        "\n",
        "1.   downloaded dataset from kaggle about 5000 movies and their credits\n",
        "2.   Performed EDA and FE to clean the data.\n",
        "3. removed unwanted columns and build functions to get required data\n",
        "4. Use vectorization of words to make vector and calculated the the cosine distance between them.\n",
        "5. Based upon cosine distance find most 5 similar movies of a given movie\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "cK_St68oYmkF"
      },
      "id": "cK_St68oYmkF"
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing liberaries"
      ],
      "metadata": {
        "id": "dKjbXH3bbIWm"
      },
      "id": "dKjbXH3bbIWm"
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "id": "58b2211f",
      "metadata": {
        "id": "58b2211f"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SoglmaFOebFL",
        "outputId": "be986fc4-fc85-46f8-bde2-c8f72d876613"
      },
      "id": "SoglmaFOebFL",
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importing data"
      ],
      "metadata": {
        "id": "wqq7ghP9bLWq"
      },
      "id": "wqq7ghP9bLWq"
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "id": "db301105",
      "metadata": {
        "id": "db301105"
      },
      "outputs": [],
      "source": [
        "credits=pd.read_csv(r'/content/drive/MyDrive/data_science_data/tmdb_5000_credits.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "id": "a25928d8",
      "metadata": {
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          "height": 206
        },
        "id": "a25928d8",
        "outputId": "1e92f89d-cee2-49a0-ebe5-511105412cca"
      },
      "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",
              "                                                cast  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
              "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
              "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
              "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
              "\n",
              "                                                crew  \n",
              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
              "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
              "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
              "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
              "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  "
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              "      <td>Pirates of the Caribbean: At World's End</td>\n",
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              "      <td>Spectre</td>\n",
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              "      <td>The Dark Knight Rises</td>\n",
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              "      <td>49529</td>\n",
              "      <td>John Carter</td>\n",
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          "metadata": {},
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      ],
      "source": [
        "credits.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "id": "d62336ea",
      "metadata": {
        "id": "d62336ea"
      },
      "outputs": [],
      "source": [
        "movies=pd.read_csv(r'/content/drive/MyDrive/data_science_data/tmdb_5000_movies.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
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              "      budget                                             genres  \\\n",
              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
              "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
              "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "\n",
              "                                       homepage      id  \\\n",
              "0                   http://www.avatarmovie.com/   19995   \n",
              "1  http://disney.go.com/disneypictures/pirates/     285   \n",
              "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
              "3            http://www.thedarkknightrises.com/   49026   \n",
              "4          http://movies.disney.com/john-carter   49529   \n",
              "\n",
              "                                            keywords original_language  \\\n",
              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
              "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
              "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
              "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...                en   \n",
              "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
              "\n",
              "                             original_title  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                            overview  popularity  \\\n",
              "0  In the 22nd century, a paraplegic Marine is di...  150.437577   \n",
              "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
              "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
              "3  Following the death of District Attorney Harve...  112.312950   \n",
              "4  John Carter is a war-weary, former military ca...   43.926995   \n",
              "\n",
              "                                production_companies  \\\n",
              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...   \n",
              "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...   \n",
              "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...   \n",
              "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...   \n",
              "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]   \n",
              "\n",
              "                                production_countries release_date     revenue  \\\n",
              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2009-12-10  2787965087   \n",
              "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2007-05-19   961000000   \n",
              "2  [{\"iso_3166_1\": \"GB\", \"name\": \"United Kingdom\"...   2015-10-26   880674609   \n",
              "3  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-07-16  1084939099   \n",
              "4  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   2012-03-07   284139100   \n",
              "\n",
              "   runtime                                   spoken_languages    status  \\\n",
              "0    162.0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
              "1    169.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
              "2    148.0  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
              "3    165.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
              "4    132.0           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
              "\n",
              "                                          tagline  \\\n",
              "0                     Enter the World of Pandora.   \n",
              "1  At the end of the world, the adventure begins.   \n",
              "2                           A Plan No One Escapes   \n",
              "3                                 The Legend Ends   \n",
              "4            Lost in our world, found in another.   \n",
              "\n",
              "                                      title  vote_average  vote_count  \n",
              "0                                    Avatar           7.2       11800  \n",
              "1  Pirates of the Caribbean: At World's End           6.9        4500  \n",
              "2                                   Spectre           6.3        4466  \n",
              "3                     The Dark Knight Rises           7.6        9106  \n",
              "4                               John Carter           6.1        2124  "
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              "      <td>At the end of the world, the adventure begins.</td>\n",
              "      <td>Pirates of the Caribbean: At World's End</td>\n",
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              "      <td>6.3</td>\n",
              "      <td>4466</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>49026</td>\n",
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              "      <td>The Legend Ends</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>7.6</td>\n",
              "      <td>9106</td>\n",
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              "      <th>4</th>\n",
              "      <td>260000000</td>\n",
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              "      <td>49529</td>\n",
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              "      <td>[{\"iso_3166_1\": \"US\", \"name\": \"United States o...</td>\n",
              "      <td>2012-03-07</td>\n",
              "      <td>284139100</td>\n",
              "      <td>132.0</td>\n",
              "      <td>[{\"iso_639_1\": \"en\", \"name\": \"English\"}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Lost in our world, found in another.</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>6.1</td>\n",
              "      <td>2124</td>\n",
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          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "movies.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Merging both the dataset"
      ],
      "metadata": {
        "id": "YeNLRGuPbO0w"
      },
      "id": "YeNLRGuPbO0w"
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "id": "0250065d",
      "metadata": {
        "id": "0250065d"
      },
      "outputs": [],
      "source": [
        "movies = movies.merge(credits,on='title')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "id": "c5836435",
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        "id": "c5836435",
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          "data": {
            "text/plain": [
              "      budget                                             genres  \\\n",
              "0  237000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "1  300000000  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
              "2  245000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "3  250000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
              "4  260000000  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "\n",
              "                                       homepage      id  \\\n",
              "0                   http://www.avatarmovie.com/   19995   \n",
              "1  http://disney.go.com/disneypictures/pirates/     285   \n",
              "2   http://www.sonypictures.com/movies/spectre/  206647   \n",
              "3            http://www.thedarkknightrises.com/   49026   \n",
              "4          http://movies.disney.com/john-carter   49529   \n",
              "\n",
              "                                            keywords original_language  \\\n",
              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...                en   \n",
              "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...                en   \n",
              "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...                en   \n",
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              "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...                en   \n",
              "\n",
              "                             original_title  \\\n",
              "0                                    Avatar   \n",
              "1  Pirates of the Caribbean: At World's End   \n",
              "2                                   Spectre   \n",
              "3                     The Dark Knight Rises   \n",
              "4                               John Carter   \n",
              "\n",
              "                                            overview  popularity  \\\n",
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              "1  Captain Barbossa, long believed to be dead, ha...  139.082615   \n",
              "2  A cryptic message from Bond’s past sends him o...  107.376788   \n",
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              "\n",
              "                                production_companies  ... runtime  \\\n",
              "0  [{\"name\": \"Ingenious Film Partners\", \"id\": 289...  ...   162.0   \n",
              "1  [{\"name\": \"Walt Disney Pictures\", \"id\": 2}, {\"...  ...   169.0   \n",
              "2  [{\"name\": \"Columbia Pictures\", \"id\": 5}, {\"nam...  ...   148.0   \n",
              "3  [{\"name\": \"Legendary Pictures\", \"id\": 923}, {\"...  ...   165.0   \n",
              "4        [{\"name\": \"Walt Disney Pictures\", \"id\": 2}]  ...   132.0   \n",
              "\n",
              "                                    spoken_languages    status  \\\n",
              "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}, {\"iso...  Released   \n",
              "1           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
              "2  [{\"iso_639_1\": \"fr\", \"name\": \"Fran\\u00e7ais\"},...  Released   \n",
              "3           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
              "4           [{\"iso_639_1\": \"en\", \"name\": \"English\"}]  Released   \n",
              "\n",
              "                                          tagline  \\\n",
              "0                     Enter the World of Pandora.   \n",
              "1  At the end of the world, the adventure begins.   \n",
              "2                           A Plan No One Escapes   \n",
              "3                                 The Legend Ends   \n",
              "4            Lost in our world, found in another.   \n",
              "\n",
              "                                      title vote_average vote_count movie_id  \\\n",
              "0                                    Avatar          7.2      11800    19995   \n",
              "1  Pirates of the Caribbean: At World's End          6.9       4500      285   \n",
              "2                                   Spectre          6.3       4466   206647   \n",
              "3                     The Dark Knight Rises          7.6       9106    49026   \n",
              "4                               John Carter          6.1       2124    49529   \n",
              "\n",
              "                                                cast  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
              "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
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              "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
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              "                                                crew  \n",
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              "      <td>...</td>\n",
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              "      <td>9106</td>\n",
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              "      <td>...</td>\n",
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              "      background-color: #E8F0FE;\n",
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              "      height: 32px;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "          document.querySelector('#df-edd06048-02bd-4631-b204-c4eb36fbf9bf 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-edd06048-02bd-4631-b204-c4eb36fbf9bf');\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": 47
        }
      ],
      "source": [
        "movies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "id": "b1f2b73e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b1f2b73e",
        "outputId": "83dce20e-5411-4e45-8efc-b4ebc0f3231b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4809, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ],
      "source": [
        "#getting the shape of the dataset\n",
        "\n",
        "movies.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "id": "a2110c85",
      "metadata": {
        "id": "a2110c85"
      },
      "outputs": [],
      "source": [
        "#removing the unnecessary columns\n",
        "\n",
        "movies =movies[['movie_id','title','overview','genres','keywords','cast','crew']]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "id": "27ecefab",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "27ecefab",
        "outputId": "dc9249be-e8df-44bf-fa5e-6a622e92c9af"
      },
      "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",
              "                                            overview  \\\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...   \n",
              "\n",
              "                                              genres  \\\n",
              "0  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "1  [{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...   \n",
              "2  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "3  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...   \n",
              "4  [{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...   \n",
              "\n",
              "                                            keywords  \\\n",
              "0  [{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...   \n",
              "1  [{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...   \n",
              "2  [{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...   \n",
              "3  [{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...   \n",
              "4  [{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...   \n",
              "\n",
              "                                                cast  \\\n",
              "0  [{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...   \n",
              "1  [{\"cast_id\": 4, \"character\": \"Captain Jack Spa...   \n",
              "2  [{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...   \n",
              "3  [{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...   \n",
              "4  [{\"cast_id\": 5, \"character\": \"John Carter\", \"c...   \n",
              "\n",
              "                                                crew  \n",
              "0  [{\"credit_id\": \"52fe48009251416c750aca23\", \"de...  \n",
              "1  [{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...  \n",
              "2  [{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...  \n",
              "3  [{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...  \n",
              "4  [{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-537e2eba-e32f-4079-80b6-98b3001607e2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_id</th>\n",
              "      <th>title</th>\n",
              "      <th>overview</th>\n",
              "      <th>genres</th>\n",
              "      <th>keywords</th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19995</td>\n",
              "      <td>Avatar</td>\n",
              "      <td>In the 22nd century, a paraplegic Marine is di...</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\":...</td>\n",
              "      <td>[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"...</td>\n",
              "      <td>[{\"credit_id\": \"52fe48009251416c750aca23\", \"de...</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>Captain Barbossa, long believed to be dead, ha...</td>\n",
              "      <td>[{\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"...</td>\n",
              "      <td>[{\"id\": 270, \"name\": \"ocean\"}, {\"id\": 726, \"na...</td>\n",
              "      <td>[{\"cast_id\": 4, \"character\": \"Captain Jack Spa...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4232c3a36847f800b579\", \"de...</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>206647</td>\n",
              "      <td>Spectre</td>\n",
              "      <td>A cryptic message from Bond’s past sends him o...</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>[{\"id\": 470, \"name\": \"spy\"}, {\"id\": 818, \"name...</td>\n",
              "      <td>[{\"cast_id\": 1, \"character\": \"James Bond\", \"cr...</td>\n",
              "      <td>[{\"credit_id\": \"54805967c3a36829b5002c41\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>49026</td>\n",
              "      <td>The Dark Knight Rises</td>\n",
              "      <td>Following the death of District Attorney Harve...</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 80, \"nam...</td>\n",
              "      <td>[{\"id\": 849, \"name\": \"dc comics\"}, {\"id\": 853,...</td>\n",
              "      <td>[{\"cast_id\": 2, \"character\": \"Bruce Wayne / Ba...</td>\n",
              "      <td>[{\"credit_id\": \"52fe4781c3a36847f81398c3\", \"de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>49529</td>\n",
              "      <td>John Carter</td>\n",
              "      <td>John Carter is a war-weary, former military ca...</td>\n",
              "      <td>[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"nam...</td>\n",
              "      <td>[{\"id\": 818, \"name\": \"based on novel\"}, {\"id\":...</td>\n",
              "      <td>[{\"cast_id\": 5, \"character\": \"John Carter\", \"c...</td>\n",
              "      <td>[{\"credit_id\": \"52fe479ac3a36847f813eaa3\", \"de...</td>\n",
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              "  </tbody>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-537e2eba-e32f-4079-80b6-98b3001607e2')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\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-537e2eba-e32f-4079-80b6-98b3001607e2 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-537e2eba-e32f-4079-80b6-98b3001607e2');\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",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ],
      "source": [
        "movies.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "id": "11dff35b",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "11dff35b",
        "outputId": "3934222b-83f9-4e70-dbc4-b9afaa5bb4e9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4809, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "#shape of the dataset after removing the unwanted data\n",
        "movies.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "checking for the null values"
      ],
      "metadata": {
        "id": "AdIrNXL9bftL"
      },
      "id": "AdIrNXL9bftL"
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "id": "06b89b96",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "06b89b96",
        "outputId": "16dc26b5-a322-4852-b06f-286d6f994a9a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movie_id    0\n",
              "title       0\n",
              "overview    3\n",
              "genres      0\n",
              "keywords    0\n",
              "cast        0\n",
              "crew        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "#finding null values\n",
        "movies.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dropping the null values"
      ],
      "metadata": {
        "id": "9Hedqf0Qbk4O"
      },
      "id": "9Hedqf0Qbk4O"
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "id": "bc357710",
      "metadata": {
        "id": "bc357710"
      },
      "outputs": [],
      "source": [
        "# as only 3 null values are their we can drop them\n",
        "\n",
        "movies.dropna(inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "id": "c345e2b7",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c345e2b7",
        "outputId": "01dc20d7-eb86-4ba5-9a8d-57ec077b06b4"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "movie_id    0\n",
              "title       0\n",
              "overview    0\n",
              "genres      0\n",
              "keywords    0\n",
              "cast        0\n",
              "crew        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ],
      "source": [
        "movies.isnull().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Checking for the duplicate data"
      ],
      "metadata": {
        "id": "UUr9akuvbu0D"
      },
      "id": "UUr9akuvbu0D"
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "id": "c35436e4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c35436e4",
        "outputId": "4ee1ddde-9c22-4146-e284-34f928be1fbb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ],
      "source": [
        "movies.duplicated().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "id": "cccfb902",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "id": "cccfb902",
        "outputId": "f8e984e8-04c5-403a-c87a-51d81e965194"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[{\"id\": 28, \"name\": \"Action\"}, {\"id\": 12, \"name\": \"Adventure\"}, {\"id\": 14, \"name\": \"Fantasy\"}, {\"id\": 878, \"name\": \"Science Fiction\"}]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 56
        }
      ],
      "source": [
        "movies.iloc[0].genres"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Funtion to fetch name"
      ],
      "metadata": {
        "id": "8T-PUs64b2nb"
      },
      "id": "8T-PUs64b2nb"
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "id": "a81240dd",
      "metadata": {
        "id": "a81240dd"
      },
      "outputs": [],
      "source": [
        "# ast is use to convert the string output to list\n",
        " \n",
        "import ast\n",
        "def extract(obj):\n",
        "    lst=[]\n",
        "    for i in ast.literal_eval(obj):\n",
        "        lst.append(i['name'])\n",
        "    return lst\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "id": "066f3fbb",
      "metadata": {
        "id": "066f3fbb"
      },
      "outputs": [],
      "source": [
        "#applying the extract function\n",
        "\n",
        "movies['genres']=movies['genres'].apply(extract)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "id": "4b7abbe5",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "4b7abbe5",
        "outputId": "d589da60-70c2-49c0-b75e-2d86f82d18c0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[{\"id\": 1463, \"name\": \"culture clash\"}, {\"id\": 2964, \"name\": \"future\"}, {\"id\": 3386, \"name\": \"space war\"}, {\"id\": 3388, \"name\": \"space colony\"}, {\"id\": 3679, \"name\": \"society\"}, {\"id\": 3801, \"name\": \"space travel\"}, {\"id\": 9685, \"name\": \"futuristic\"}, {\"id\": 9840, \"name\": \"romance\"}, {\"id\": 9882, \"name\": \"space\"}, {\"id\": 9951, \"name\": \"alien\"}, {\"id\": 10148, \"name\": \"tribe\"}, {\"id\": 10158, \"name\": \"alien planet\"}, {\"id\": 10987, \"name\": \"cgi\"}, {\"id\": 11399, \"name\": \"marine\"}, {\"id\": 13065, \"name\": \"soldier\"}, {\"id\": 14643, \"name\": \"battle\"}, {\"id\": 14720, \"name\": \"love affair\"}, {\"id\": 165431, \"name\": \"anti war\"}, {\"id\": 193554, \"name\": \"power relations\"}, {\"id\": 206690, \"name\": \"mind and soul\"}, {\"id\": 209714, \"name\": \"3d\"}]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "movies.iloc[0].keywords"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "id": "de7eadd0",
      "metadata": {
        "id": "de7eadd0"
      },
      "outputs": [],
      "source": [
        "#to extract name from the keywords also we can use same function\n",
        "\n",
        "movies['keywords']=movies['keywords'].apply(extract)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "id": "8bb9d49e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "8bb9d49e",
        "outputId": "0f6de4b7-7d6a-426a-d72e-d83e1025a713"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'[{\"cast_id\": 242, \"character\": \"Jake Sully\", \"credit_id\": \"5602a8a7c3a3685532001c9a\", \"gender\": 2, \"id\": 65731, \"name\": \"Sam Worthington\", \"order\": 0}, {\"cast_id\": 3, \"character\": \"Neytiri\", \"credit_id\": \"52fe48009251416c750ac9cb\", \"gender\": 1, \"id\": 8691, \"name\": \"Zoe Saldana\", \"order\": 1}, {\"cast_id\": 25, \"character\": \"Dr. Grace Augustine\", \"credit_id\": \"52fe48009251416c750aca39\", \"gender\": 1, \"id\": 10205, \"name\": \"Sigourney Weaver\", \"order\": 2}, {\"cast_id\": 4, \"character\": \"Col. Quaritch\", \"credit_id\": \"52fe48009251416c750ac9cf\", \"gender\": 2, \"id\": 32747, \"name\": \"Stephen Lang\", \"order\": 3}, {\"cast_id\": 5, \"character\": \"Trudy Chacon\", \"credit_id\": \"52fe48009251416c750ac9d3\", \"gender\": 1, \"id\": 17647, \"name\": \"Michelle Rodriguez\", \"order\": 4}, {\"cast_id\": 8, \"character\": \"Selfridge\", \"credit_id\": \"52fe48009251416c750ac9e1\", \"gender\": 2, \"id\": 1771, \"name\": \"Giovanni Ribisi\", \"order\": 5}, {\"cast_id\": 7, \"character\": \"Norm Spellman\", \"credit_id\": \"52fe48009251416c750ac9dd\", \"gender\": 2, \"id\": 59231, \"name\": \"Joel David Moore\", \"order\": 6}, {\"cast_id\": 9, \"character\": \"Moat\", \"credit_id\": \"52fe48009251416c750ac9e5\", \"gender\": 1, \"id\": 30485, \"name\": \"CCH Pounder\", \"order\": 7}, {\"cast_id\": 11, \"character\": \"Eytukan\", \"credit_id\": \"52fe48009251416c750ac9ed\", \"gender\": 2, \"id\": 15853, \"name\": \"Wes Studi\", \"order\": 8}, {\"cast_id\": 10, \"character\": \"Tsu\\'Tey\", \"credit_id\": \"52fe48009251416c750ac9e9\", \"gender\": 2, \"id\": 10964, \"name\": \"Laz Alonso\", \"order\": 9}, {\"cast_id\": 12, \"character\": \"Dr. Max Patel\", \"credit_id\": \"52fe48009251416c750ac9f1\", \"gender\": 2, \"id\": 95697, \"name\": \"Dileep Rao\", \"order\": 10}, {\"cast_id\": 13, \"character\": \"Lyle Wainfleet\", \"credit_id\": \"52fe48009251416c750ac9f5\", \"gender\": 2, \"id\": 98215, \"name\": \"Matt Gerald\", \"order\": 11}, {\"cast_id\": 32, \"character\": \"Private Fike\", \"credit_id\": \"52fe48009251416c750aca5b\", \"gender\": 2, \"id\": 154153, \"name\": \"Sean Anthony Moran\", \"order\": 12}, {\"cast_id\": 33, \"character\": \"Cryo Vault Med Tech\", \"credit_id\": \"52fe48009251416c750aca5f\", \"gender\": 2, \"id\": 397312, \"name\": \"Jason Whyte\", \"order\": 13}, {\"cast_id\": 34, \"character\": \"Venture Star Crew Chief\", \"credit_id\": \"52fe48009251416c750aca63\", \"gender\": 2, \"id\": 42317, \"name\": \"Scott Lawrence\", \"order\": 14}, {\"cast_id\": 35, \"character\": \"Lock Up Trooper\", \"credit_id\": \"52fe48009251416c750aca67\", \"gender\": 2, \"id\": 986734, \"name\": \"Kelly Kilgour\", \"order\": 15}, {\"cast_id\": 36, \"character\": \"Shuttle Pilot\", \"credit_id\": \"52fe48009251416c750aca6b\", \"gender\": 0, \"id\": 1207227, \"name\": \"James Patrick Pitt\", \"order\": 16}, {\"cast_id\": 37, \"character\": \"Shuttle Co-Pilot\", \"credit_id\": \"52fe48009251416c750aca6f\", \"gender\": 0, \"id\": 1180936, \"name\": \"Sean Patrick Murphy\", \"order\": 17}, {\"cast_id\": 38, \"character\": \"Shuttle Crew Chief\", \"credit_id\": \"52fe48009251416c750aca73\", \"gender\": 2, \"id\": 1019578, \"name\": \"Peter Dillon\", \"order\": 18}, {\"cast_id\": 39, \"character\": \"Tractor Operator / Troupe\", \"credit_id\": \"52fe48009251416c750aca77\", \"gender\": 0, \"id\": 91443, \"name\": \"Kevin Dorman\", \"order\": 19}, {\"cast_id\": 40, \"character\": \"Dragon Gunship Pilot\", \"credit_id\": \"52fe48009251416c750aca7b\", \"gender\": 2, \"id\": 173391, \"name\": \"Kelson Henderson\", \"order\": 20}, {\"cast_id\": 41, \"character\": \"Dragon Gunship Gunner\", \"credit_id\": \"52fe48009251416c750aca7f\", \"gender\": 0, \"id\": 1207236, \"name\": \"David Van Horn\", \"order\": 21}, {\"cast_id\": 42, \"character\": \"Dragon Gunship Navigator\", \"credit_id\": \"52fe48009251416c750aca83\", \"gender\": 0, \"id\": 215913, \"name\": \"Jacob Tomuri\", \"order\": 22}, {\"cast_id\": 43, \"character\": \"Suit #1\", \"credit_id\": \"52fe48009251416c750aca87\", \"gender\": 0, \"id\": 143206, \"name\": \"Michael Blain-Rozgay\", \"order\": 23}, {\"cast_id\": 44, \"character\": \"Suit #2\", \"credit_id\": \"52fe48009251416c750aca8b\", \"gender\": 2, \"id\": 169676, \"name\": \"Jon Curry\", \"order\": 24}, {\"cast_id\": 46, \"character\": \"Ambient Room Tech\", \"credit_id\": \"52fe48009251416c750aca8f\", \"gender\": 0, \"id\": 1048610, \"name\": \"Luke Hawker\", \"order\": 25}, {\"cast_id\": 47, \"character\": \"Ambient Room Tech / Troupe\", \"credit_id\": \"52fe48009251416c750aca93\", \"gender\": 0, \"id\": 42288, \"name\": \"Woody Schultz\", \"order\": 26}, {\"cast_id\": 48, \"character\": \"Horse Clan Leader\", \"credit_id\": \"52fe48009251416c750aca97\", \"gender\": 2, \"id\": 68278, \"name\": \"Peter Mensah\", \"order\": 27}, {\"cast_id\": 49, \"character\": \"Link Room Tech\", \"credit_id\": \"52fe48009251416c750aca9b\", \"gender\": 0, \"id\": 1207247, \"name\": \"Sonia Yee\", \"order\": 28}, {\"cast_id\": 50, \"character\": \"Basketball Avatar / Troupe\", \"credit_id\": \"52fe48009251416c750aca9f\", \"gender\": 1, \"id\": 1207248, \"name\": \"Jahnel Curfman\", \"order\": 29}, {\"cast_id\": 51, \"character\": \"Basketball Avatar\", \"credit_id\": \"52fe48009251416c750acaa3\", \"gender\": 0, \"id\": 89714, \"name\": \"Ilram Choi\", \"order\": 30}, {\"cast_id\": 52, \"character\": \"Na\\'vi Child\", \"credit_id\": \"52fe48009251416c750acaa7\", \"gender\": 0, \"id\": 1207249, \"name\": \"Kyla Warren\", \"order\": 31}, {\"cast_id\": 53, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acaab\", \"gender\": 0, \"id\": 1207250, \"name\": \"Lisa Roumain\", \"order\": 32}, {\"cast_id\": 54, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acaaf\", \"gender\": 1, \"id\": 83105, \"name\": \"Debra Wilson\", \"order\": 33}, {\"cast_id\": 57, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acabb\", \"gender\": 0, \"id\": 1207253, \"name\": \"Chris Mala\", \"order\": 34}, {\"cast_id\": 55, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acab3\", \"gender\": 0, \"id\": 1207251, \"name\": \"Taylor Kibby\", \"order\": 35}, {\"cast_id\": 56, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acab7\", \"gender\": 0, \"id\": 1207252, \"name\": \"Jodie Landau\", \"order\": 36}, {\"cast_id\": 58, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acabf\", \"gender\": 0, \"id\": 1207254, \"name\": \"Julie Lamm\", \"order\": 37}, {\"cast_id\": 59, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acac3\", \"gender\": 0, \"id\": 1207257, \"name\": \"Cullen B. Madden\", \"order\": 38}, {\"cast_id\": 60, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acac7\", \"gender\": 0, \"id\": 1207259, \"name\": \"Joseph Brady Madden\", \"order\": 39}, {\"cast_id\": 61, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acacb\", \"gender\": 0, \"id\": 1207262, \"name\": \"Frankie Torres\", \"order\": 40}, {\"cast_id\": 62, \"character\": \"Troupe\", \"credit_id\": \"52fe48009251416c750acacf\", \"gender\": 1, \"id\": 1158600, \"name\": \"Austin Wilson\", \"order\": 41}, {\"cast_id\": 63, \"character\": \"Troupe\", \"credit_id\": \"52fe48019251416c750acad3\", \"gender\": 1, \"id\": 983705, \"name\": \"Sara Wilson\", \"order\": 42}, {\"cast_id\": 64, \"character\": \"Troupe\", \"credit_id\": \"52fe48019251416c750acad7\", \"gender\": 0, \"id\": 1207263, \"name\": \"Tamica Washington-Miller\", \"order\": 43}, {\"cast_id\": 65, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acadb\", \"gender\": 1, \"id\": 1145098, \"name\": \"Lucy Briant\", \"order\": 44}, {\"cast_id\": 66, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acadf\", \"gender\": 2, \"id\": 33305, \"name\": \"Nathan Meister\", \"order\": 45}, {\"cast_id\": 67, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acae3\", \"gender\": 0, \"id\": 1207264, \"name\": \"Gerry Blair\", \"order\": 46}, {\"cast_id\": 68, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acae7\", \"gender\": 2, \"id\": 33311, \"name\": \"Matthew Chamberlain\", \"order\": 47}, {\"cast_id\": 69, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acaeb\", \"gender\": 0, \"id\": 1207265, \"name\": \"Paul Yates\", \"order\": 48}, {\"cast_id\": 70, \"character\": \"Op Center Duty Officer\", \"credit_id\": \"52fe48019251416c750acaef\", \"gender\": 0, \"id\": 1207266, \"name\": \"Wray Wilson\", \"order\": 49}, {\"cast_id\": 71, \"character\": \"Op Center Staff\", \"credit_id\": \"52fe48019251416c750acaf3\", \"gender\": 2, \"id\": 54492, \"name\": \"James Gaylyn\", \"order\": 50}, {\"cast_id\": 72, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acaf7\", \"gender\": 0, \"id\": 1207267, \"name\": \"Melvin Leno Clark III\", \"order\": 51}, {\"cast_id\": 73, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acafb\", \"gender\": 0, \"id\": 1207268, \"name\": \"Carvon Futrell\", \"order\": 52}, {\"cast_id\": 74, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acaff\", \"gender\": 0, \"id\": 1207269, \"name\": \"Brandon Jelkes\", \"order\": 53}, {\"cast_id\": 75, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb03\", \"gender\": 0, \"id\": 1207270, \"name\": \"Micah Moch\", \"order\": 54}, {\"cast_id\": 76, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb07\", \"gender\": 0, \"id\": 1207271, \"name\": \"Hanniyah Muhammad\", \"order\": 55}, {\"cast_id\": 77, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb0b\", \"gender\": 0, \"id\": 1207272, \"name\": \"Christopher Nolen\", \"order\": 56}, {\"cast_id\": 78, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb0f\", \"gender\": 0, \"id\": 1207273, \"name\": \"Christa Oliver\", \"order\": 57}, {\"cast_id\": 79, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb13\", \"gender\": 0, \"id\": 1207274, \"name\": \"April Marie Thomas\", \"order\": 58}, {\"cast_id\": 80, \"character\": \"Dancer\", \"credit_id\": \"52fe48019251416c750acb17\", \"gender\": 0, \"id\": 1207275, \"name\": \"Bravita A. Threatt\", \"order\": 59}, {\"cast_id\": 81, \"character\": \"Mining Chief (uncredited)\", \"credit_id\": \"52fe48019251416c750acb1b\", \"gender\": 0, \"id\": 1207276, \"name\": \"Colin Bleasdale\", \"order\": 60}, {\"cast_id\": 82, \"character\": \"Veteran Miner (uncredited)\", \"credit_id\": \"52fe48019251416c750acb1f\", \"gender\": 0, \"id\": 107969, \"name\": \"Mike Bodnar\", \"order\": 61}, {\"cast_id\": 83, \"character\": \"Richard (uncredited)\", \"credit_id\": \"52fe48019251416c750acb23\", \"gender\": 0, \"id\": 1207278, \"name\": \"Matt Clayton\", \"order\": 62}, {\"cast_id\": 84, \"character\": \"Nav\\'i (uncredited)\", \"credit_id\": \"52fe48019251416c750acb27\", \"gender\": 1, \"id\": 147898, \"name\": \"Nicole Dionne\", \"order\": 63}, {\"cast_id\": 85, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb2b\", \"gender\": 0, \"id\": 1207280, \"name\": \"Jamie Harrison\", \"order\": 64}, {\"cast_id\": 86, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb2f\", \"gender\": 0, \"id\": 1207281, \"name\": \"Allan Henry\", \"order\": 65}, {\"cast_id\": 87, \"character\": \"Ground Technician (uncredited)\", \"credit_id\": \"52fe48019251416c750acb33\", \"gender\": 2, \"id\": 1207282, \"name\": \"Anthony Ingruber\", \"order\": 66}, {\"cast_id\": 88, \"character\": \"Flight Crew Mechanic (uncredited)\", \"credit_id\": \"52fe48019251416c750acb37\", \"gender\": 0, \"id\": 1207283, \"name\": \"Ashley Jeffery\", \"order\": 67}, {\"cast_id\": 14, \"character\": \"Samson Pilot\", \"credit_id\": \"52fe48009251416c750ac9f9\", \"gender\": 0, \"id\": 98216, \"name\": \"Dean Knowsley\", \"order\": 68}, {\"cast_id\": 89, \"character\": \"Trooper (uncredited)\", \"credit_id\": \"52fe48019251416c750acb3b\", \"gender\": 0, \"id\": 1201399, \"name\": \"Joseph Mika-Hunt\", \"order\": 69}, {\"cast_id\": 90, \"character\": \"Banshee (uncredited)\", \"credit_id\": \"52fe48019251416c750acb3f\", \"gender\": 0, \"id\": 236696, \"name\": \"Terry Notary\", \"order\": 70}, {\"cast_id\": 91, \"character\": \"Soldier (uncredited)\", \"credit_id\": \"52fe48019251416c750acb43\", \"gender\": 0, \"id\": 1207287, \"name\": \"Kai Pantano\", \"order\": 71}, {\"cast_id\": 92, \"character\": \"Blast Technician (uncredited)\", \"credit_id\": \"52fe48019251416c750acb47\", \"gender\": 0, \"id\": 1207288, \"name\": \"Logan Pithyou\", \"order\": 72}, {\"cast_id\": 93, \"character\": \"Vindum Raah (uncredited)\", \"credit_id\": \"52fe48019251416c750acb4b\", \"gender\": 0, \"id\": 1207289, \"name\": \"Stuart Pollock\", \"order\": 73}, {\"cast_id\": 94, \"character\": \"Hero (uncredited)\", \"credit_id\": \"52fe48019251416c750acb4f\", \"gender\": 0, \"id\": 584868, \"name\": \"Raja\", \"order\": 74}, {\"cast_id\": 95, \"character\": \"Ops Centreworker (uncredited)\", \"credit_id\": \"52fe48019251416c750acb53\", \"gender\": 0, \"id\": 1207290, \"name\": \"Gareth Ruck\", \"order\": 75}, {\"cast_id\": 96, \"character\": \"Engineer (uncredited)\", \"credit_id\": \"52fe48019251416c750acb57\", \"gender\": 0, \"id\": 1062463, \"name\": \"Rhian Sheehan\", \"order\": 76}, {\"cast_id\": 97, \"character\": \"Col. Quaritch\\'s Mech Suit (uncredited)\", \"credit_id\": \"52fe48019251416c750acb5b\", \"gender\": 0, \"id\": 60656, \"name\": \"T. J. Storm\", \"order\": 77}, {\"cast_id\": 98, \"character\": \"Female Marine (uncredited)\", \"credit_id\": \"52fe48019251416c750acb5f\", \"gender\": 0, \"id\": 1207291, \"name\": \"Jodie Taylor\", \"order\": 78}, {\"cast_id\": 99, \"character\": \"Ikran Clan Leader (uncredited)\", \"credit_id\": \"52fe48019251416c750acb63\", \"gender\": 1, \"id\": 1186027, \"name\": \"Alicia Vela-Bailey\", \"order\": 79}, {\"cast_id\": 100, \"character\": \"Geologist (uncredited)\", \"credit_id\": \"52fe48019251416c750acb67\", \"gender\": 0, \"id\": 1207292, \"name\": \"Richard Whiteside\", \"order\": 80}, {\"cast_id\": 101, \"character\": \"Na\\'vi (uncredited)\", \"credit_id\": \"52fe48019251416c750acb6b\", \"gender\": 0, \"id\": 103259, \"name\": \"Nikie Zambo\", \"order\": 81}, {\"cast_id\": 102, \"character\": \"Ambient Room Tech / Troupe\", \"credit_id\": \"52fe48019251416c750acb6f\", \"gender\": 1, \"id\": 42286, \"name\": \"Julene Renee\", \"order\": 82}]'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ],
      "source": [
        "# checking for the cast column\n",
        "\n",
        "movies['cast'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Function to fetch only top 3 Cast"
      ],
      "metadata": {
        "id": "PuappFxRchA5"
      },
      "id": "PuappFxRchA5"
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "id": "9a62402b",
      "metadata": {
        "id": "9a62402b"
      },
      "outputs": [],
      "source": [
        "def convert_cast(obj):\n",
        "    cnt=0\n",
        "    lst=[]\n",
        "    for i in ast.literal_eval(obj):\n",
        "        if cnt!=3:\n",
        "            lst.append(i['name'])\n",
        "            cnt+=1\n",
        "        else:\n",
        "            break\n",
        "    return lst"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "id": "3861d1c0",
      "metadata": {
        "scrolled": true,
        "id": "3861d1c0"
      },
      "outputs": [],
      "source": [
        "#applying the convert_cast function\n",
        "\n",
        "movies['cast']=movies['cast'].apply(convert_cast)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "id": "abbc7eda",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 123
        },
        "id": "abbc7eda",
        "outputId": "2d12b2de-12eb-4953-e898-bf69a314b097"
      },
      "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": 64
        }
      ],
      "source": [
        "#getting first row of crew column\n",
        "\n",
        "movies['crew'][0]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Function to extract director name from the crew column"
      ],
      "metadata": {
        "id": "ofu3e5Shc0X3"
      },
      "id": "ofu3e5Shc0X3"
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "id": "0193d6bb",
      "metadata": {
        "id": "0193d6bb"
      },
      "outputs": [],
      "source": [
        "# to get the director name\n",
        "\n",
        "def director_name(obj):\n",
        "    lst=[]\n",
        "    for i in ast.literal_eval(obj):\n",
        "        if i['job']=='Director':\n",
        "            lst.append(i['name'])\n",
        "            break\n",
        "    return lst\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "id": "49534201",
      "metadata": {
        "id": "49534201"
      },
      "outputs": [],
      "source": [
        "#applying the director_name funtion\n",
        "\n",
        "movies['crew']=movies['crew'].apply(director_name)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "id": "8f15c55f",
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        "id": "8f15c55f",
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              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
              "1       285  Pirates of the Caribbean: At World's End   \n",
              "2    206647                                   Spectre   \n",
              "3     49026                     The Dark Knight Rises   \n",
              "4     49529                               John Carter   \n",
              "\n",
              "                                            overview  \\\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...   \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",
              "                                               cast                 crew  \n",
              "0  [Sam Worthington, Zoe Saldana, Sigourney Weaver]      [James Cameron]  \n",
              "1     [Johnny Depp, Orlando Bloom, Keira Knightley]     [Gore Verbinski]  \n",
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              "      <td>[ocean, drug abuse, exotic island, east india ...</td>\n",
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              "          const element = document.querySelector('#df-65352401-736a-4f44-b1aa-a71dae118023');\n",
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              "                                                     [key], {});\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 67
        }
      ],
      "source": [
        "movies.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Spllting overview on the basis of space"
      ],
      "metadata": {
        "id": "TdBVnOxadBNO"
      },
      "id": "TdBVnOxadBNO"
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "id": "abf1047c",
      "metadata": {
        "id": "abf1047c"
      },
      "outputs": [],
      "source": [
        "movies['overview']=movies['overview'].apply(lambda x:x.split())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "id": "db93165d",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "db93165d",
        "outputId": "67d749e0-e475-4abc-bb62-5129c01078ae"
      },
      "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",
              "                                            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",
              "                                          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",
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              "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",
              "                                               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",
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              "      <td>[Adventure, Fantasy, Action]</td>\n",
              "      <td>[ocean, drug abuse, exotic island, east india ...</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>[A, cryptic, message, from, Bond’s, past, send...</td>\n",
              "      <td>[Action, Adventure, Crime]</td>\n",
              "      <td>[spy, based on novel, secret agent, sequel, mi...</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>[Following, the, death, of, District, Attorney...</td>\n",
              "      <td>[Action, Crime, Drama, Thriller]</td>\n",
              "      <td>[dc comics, crime fighter, terrorist, secret i...</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>[John, Carter, is, a, war-weary,, former, mili...</td>\n",
              "      <td>[Action, Adventure, Science Fiction]</td>\n",
              "      <td>[based on novel, mars, medallion, space travel...</td>\n",
              "      <td>[Taylor Kitsch, Lynn Collins, Samantha Morton]</td>\n",
              "      <td>[Andrew Stanton]</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ],
      "source": [
        "movies.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Removing the Space from each element "
      ],
      "metadata": {
        "id": "AqTN2G5HdLYg"
      },
      "id": "AqTN2G5HdLYg"
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "id": "af279db3",
      "metadata": {
        "id": "af279db3"
      },
      "outputs": [],
      "source": [
        "movies['genres']=movies['genres'].apply(lambda x : [i.replace(\" \",\"\") for i in x])\n",
        "movies['keywords']=movies['keywords'].apply(lambda x : [i.replace(\" \",\"\") for i in x])\n",
        "movies['cast']=movies['cast'].apply(lambda x : [i.replace(\" \",\"\") for i in x])\n",
        "movies['crew']=movies['crew'].apply(lambda x : [i.replace(\" \",\"\") for i in x])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "id": "d3999c4e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "d3999c4e",
        "outputId": "9124bded-6493-40b2-9b3a-ea8570480e18"
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              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
              "1       285  Pirates of the Caribbean: At World's End   \n",
              "2    206647                                   Spectre   \n",
              "3     49026                     The Dark Knight Rises   \n",
              "4     49529                               John Carter   \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",
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              "                                         genres  \\\n",
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              "2                    [Action, Adventure, Crime]   \n",
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              "4           [Action, Adventure, ScienceFiction]   \n",
              "\n",
              "                                            keywords  \\\n",
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              "3  [dccomics, crimefighter, terrorist, secretiden...   \n",
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              "\n",
              "                                            cast                crew  \n",
              "0  [SamWorthington, ZoeSaldana, SigourneyWeaver]      [JamesCameron]  \n",
              "1     [JohnnyDepp, OrlandoBloom, KeiraKnightley]     [GoreVerbinski]  \n",
              "2      [DanielCraig, ChristophWaltz, LéaSeydoux]         [SamMendes]  \n",
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              "      <td>Pirates of the Caribbean: At World's End</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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      ],
      "source": [
        "movies.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Adding overview, genres, keywords, crew to make a single tag"
      ],
      "metadata": {
        "id": "seuYil__d9ju"
      },
      "id": "seuYil__d9ju"
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "id": "6b0f99c4",
      "metadata": {
        "id": "6b0f99c4"
      },
      "outputs": [],
      "source": [
        "movies['tags']=movies['overview']+movies['genres']+movies['keywords']+movies['cast']+movies['crew']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "id": "34afc2b0",
      "metadata": {
        "id": "34afc2b0"
      },
      "outputs": [],
      "source": [
        "movies_df=movies[['movie_id','title','tags']]"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "ZgsTqz48eKnK",
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      "id": "ZgsTqz48eKnK",
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movie_id                                     title  \\\n",
              "0     19995                                    Avatar   \n",
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              "2    206647                                   Spectre   \n",
              "3     49026                     The Dark Knight Rises   \n",
              "4     49529                               John Carter   \n",
              "\n",
              "                                                tags  \n",
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              "1  [Captain, Barbossa,, long, believed, to, be, d...  \n",
              "2  [A, cryptic, message, from, Bond’s, past, send...  \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Making tags column as string from list"
      ],
      "metadata": {
        "id": "XvgKX6YeevAa"
      },
      "id": "XvgKX6YeevAa"
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "id": "0838aba4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0838aba4",
        "outputId": "77ab6165-d819-4212-d072-7eb4c4700bb6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-469cc4a8c2e5>: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",
            "  movies_df['tags']=movies_df['tags'].apply(lambda x:\" \".join(x))\n"
          ]
        }
      ],
      "source": [
        "movies_df['tags']=movies_df['tags'].apply(lambda x:\" \".join(x))"
      ]
    },
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      "cell_type": "code",
      "execution_count": 76,
      "id": "961611c5",
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            "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",
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              "2  A cryptic message from Bond’s past sends him o...  \n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "movies_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Model Building"
      ],
      "metadata": {
        "id": "cr1CLR6YfDJT"
      },
      "id": "cr1CLR6YfDJT"
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "id": "15584022",
      "metadata": {
        "id": "15584022"
      },
      "outputs": [],
      "source": [
        "import nltk\n",
        "from nltk.stem.porter import PorterStemmer\n",
        "ps = PorterStemmer()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 78,
      "id": "03f36939",
      "metadata": {
        "id": "03f36939"
      },
      "outputs": [],
      "source": [
        "def stem(text):\n",
        "    y=[]\n",
        "    for i in text.split():\n",
        "        y.append(ps.stem(i))\n",
        "    return \" \".join(y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "id": "52d87ff1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "52d87ff1",
        "outputId": "e0f9dfbf-9239-43f9-da72-264d03097c90"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-79-7ceb4f928cfd>: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",
            "  movies_df['tags']= movies_df['tags'].apply(stem)\n"
          ]
        }
      ],
      "source": [
        "movies_df['tags']= movies_df['tags'].apply(stem)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "id": "1264924e",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "id": "1264924e",
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'in the 22nd century, a parapleg marin is dispatch to the moon pandora on a uniqu mission, but becom torn between follow order and protect an alien civilization. action adventur fantasi sciencefict cultureclash futur spacewar spacecoloni societi spacetravel futurist romanc space alien tribe alienplanet cgi marin soldier battl loveaffair antiwar powerrel mindandsoul 3d samworthington zoesaldana sigourneyweav jamescameron'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 80
        }
      ],
      "source": [
        "movies_df['tags'][0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "id": "3c547c61",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c547c61",
        "outputId": "e7a7e953-bee4-4626-a156-a6a09a82e48a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-81-1e749a422145>: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",
            "  movies_df['tags']=movies_df['tags'].apply(lambda x : x.lower())\n"
          ]
        }
      ],
      "source": [
        "movies_df['tags']=movies_df['tags'].apply(lambda x : x.lower())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "id": "fb7bdb1e",
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        "id": "fb7bdb1e",
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        {
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            "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",
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              "                                                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",
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              "  </svg>\n",
              "      </button>\n",
              "      \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-108d0c14-694e-4517-bc35-eea99539a89a 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-108d0c14-694e-4517-bc35-eea99539a89a');\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": 82
        }
      ],
      "source": [
        "movies_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Vectorization"
      ],
      "metadata": {
        "id": "PXqmaW1sfQd5"
      },
      "id": "PXqmaW1sfQd5"
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "id": "fae614f5",
      "metadata": {
        "id": "fae614f5"
      },
      "outputs": [],
      "source": [
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "cv = CountVectorizer(max_features=5000,stop_words='english')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Making vectors from the tags"
      ],
      "metadata": {
        "id": "WLdN77OWfWec"
      },
      "id": "WLdN77OWfWec"
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "id": "a39b554e",
      "metadata": {
        "id": "a39b554e"
      },
      "outputs": [],
      "source": [
        "vectors=cv.fit_transform(movies_df['tags']).toarray()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "id": "38a6d569",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "38a6d569",
        "outputId": "1c5e6e7d-29d2-4cf5-e4d3-76b61205718c"
      },
      "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": 85
        }
      ],
      "source": [
        "vectors"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "id": "0330a3c1",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0330a3c1",
        "outputId": "67833af1-decc-4043-9e96-a44dc2201296"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4806, 5000)"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ],
      "source": [
        "vectors.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating the cosine Distance between each vectors"
      ],
      "metadata": {
        "id": "4LbiLER_fd6K"
      },
      "id": "4LbiLER_fd6K"
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "id": "a76f1be9",
      "metadata": {
        "id": "a76f1be9"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "id": "3e69bdb6",
      "metadata": {
        "id": "3e69bdb6"
      },
      "outputs": [],
      "source": [
        "similarity= cosine_similarity(vectors)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "id": "155ab0c4",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "155ab0c4",
        "outputId": "ebaabc0f-c533-4541-a22c-a7f8d1b5ee43"
      },
      "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": 89
        }
      ],
      "source": [
        "similarity"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "id": "4930ecff",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4930ecff",
        "outputId": "f3f0be6e-dc29-473a-d9d8-283229d4d654"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.08346223, 1.        , 0.06063391, ..., 0.02378257, 0.        ,\n",
              "       0.02615329])"
            ]
          },
          "metadata": {},
          "execution_count": 90
        }
      ],
      "source": [
        "similarity[1]"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Recommend function"
      ],
      "metadata": {
        "id": "k0aKU3EhfmJb"
      },
      "id": "k0aKU3EhfmJb"
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "id": "3215a9ec",
      "metadata": {
        "id": "3215a9ec"
      },
      "outputs": [],
      "source": [
        "def recommend(movie):\n",
        "    \n",
        "    #getting the index of the current movies\n",
        "    movie_index=movies_df[movies_df['title']==movie].index[0]\n",
        "\n",
        "    #getting the distance values for the current movie with others\n",
        "    distance = similarity[movie_index]\n",
        "\n",
        "    #sorting movies with enumerate function to avoid loss of index \n",
        "    movies_list = sorted(list(enumerate(distance)),reverse = True,key=lambda x:x[1])[1:6]\n",
        "    \n",
        "    #Getting the top 5 similar movies\n",
        "    for i in movies_list:\n",
        "        print(movies_df.iloc[i[0]].title)\n",
        "        "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "id": "3c1d8c73",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c1d8c73",
        "outputId": "ea0f67e3-f56c-44ff-ba9f-424aa2109a3c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Dark Knight\n",
            "Batman\n",
            "Batman\n",
            "The Dark Knight Rises\n",
            "10th & Wolf\n"
          ]
        }
      ],
      "source": [
        "#applying the function recommend\n",
        "\n",
        "recommend('Batman Begins')"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.12"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}