MovieReccommendation.ipynb

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      "source": [
        "# **Movie** **Recommendation** **System**"
      ],
      "metadata": {
        "id": "c8jHEhhTNIm1"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**import library**\n"
      ],
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        "id": "bcnzZH3rN9eM"
      }
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    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
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      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
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        "id": "7KEkSeprOf24"
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      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Import Dataset**"
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        "id": "bz1bWGtSOjtg"
      }
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    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/Lorddhaval/Dataset/main/Movies%20Recommendation.csv')\n"
      ],
      "metadata": {
        "id": "mJ3iF71YOl90"
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      "execution_count": 3,
      "outputs": []
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      "source": [
        "df.head()"
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      "execution_count": 4,
      "outputs": [
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            "text/plain": [
              "   Movie_ID      Movie_Title                       Movie_Genre Movie_Language  \\\n",
              "0         1       Four Rooms                      Crime Comedy             en   \n",
              "1         2        Star Wars  Adventure Action Science Fiction             en   \n",
              "2         3     Finding Nemo                  Animation Family             en   \n",
              "3         4     Forrest Gump              Comedy Drama Romance             en   \n",
              "4         5  American Beauty                             Drama             en   \n",
              "\n",
              "   Movie_Budget  Movie_Popularity Movie_Release_Date  Movie_Revenue  \\\n",
              "0       4000000         22.876230         09-12-1995        4300000   \n",
              "1      11000000        126.393695         25-05-1977      775398007   \n",
              "2      94000000         85.688789         30-05-2003      940335536   \n",
              "3      55000000        138.133331         06-07-1994      677945399   \n",
              "4      15000000         80.878605         15-09-1999      356296601   \n",
              "\n",
              "   Movie_Runtime  Movie_Vote  ...  \\\n",
              "0           98.0         6.5  ...   \n",
              "1          121.0         8.1  ...   \n",
              "2          100.0         7.6  ...   \n",
              "3          142.0         8.2  ...   \n",
              "4          122.0         7.9  ...   \n",
              "\n",
              "                                      Movie_Homepage  \\\n",
              "0                                                NaN   \n",
              "1  http://www.starwars.com/films/star-wars-episod...   \n",
              "2              http://movies.disney.com/finding-nemo   \n",
              "3                                                NaN   \n",
              "4                      http://www.dreamworks.com/ab/   \n",
              "\n",
              "                                      Movie_Keywords  \\\n",
              "0          hotel new year's eve witch bet hotel room   \n",
              "1        android galaxy hermit death star lightsaber   \n",
              "2  father son relationship harbor underwater fish...   \n",
              "3  vietnam veteran hippie mentally disabled runni...   \n",
              "4  male nudity female nudity adultery midlife cri...   \n",
              "\n",
              "                                      Movie_Overview  \\\n",
              "0  It's Ted the Bellhop's first night on the job....   \n",
              "1  Princess Leia is captured and held hostage by ...   \n",
              "2  Nemo, an adventurous young clownfish, is unexp...   \n",
              "3  A man with a low IQ has accomplished great thi...   \n",
              "4  Lester Burnham, a depressed suburban father in...   \n",
              "\n",
              "                              Movie_Production_House  \\\n",
              "0  [{\"name\": \"Miramax Films\", \"id\": 14}, {\"name\":...   \n",
              "1  [{\"name\": \"Lucasfilm\", \"id\": 1}, {\"name\": \"Twe...   \n",
              "2     [{\"name\": \"Pixar Animation Studios\", \"id\": 3}]   \n",
              "3          [{\"name\": \"Paramount Pictures\", \"id\": 4}]   \n",
              "4  [{\"name\": \"DreamWorks SKG\", \"id\": 27}, {\"name\"...   \n",
              "\n",
              "                            Movie_Production_Country  \\\n",
              "0  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   \n",
              "1  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   \n",
              "2  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   \n",
              "3  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   \n",
              "4  [{\"iso_3166_1\": \"US\", \"name\": \"United States o...   \n",
              "\n",
              "                      Movie_Spoken_Language  \\\n",
              "0  [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "1  [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "2  [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "3  [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "4  [{\"iso_639_1\": \"en\", \"name\": \"English\"}]   \n",
              "\n",
              "                                       Movie_Tagline  \\\n",
              "0  Twelve outrageous guests. Four scandalous requ...   \n",
              "1       A long time ago in a galaxy far, far away...   \n",
              "2  There are 3.7 trillion fish in the ocean, they...   \n",
              "3  The world will never be the same, once you've ...   \n",
              "4                                       Look closer.   \n",
              "\n",
              "                                          Movie_Cast  \\\n",
              "0  Tim Roth Antonio Banderas Jennifer Beals Madon...   \n",
              "1  Mark Hamill Harrison Ford Carrie Fisher Peter ...   \n",
              "2  Albert Brooks Ellen DeGeneres Alexander Gould ...   \n",
              "3  Tom Hanks Robin Wright Gary Sinise Mykelti Wil...   \n",
              "4  Kevin Spacey Annette Bening Thora Birch Wes Be...   \n",
              "\n",
              "                                          Movie_Crew   Movie_Director  \n",
              "0  [{'name': 'Allison Anders', 'gender': 1, 'depa...   Allison Anders  \n",
              "1  [{'name': 'George Lucas', 'gender': 2, 'depart...     George Lucas  \n",
              "2  [{'name': 'Andrew Stanton', 'gender': 2, 'depa...   Andrew Stanton  \n",
              "3  [{'name': 'Alan Silvestri', 'gender': 2, 'depa...  Robert Zemeckis  \n",
              "4  [{'name': 'Thomas Newman', 'gender': 2, 'depar...       Sam Mendes  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-5dd70685-8022-4ee5-a617-76ad1eb7b474 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CBdbVL8lPzmW",
        "outputId": "bae5442b-af31-4901-d90e-98fa6f1c8ca7"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cvmyR5TTP3Pu",
        "outputId": "e54cc1da-7f63-47ad-ce2a-106f9bb8d130"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Movie_ID', 'Movie_Title', 'Movie_Genre', 'Movie_Language',\n",
              "       'Movie_Budget', 'Movie_Popularity', 'Movie_Release_Date',\n",
              "       'Movie_Revenue', 'Movie_Runtime', 'Movie_Vote', 'Movie_Vote_Count',\n",
              "       'Movie_Homepage', 'Movie_Keywords', 'Movie_Overview',\n",
              "       'Movie_Production_House', 'Movie_Production_Country',\n",
              "       'Movie_Spoken_Language', 'Movie_Tagline', 'Movie_Cast', 'Movie_Crew',\n",
              "       'Movie_Director'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "**Get Feature** **Selection**\n",
        "\n"
      ],
      "metadata": {
        "id": "s7nlM80Ycioj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_features = df[['Movie_Genre','Movie_Keywords','Movie_Tagline','Movie_Cast','Movie_Director']].fillna('')\n"
      ],
      "metadata": {
        "id": "0WGQXm53QGtO"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_features.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oM8ERkJ9QKAS",
        "outputId": "fe480243-bc62-4b35-cc87-a4edfeaa0f39"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 579
        },
        "id": "ChbzhA2ZQNwF",
        "outputId": "6d9c1317-31b7-42bd-b623-e9fcf9003b2a"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           Movie_Genre  \\\n",
              "0                         Crime Comedy   \n",
              "1     Adventure Action Science Fiction   \n",
              "2                     Animation Family   \n",
              "3                 Comedy Drama Romance   \n",
              "4                                Drama   \n",
              "...                                ...   \n",
              "4755                            Horror   \n",
              "4756               Comedy Family Drama   \n",
              "4757                    Thriller Drama   \n",
              "4758                            Family   \n",
              "4759                       Documentary   \n",
              "\n",
              "                                         Movie_Keywords  \\\n",
              "0             hotel new year's eve witch bet hotel room   \n",
              "1           android galaxy hermit death star lightsaber   \n",
              "2     father son relationship harbor underwater fish...   \n",
              "3     vietnam veteran hippie mentally disabled runni...   \n",
              "4     male nudity female nudity adultery midlife cri...   \n",
              "...                                                 ...   \n",
              "4755                                                      \n",
              "4756                                                      \n",
              "4757                     christian film sex trafficking   \n",
              "4758                                                      \n",
              "4759  music actors legendary perfomer classic hollyw...   \n",
              "\n",
              "                                          Movie_Tagline  \\\n",
              "0     Twelve outrageous guests. Four scandalous requ...   \n",
              "1          A long time ago in a galaxy far, far away...   \n",
              "2     There are 3.7 trillion fish in the ocean, they...   \n",
              "3     The world will never be the same, once you've ...   \n",
              "4                                          Look closer.   \n",
              "...                                                 ...   \n",
              "4755                The hot spot where Satan's waitin'.   \n",
              "4756           It’s better to stand out than to fit in.   \n",
              "4757           She never knew it could happen to her...   \n",
              "4758                                                      \n",
              "4759                                                      \n",
              "\n",
              "                                             Movie_Cast     Movie_Director  \n",
              "0     Tim Roth Antonio Banderas Jennifer Beals Madon...     Allison Anders  \n",
              "1     Mark Hamill Harrison Ford Carrie Fisher Peter ...       George Lucas  \n",
              "2     Albert Brooks Ellen DeGeneres Alexander Gould ...     Andrew Stanton  \n",
              "3     Tom Hanks Robin Wright Gary Sinise Mykelti Wil...    Robert Zemeckis  \n",
              "4     Kevin Spacey Annette Bening Thora Birch Wes Be...         Sam Mendes  \n",
              "...                                                 ...                ...  \n",
              "4755  Lisa Hart Carroll Michael Des Barres Paul Drak...         Pece Dingo  \n",
              "4756  Roni Akurati Brighton Sharbino Jason Lee Anjul...       Frank Lotito  \n",
              "4757  Nicole Smolen Kim Baldwin Ariana Stephens Brys...       Jaco Booyens  \n",
              "4758                                                                        \n",
              "4759                                    Tony Oppedisano  Simon Napier-Bell  \n",
              "\n",
              "[4760 rows x 5 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-387604de-092f-4243-956d-43ad970c3b7c\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Movie_Genre</th>\n",
              "      <th>Movie_Keywords</th>\n",
              "      <th>Movie_Tagline</th>\n",
              "      <th>Movie_Cast</th>\n",
              "      <th>Movie_Director</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Crime Comedy</td>\n",
              "      <td>hotel new year's eve witch bet hotel room</td>\n",
              "      <td>Twelve outrageous guests. Four scandalous requ...</td>\n",
              "      <td>Tim Roth Antonio Banderas Jennifer Beals Madon...</td>\n",
              "      <td>Allison Anders</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adventure Action Science Fiction</td>\n",
              "      <td>android galaxy hermit death star lightsaber</td>\n",
              "      <td>A long time ago in a galaxy far, far away...</td>\n",
              "      <td>Mark Hamill Harrison Ford Carrie Fisher Peter ...</td>\n",
              "      <td>George Lucas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Animation Family</td>\n",
              "      <td>father son relationship harbor underwater fish...</td>\n",
              "      <td>There are 3.7 trillion fish in the ocean, they...</td>\n",
              "      <td>Albert Brooks Ellen DeGeneres Alexander Gould ...</td>\n",
              "      <td>Andrew Stanton</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Comedy Drama Romance</td>\n",
              "      <td>vietnam veteran hippie mentally disabled runni...</td>\n",
              "      <td>The world will never be the same, once you've ...</td>\n",
              "      <td>Tom Hanks Robin Wright Gary Sinise Mykelti Wil...</td>\n",
              "      <td>Robert Zemeckis</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Drama</td>\n",
              "      <td>male nudity female nudity adultery midlife cri...</td>\n",
              "      <td>Look closer.</td>\n",
              "      <td>Kevin Spacey Annette Bening Thora Birch Wes Be...</td>\n",
              "      <td>Sam Mendes</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4755</th>\n",
              "      <td>Horror</td>\n",
              "      <td></td>\n",
              "      <td>The hot spot where Satan's waitin'.</td>\n",
              "      <td>Lisa Hart Carroll Michael Des Barres Paul Drak...</td>\n",
              "      <td>Pece Dingo</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4756</th>\n",
              "      <td>Comedy Family Drama</td>\n",
              "      <td></td>\n",
              "      <td>It’s better to stand out than to fit in.</td>\n",
              "      <td>Roni Akurati Brighton Sharbino Jason Lee Anjul...</td>\n",
              "      <td>Frank Lotito</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4757</th>\n",
              "      <td>Thriller Drama</td>\n",
              "      <td>christian film sex trafficking</td>\n",
              "      <td>She never knew it could happen to her...</td>\n",
              "      <td>Nicole Smolen Kim Baldwin Ariana Stephens Brys...</td>\n",
              "      <td>Jaco Booyens</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4758</th>\n",
              "      <td>Family</td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "      <td></td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4759</th>\n",
              "      <td>Documentary</td>\n",
              "      <td>music actors legendary perfomer classic hollyw...</td>\n",
              "      <td></td>\n",
              "      <td>Tony Oppedisano</td>\n",
              "      <td>Simon Napier-Bell</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4760 rows × 5 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-387604de-092f-4243-956d-43ad970c3b7c')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-387604de-092f-4243-956d-43ad970c3b7c 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-387604de-092f-4243-956d-43ad970c3b7c');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-7804a2cc-f790-4783-ad45-7e508bf742b8\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-7804a2cc-f790-4783-ad45-7e508bf742b8')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-7804a2cc-f790-4783-ad45-7e508bf742b8 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_8e3b0c3f-aaad-4448-87df-7c64052c83e1\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('df_features')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_8e3b0c3f-aaad-4448-87df-7c64052c83e1 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('df_features');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df_features",
              "summary": "{\n  \"name\": \"df_features\",\n  \"rows\": 4760,\n  \"fields\": [\n    {\n      \"column\": \"Movie_Genre\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1164,\n        \"samples\": [\n          \"Family Animation Adventure\",\n          \"Adventure Science Fiction Action\",\n          \"Action Crime Drama Romance Thriller\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Movie_Keywords\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4203,\n        \"samples\": [\n          \"canada nazis sequel spin off ancient evil\",\n          \"sex professor wedding woman director columbia university\",\n          \"prophecy sea beach gold small town\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Movie_Tagline\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3928,\n        \"samples\": [\n          \"The messenger must be silenced.\",\n          \"It's not the house that's haunted.\",\n          \"The final hunt begins.\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Movie_Cast\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4715,\n        \"samples\": [\n          \"Robin Tunney Fairuza Balk Neve Campbell Rachel True Skeet Ulrich\",\n          \"Peter Sellers Christopher Plummer Herbert Lom Catherine Schell Peter Arne\",\n          \"John Wayne Laraine Day Cedric Hardwicke Judith Anderson Anthony Quinn\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Movie_Director\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2332,\n        \"samples\": [\n          \"Rob McKittrick\",\n          \"Terron R. Parsons\",\n          \"Cyrus Nowrasteh\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Features Text Conversion to Tokens**"
      ],
      "metadata": {
        "id": "0hwCg_b0cuer"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x = df_features['Movie_Genre'] + ' ' + df_features['Movie_Keywords'] + ' ' + df_features['Movie_Tagline'] + ' ' + df_features['Movie_Cast']+ ' ' + df_features['Movie_Director']\n"
      ],
      "metadata": {
        "id": "zTgKjeQNQR55"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EPT8y5d9QVrS",
        "outputId": "e463c5c3-f2ef-4ceb-84f7-b831dc307a19"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760,)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "metadata": {
        "id": "-T86AhojQaJo"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "tfidf = TfidfVectorizer()"
      ],
      "metadata": {
        "id": "xudG0PzpQczm"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x = tfidf.fit_transform(x)"
      ],
      "metadata": {
        "id": "17v1doygQd9h"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "x.shape\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_gdyVS2dQoTg",
        "outputId": "76a638e8-44e5-46aa-886d-461a06625e5b"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 17258)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SotLtdx0Qrii",
        "outputId": "bf711801-7299-4774-efd6-385ec788db77"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 617)\t0.1633382144407513\n",
            "  (0, 492)\t0.1432591540388685\n",
            "  (0, 15413)\t0.1465525095337543\n",
            "  (0, 9675)\t0.14226057295252661\n",
            "  (0, 9465)\t0.1659841367820977\n",
            "  (0, 1390)\t0.16898383612799558\n",
            "  (0, 7825)\t0.09799561597509843\n",
            "  (0, 1214)\t0.13865857545144072\n",
            "  (0, 729)\t0.13415063359531618\n",
            "  (0, 13093)\t0.1432591540388685\n",
            "  (0, 15355)\t0.10477815972666779\n",
            "  (0, 9048)\t0.0866842116160778\n",
            "  (0, 11161)\t0.06250380151644369\n",
            "  (0, 16773)\t0.17654247479915475\n",
            "  (0, 5612)\t0.08603537588547631\n",
            "  (0, 16735)\t0.10690083751525419\n",
            "  (0, 7904)\t0.13348000542112332\n",
            "  (0, 15219)\t0.09800472886453934\n",
            "  (0, 11242)\t0.07277788238484746\n",
            "  (0, 3878)\t0.11998399582562203\n",
            "  (0, 5499)\t0.11454057510303811\n",
            "  (0, 7071)\t0.19822417598406614\n",
            "  (0, 7454)\t0.14745635785412262\n",
            "  (0, 1495)\t0.19712637387361423\n",
            "  (0, 9206)\t0.15186283580984414\n",
            "  :\t:\n",
            "  (4757, 5455)\t0.12491480594769522\n",
            "  (4757, 2967)\t0.16273475835631626\n",
            "  (4757, 8464)\t0.23522565554066333\n",
            "  (4757, 6938)\t0.17088173678136628\n",
            "  (4757, 8379)\t0.17480603856721913\n",
            "  (4757, 15303)\t0.07654356007668191\n",
            "  (4757, 15384)\t0.09754322497537371\n",
            "  (4757, 7649)\t0.11479421494340192\n",
            "  (4757, 10896)\t0.14546473055066447\n",
            "  (4757, 4494)\t0.05675298448720501\n",
            "  (4758, 5238)\t1.0\n",
            "  (4759, 11264)\t0.33947721804318337\n",
            "  (4759, 11708)\t0.33947721804318337\n",
            "  (4759, 205)\t0.3237911628497312\n",
            "  (4759, 8902)\t0.3040290704566037\n",
            "  (4759, 14062)\t0.3237911628497312\n",
            "  (4759, 3058)\t0.2812896191863103\n",
            "  (4759, 7130)\t0.26419662449963793\n",
            "  (4759, 10761)\t0.3126617295732147\n",
            "  (4759, 4358)\t0.18306542312175342\n",
            "  (4759, 14051)\t0.20084315377640435\n",
            "  (4759, 5690)\t0.19534291014627303\n",
            "  (4759, 15431)\t0.19628653185946862\n",
            "  (4759, 1490)\t0.21197258705292082\n",
            "  (4759, 10666)\t0.15888268987343043\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Similarity Score Using Cosine Similarity**"
      ],
      "metadata": {
        "id": "9LKJiFO4c26D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "T2MAbLt2Qu_z"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Similarity_Score = cosine_similarity(x)"
      ],
      "metadata": {
        "id": "ogOS4dg9QxgC"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Similarity_Score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ihT9LRZXQ1xK",
        "outputId": "ab2d9970-22f2-4936-9c6a-dbc8d391ecfc"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1.        , 0.01351235, 0.03570468, ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.01351235, 1.        , 0.00806674, ..., 0.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.03570468, 0.00806674, 1.        , ..., 0.        , 0.08014876,\n",
              "        0.        ],\n",
              "       ...,\n",
              "       [0.        , 0.        , 0.        , ..., 1.        , 0.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.08014876, ..., 0.        , 1.        ,\n",
              "        0.        ],\n",
              "       [0.        , 0.        , 0.        , ..., 0.        , 0.        ,\n",
              "        1.        ]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Similarity_Score.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NzE5WgTTQ5Yi",
        "outputId": "94940e09-443d-47ec-829c-10e5252a53c2"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 4760)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Get Movie Name as Input form User & Validate for Closest Spelling**"
      ],
      "metadata": {
        "id": "uP_WhS1Nc-DY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Favourite_Movie_Name = input(' Enter your favourite movie name :')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eGKoAtnrQ7xt",
        "outputId": "260e3a22-c42e-41c9-ac09-f32444d76a42"
      },
      "execution_count": 21,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            " Enter your favourite movie name :iron man\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "All_Movies_Title_List = df['Movie_Title'].tolist()"
      ],
      "metadata": {
        "id": "2YhI-UxUQ-iQ"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "import difflib\n"
      ],
      "metadata": {
        "id": "YIbHLFI4RNTT"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Movie_Recommendation = difflib.get_close_matches(Favourite_Movie_Name, All_Movies_Title_List)\n",
        "print(Movie_Recommendation)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AuD3lFBNRPqZ",
        "outputId": "81c1a5ed-1549-4656-b864-08fec5b883e9"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Iron Man', 'Iron Man 3', 'Iron Man 2']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Close_Match = Movie_Recommendation[0]\n",
        "print(Close_Match)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCzcNPQSRR5K",
        "outputId": "eaa50d00-da68-4008-8640-5e92ea85f95a"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Iron Man\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "\n",
        "Index_of_Close_Match_Movie = df[df.Movie_Title == Close_Match]['Movie_ID'].values[0]\n",
        "print(Index_of_Close_Match_Movie)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZvbIqwK6WlHN",
        "outputId": "5c688d79-f66e-4431-817e-53df94e68cc8"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "568\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "Recommendation_Score = list(enumerate(Similarity_Score[Index_of_Close_Match_Movie]))\n",
        "print(Recommendation_Score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NbnZlEa3Wrei",
        "outputId": "695285c1-ed33-46cc-fd37-fd6beaf640b1"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sorted_Similar_Movies = sorted(Recommendation_Score, key = lambda x:x[1],reverse =True)\n",
        "print(Sorted_Similar_Movies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8cJyU70jXXP-",
        "outputId": "d0d5f74f-3a6d-4f8d-9af9-c27134d66936"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(568, 1.0000000000000002), (570, 0.23774638753738936), (3724, 0.1834643245085129), (2361, 0.16436286018967577), (418, 0.16093391568075596), (1799, 0.16089450005652564), (1681, 0.15366093144614124), (1540, 0.14326199460383862), (3590, 0.14011656375639475), (682, 0.13792739122041112), (3459, 0.13763496549881873), (495, 0.13172799619425368), (1732, 0.13151019053607485), (1333, 0.13068374655395776), (1736, 0.12838464360565432), (1113, 0.12722643271405384), (4279, 0.12378550300043255), (1578, 0.1218507485725904), (4742, 0.12136086712670666), (32, 0.11981278942051383), (2113, 0.11937772926054144), (2055, 0.11793110203851845), (1788, 0.11512033774631325), (4589, 0.10981794830020203), (1948, 0.10926697883678246), (668, 0.10384678106617842), (2697, 0.10280361085864685), (904, 0.10232723973821155), (1844, 0.10201289204738176), (3457, 0.09941298667653828), (2810, 0.09750823594715402), (1784, 0.09669182663465674), (1495, 0.0961889423177075), (905, 0.0958935386885064), (644, 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print the name of movie based on index"
      ],
      "metadata": {
        "id": "008j6q2ZYJlY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "print('Top 30 Movies Suggested for You : \\n')\n",
        "\n",
        "i = 1\n",
        "for movie in Sorted_Similar_Movies:\n",
        "  index = movie[0]\n",
        "  title_from_index = df[df.index==index]['Movie_Title'].values[0]\n",
        "  if (i<31):\n",
        "    print(i, '.',title_from_index)\n",
        "    i+=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIF-acxJYOty",
        "outputId": "6d5018f2-1376-492b-ce35-68bb0f1a56d3"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 30 Movies Suggested for You : \n",
            "\n",
            "1 . The Forbidden Kingdom\n",
            "2 . The Mummy: Tomb of the Dragon Emperor\n",
            "3 . Resident Evil: Retribution\n",
            "4 . The Warlords\n",
            "5 . Lethal Weapon 4\n",
            "6 . The One\n",
            "7 . War\n",
            "8 . Unleashed\n",
            "9 . Snow Flower and the Secret Fan\n",
            "10 . Kiss of the Dragon\n",
            "11 . Warriors of Virtue\n",
            "12 . Curse of the Golden Flower\n",
            "13 . The Medallion\n",
            "14 . Kung Fu Panda\n",
            "15 . Cradle 2 the Grave\n",
            "16 . Shanghai Noon\n",
            "17 . Gods of Egypt\n",
            "18 . Stuart Little\n",
            "19 . The Monkey King 2\n",
            "20 . Hero\n",
            "21 . The Legend of Drunken Master\n",
            "22 . Young Sherlock Holmes\n",
            "23 . Without a Paddle\n",
            "24 . Dragon Blade\n",
            "25 . Superman IV: The Quest for Peace\n",
            "26 . Romeo Must Die\n",
            "27 . Shinjuku Incident\n",
            "28 . Rush Hour 3\n",
            "29 . Stuart Little 2\n",
            "30 . Kung Fu Panda 2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Sorted_Similar_Movies = sorted(Recommendation_Score, key = lambda x:x[1],reverse =True)\n",
        "print(Sorted_Similar_Movies)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zvIBxQD5a0o2",
        "outputId": "e9fabd47-a16e-468c-d317-2cbfb096f9fe"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Top 10 Movie Recommendation System**"
      ],
      "metadata": {
        "id": "vxPKn5jrb3c2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Movie_Name = input('Enter Your Favourite Movie Name: ')\n",
        "\n",
        "list_of_all_titles = df['Movie_Title'].tolist()\n",
        "\n",
        "Find_Close_Match = difflib.get_close_matches(Movie_Name, list_of_all_titles)\n",
        "\n",
        "Close_Match = Find_Close_Match[0]\n",
        "\n",
        "Index_of_Movie = df[df.Movie_Title == Close_Match]['Movie_ID'].values[0]\n",
        "\n",
        "Recommendation_Score = list(enumerate(Similarity_Score[Index_of_Movie]))\n",
        "\n",
        "sorted_similar_movies = sorted(Recommendation_Score, key = lambda x:x[1], reverse = True)\n",
        "\n",
        "print('Top 10 Movies Suggested For You : \\n')\n",
        "i = 1\n",
        "for movie in Sorted_Similar_Movies:\n",
        "  index = movie[0]\n",
        "  title_from_index = df[df.Movie_ID==index]['Movie_Title'].values\n",
        "  if (i<11):\n",
        "    print(i, '.',title_from_index)\n",
        "    i+=1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SWAs90KZbWmG",
        "outputId": "7ecc7b99-a3a2-406c-883e-e67a769a4538"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter Your Favourite Movie Name: iron man\n",
            "Top 10 Movies Suggested For You : \n",
            "\n",
            "1 . ['Iron Man']\n",
            "2 . ['The Mummy Returns']\n",
            "3 . ['Fascination']\n",
            "4 . ['Out Cold']\n",
            "5 . ['Lethal Weapon 3']\n",
            "6 . ['Robin and Marian']\n",
            "7 . ['Hackers']\n",
            "8 . ['Just My Luck']\n",
            "9 . ['Snow White and the Huntsman']\n",
            "10 . ['The Time Machine']\n"
          ]
        }
      ]
    }
  ]
}