Untitled1.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/mayankjr98/D4Dash/blob/main/Untitled1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "markdown",
      "source": [
        "# **Import Library**"
      ],
      "metadata": {
        "id": "V8KCb0mv3KyS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd"
      ],
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        "id": "OmaLjLbs3Uph"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np"
      ],
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        "id": "h6TBu_nq3ZT-"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Import Dataset**"
      ],
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        "id": "4H_j5zAa3haw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(r'https://raw.githubusercontent.com/YBIFoundation/Dataset/refs/heads/main/Movies%20Recommendation.csv')"
      ],
      "metadata": {
        "id": "hQCWiXsg3mAr"
      },
      "execution_count": null,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df.head()"
      ],
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "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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              "      <td>It's Ted the Bellhop's first night on the job....</td>\n",
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              "      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-8e64fd17-4954-4cc9-ae0a-edbaccceeb4e 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": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QZuC3vUf4nzQ",
        "outputId": "f407716b-33a4-44ba-84c1-d33918a92bed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4760 entries, 0 to 4759\n",
            "Data columns (total 21 columns):\n",
            " #   Column                    Non-Null Count  Dtype  \n",
            "---  ------                    --------------  -----  \n",
            " 0   Movie_ID                  4760 non-null   int64  \n",
            " 1   Movie_Title               4760 non-null   object \n",
            " 2   Movie_Genre               4760 non-null   object \n",
            " 3   Movie_Language            4760 non-null   object \n",
            " 4   Movie_Budget              4760 non-null   int64  \n",
            " 5   Movie_Popularity          4760 non-null   float64\n",
            " 6   Movie_Release_Date        4760 non-null   object \n",
            " 7   Movie_Revenue             4760 non-null   int64  \n",
            " 8   Movie_Runtime             4758 non-null   float64\n",
            " 9   Movie_Vote                4760 non-null   float64\n",
            " 10  Movie_Vote_Count          4760 non-null   int64  \n",
            " 11  Movie_Homepage            1699 non-null   object \n",
            " 12  Movie_Keywords            4373 non-null   object \n",
            " 13  Movie_Overview            4757 non-null   object \n",
            " 14  Movie_Production_House    4760 non-null   object \n",
            " 15  Movie_Production_Country  4760 non-null   object \n",
            " 16  Movie_Spoken_Language     4760 non-null   object \n",
            " 17  Movie_Tagline             3942 non-null   object \n",
            " 18  Movie_Cast                4733 non-null   object \n",
            " 19  Movie_Crew                4760 non-null   object \n",
            " 20  Movie_Director            4738 non-null   object \n",
            "dtypes: float64(3), int64(4), object(14)\n",
            "memory usage: 781.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X1JoADxV4ycx",
        "outputId": "ba66ec9c-5918-4457-f9c1-54a4ec69e3de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 21)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eC2OrS5d44wr",
        "outputId": "943c3fa8-8264-4b22-894c-e6cbb804873c"
      },
      "execution_count": null,
      "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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Get Feature Selection**"
      ],
      "metadata": {
        "id": "ThEU2p0U5eeD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_features = df[['Movie_Genre','Movie_Keywords', 'Movie_Tagline', 'Movie_Cast', 'Movie_Director']].fillna('')"
      ],
      "metadata": {
        "id": "U0PZ6nqQ5lcf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selected five existing features to recommend movies."
      ],
      "metadata": {
        "id": "4Bf_ofoX7mi1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df_features.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xYLyZMJv749v",
        "outputId": "a0a7f9b8-aa7a-42a3-b9e0-afd16daee736"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_features"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "5t0PqI8K8Gqw",
        "outputId": "7dbab88b-0f4f-43b3-8e5f-04e284d43251"
      },
      "execution_count": null,
      "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]"
            ],
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              "      <td>Nicole Smolen Kim Baldwin Ariana Stephens Brys...</td>\n",
              "      <td>Jaco Booyens</td>\n",
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              "      <td></td>\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
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              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-62f76661-88f7-4b2a-b74a-3551b0313e42 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_3aa0e537-a5de-4558-9a4a-eb821ba4ccf3\">\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_3aa0e537-a5de-4558-9a4a-eb821ba4ccf3 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": 16
        }
      ]
    },
    {
      "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']"
      ],
      "metadata": {
        "id": "-08X4xKY8U9q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "hJGpiL1Z88jt",
        "outputId": "09f4509d-9591-420a-f5b2-100ecbe7828d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0       Crime Comedy hotel new year's eve witch bet ho...\n",
              "1       Adventure Action Science Fiction android galax...\n",
              "2       Animation Family father son relationship harbo...\n",
              "3       Comedy Drama Romance vietnam veteran hippie me...\n",
              "4       Drama male nudity female nudity adultery midli...\n",
              "                              ...                        \n",
              "4755    Horror  The hot spot where Satan's waitin'. Li...\n",
              "4756    Comedy Family Drama  It’s better to stand out ...\n",
              "4757    Thriller Drama christian film sex trafficking ...\n",
              "4758                                           Family    \n",
              "4759    Documentary music actors legendary perfomer cl...\n",
              "Length: 4760, dtype: object"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Crime Comedy hotel new year's eve witch bet ho...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Adventure Action Science Fiction android galax...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Animation Family father son relationship harbo...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Comedy Drama Romance vietnam veteran hippie me...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Drama male nudity female nudity adultery midli...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4755</th>\n",
              "      <td>Horror  The hot spot where Satan's waitin'. Li...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4756</th>\n",
              "      <td>Comedy Family Drama  It’s better to stand out ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4757</th>\n",
              "      <td>Thriller Drama christian film sex trafficking ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4758</th>\n",
              "      <td>Family</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4759</th>\n",
              "      <td>Documentary music actors legendary perfomer cl...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4760 rows × 1 columns</p>\n",
              "</div><br><label><b>dtype:</b> object</label>"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lwAZrApH9FH3",
        "outputId": "7856abea-28e1-4fb9-feae-788ed9e4b3a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760,)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Get Feature Text Conversion to Tokens**"
      ],
      "metadata": {
        "id": "I0FXk2Ae9Osn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.feature_extraction.text import TfidfVectorizer"
      ],
      "metadata": {
        "id": "KkcQzuLS9XuS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tfidf = TfidfVectorizer()"
      ],
      "metadata": {
        "id": "4WMad_et9p_l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x = tfidf.fit_transform(x)"
      ],
      "metadata": {
        "id": "Sx7SHkzc92PF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yIp_mPZ-ESH",
        "outputId": "16e4957a-30de-4146-bc93-6e1366bc67ef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 17258)"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(x)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rjZiGvR8-MNF",
        "outputId": "14df5a1d-ee65-42a3-93fa-c09ccfbaea5b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  (0, 3583)\t0.06486754376295062\n",
            "  (0, 3240)\t0.04527089872278055\n",
            "  (0, 7213)\t0.25146675849405775\n",
            "  (0, 10898)\t0.17625708810661284\n",
            "  (0, 17052)\t0.26079573581490934\n",
            "  (0, 5059)\t0.29553419178998613\n",
            "  (0, 16862)\t0.12768803549311025\n",
            "  (0, 1595)\t0.15687561633854538\n",
            "  (0, 13052)\t0.1465525095337543\n",
            "  (0, 15708)\t0.17654247479915475\n",
            "  (0, 11362)\t0.18801785343006192\n",
            "  (0, 6463)\t0.18801785343006192\n",
            "  (0, 5662)\t0.1465525095337543\n",
            "  (0, 13467)\t0.19712637387361423\n",
            "  (0, 12731)\t0.19712637387361423\n",
            "  (0, 614)\t0.07642616241686973\n",
            "  (0, 11244)\t0.08262965296941757\n",
            "  (0, 9206)\t0.15186283580984414\n",
            "  (0, 1495)\t0.19712637387361423\n",
            "  (0, 7454)\t0.14745635785412262\n",
            "  (0, 7071)\t0.19822417598406614\n",
            "  (0, 5499)\t0.11454057510303811\n",
            "  (0, 3878)\t0.11998399582562203\n",
            "  (0, 11242)\t0.07277788238484746\n",
            "  (0, 15219)\t0.09800472886453934\n",
            "  :\t:\n",
            "  (4757, 3485)\t0.199161573117024\n",
            "  (4757, 1184)\t0.18890726729447022\n",
            "  (4757, 14568)\t0.24255077606762876\n",
            "  (4757, 15508)\t0.24255077606762876\n",
            "  (4757, 5802)\t0.24255077606762876\n",
            "  (4757, 819)\t0.27474840155297187\n",
            "  (4757, 14195)\t0.28805858134028367\n",
            "  (4757, 2227)\t0.28805858134028367\n",
            "  (4757, 7691)\t0.28805858134028367\n",
            "  (4757, 1932)\t0.28805858134028367\n",
            "  (4758, 5238)\t1.0\n",
            "  (4759, 10666)\t0.15888268987343043\n",
            "  (4759, 1490)\t0.21197258705292082\n",
            "  (4759, 15431)\t0.19628653185946862\n",
            "  (4759, 5690)\t0.19534291014627303\n",
            "  (4759, 14051)\t0.20084315377640435\n",
            "  (4759, 4358)\t0.18306542312175342\n",
            "  (4759, 10761)\t0.3126617295732147\n",
            "  (4759, 7130)\t0.26419662449963793\n",
            "  (4759, 3058)\t0.2812896191863103\n",
            "  (4759, 14062)\t0.3237911628497312\n",
            "  (4759, 8902)\t0.3040290704566037\n",
            "  (4759, 205)\t0.3237911628497312\n",
            "  (4759, 11708)\t0.33947721804318337\n",
            "  (4759, 11264)\t0.33947721804318337\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Get Similarity Score using Cosine Similarity**\n",
        "\n",
        "cosine_similarity computes the L2-normalized dot product of vectors. Euclidean (L2) normalization projects the vectors onto the unit sphere, and their dot product is then the cosine of the angle between the points denoted by vectors"
      ],
      "metadata": {
        "id": "P4KORYvM-aRM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "bJN9Xr36_7hs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Similarity_Score = cosine_similarity(x)"
      ],
      "metadata": {
        "id": "BIzpellYA89Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Similarity_Score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZQ5bZAqyCWYA",
        "outputId": "2e2a9701-cea0-4f9b-c912-c10cf409cb14"
      },
      "execution_count": null,
      "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": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Similarity_Score.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gpVsN-kkCbZl",
        "outputId": "d4336a34-e3d4-43a5-dc4e-d266dea7af3e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4760, 4760)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Get Movie Name as Input from User**"
      ],
      "metadata": {
        "id": "UkGPNcBQCzuL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Favourite_Movie_Name = input('Enter your favourite movie name: ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DLBfp-TjC8bS",
        "outputId": "5d448dc6-e641-41ec-cf35-70e11fee9daa"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter your favourite movie name: Avatar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "All_Movies_Title_List = df['Movie_Title'].tolist()"
      ],
      "metadata": {
        "id": "bh6cIHwHDXsC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import difflib"
      ],
      "metadata": {
        "id": "J5Un9YuJEQML"
      },
      "execution_count": null,
      "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": "KWjDXkWsF5eJ",
        "outputId": "f5e1c46d-d28a-4d5b-ec9a-a368eb0a5dfb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Avatar']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Close_Match = Movie_Recommendation[0]\n",
        "print(Close_Match)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "st9tvfLOoyHP",
        "outputId": "079ea16c-62f1-4528-c283-bf9380309a55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Avatar\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "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": "Fr75ZqZmp8v1",
        "outputId": "831d4951-28ec-4d85-eb9d-35db44671795"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2692\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting list of similar movies\n",
        "Recommendation_Score = list(enumerate(Similarity_Score[Index_of_Close_Match_Movie]))\n",
        "print(Recommendation_Score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "67Q6vD1XqmSc",
        "outputId": "ee0fea59-b51b-4732-a29b-90879c1584b0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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(4758, 0.0), (4759, 0.0)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(Recommendation_Score)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TkRjFgxMrGSY",
        "outputId": "e8287177-95fc-4c3d-d536-0327a996ba72"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4760"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Get all movies based on Recommendation Score**"
      ],
      "metadata": {
        "id": "lzb7mwMNrN64"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Sorting the movies based on Similarity Socre\n",
        "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": "Etwjx0_1r_GR",
        "outputId": "f27913a1-ba7c-4890-ef2a-18d8f0e28a2b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[(2692, 1.0), (3276, 0.11904275527845871), (3779, 0.10185805797079384), (62, 0.10153560702418994), (2903, 0.10063787314386036), (1647, 0.09397055536069451), (4614, 0.09362226751043301), (4375, 0.09117512301489195), (45, 0.08999324643162437), (1383, 0.08425242441722802), (110, 0.08361784775029485), (628, 0.08334515876919321), (1994, 0.08287835345252215), (2558, 0.08267633224298854), (1070, 0.08104448918225104), (4378, 0.07894345402700792), (1341, 0.0773269380936194), (1977, 0.07510309168081497), (3465, 0.07411089841255805), (3053, 0.0732438108456325), (4116, 0.07264153003988619), (1982, 0.07246569778553744), (2538, 0.06802035746289194), (3248, 0.06683400770968473), (3946, 0.06577120166835924), (3480, 0.06560363079666712), (254, 0.06351452702158421), (590, 0.06275727122098755), (3450, 0.06274272831079739), (1886, 0.06267985852941993), (4594, 0.0624699521049894), (2112, 0.06218435141221765), (84, 0.06182375996841291), (675, 0.061769915175723035), (3854, 0.061615662703783644), (1134, 0.06151448371353247), (3463, 0.06070604565602542), (4252, 0.0605981550841241), (4137, 0.060477037097691845), (1118, 0.05998954734066492), (4389, 0.059627372790876695), (3385, 0.05898328865604497), (4062, 0.05895899420588935), (282, 0.05879285017883315), (4398, 0.05848106495843603), (424, 0.05839654732699123), (2358, 0.05826769637272553), (3462, 0.057434079728437545), (2985, 0.05717355295839897), (2318, 0.05698746413620389), (1021, 0.05671999964768967), (3728, 0.05632573704373771), (3334, 0.05583492547104911), (1243, 0.05520158300072489), (1970, 0.05299390678023471), (2026, 0.05264268750759822), (4123, 0.05255262820554276), (137, 0.05248774397823476), (3360, 0.05160066999995516), (877, 0.0512230973907653), (292, 0.05110461872364793), (761, 0.05084432535535455), (873, 0.050648129131911857), (1030, 0.050582789674035046), (2191, 0.050557727650637894), (2944, 0.050454430002777675), (3655, 0.05037922104829879), (1148, 0.05022542698748971), (4503, 0.05019632180888005), (1102, 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          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Top 30 Movies Suggested to You : \\n')\n",
        "\n",
        "i = 1\n",
        "\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\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X6RM5KGErI01",
        "outputId": "dfb4a5b4-c054-4c34-88bc-fdf345bdda2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Top 30 Movies Suggested to You : \n",
            "\n",
            "1 . Niagara\n",
            "2 . Caravans\n",
            "3 . My Week with Marilyn\n",
            "4 . Brokeback Mountain\n",
            "5 . Harry Brown\n",
            "6 . Night of the Living Dead\n",
            "7 . The Curse of Downers Grove\n",
            "8 . The Boy Next Door\n",
            "9 . Back to the Future\n",
            "10 . The Juror\n",
            "11 . Some Like It Hot\n",
            "12 . Enough\n",
            "13 . The Kentucky Fried Movie\n",
            "14 . Eye for an Eye\n",
            "15 . Welcome to the Sticks\n",
            "16 . Alice Through the Looking Glass\n",
            "17 . Superman III\n",
            "18 . The Misfits\n",
            "19 . Premium Rush\n",
            "20 . Duel in the Sun\n",
            "21 . Sabotage\n",
            "22 . Small Soldiers\n",
            "23 . All That Jazz\n",
            "24 . Camping Sauvage\n",
            "25 . The Raid\n",
            "26 . Beyond the Black Rainbow\n",
            "27 . To Kill a Mockingbird\n",
            "28 . World Trade Center\n",
            "29 . The Dark Knight Rises\n",
            "30 . Tora! Tora! Tora!\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Top 10 Movie Recommendation System**"
      ],
      "metadata": {
        "id": "4n3j4QmCtO6d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Movie_Name = input('Enter your favourite movie name :')\n",
        "\n",
        "list_of_all_titles = df['Movie_Title'].tolist()\n",
        "Find_Close_Match = difflib.get_close_matches(Movie_Name, list_of_all_titles)\n",
        "Close_Match = Find_Close_Match[0]\n",
        "Index_of_Movie = df[df.Movie_Title == Close_Match]['Movie_ID'].values[0]\n",
        "Recommendation_Score = list(enumerate(Similarity_Score[Index_of_Movie]))\n",
        "sorted_similar_movies = sorted(Recommendation_Score, key = lambda x:x[1], reverse = True)\n",
        "\n",
        "print('Top 10 Movies Suggested to You : \\n')\n",
        "\n",
        "i = 1\n",
        "\n",
        "for movie in sorted_similar_movies:\n",
        "  index = movie[0]\n",
        "  title_from_index = df[df.index == index]['Movie_Title'].values\n",
        "  if(i<11):\n",
        "    print(i, '.', title_from_index)\n",
        "    i += 1\n",
        "\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LjWcExbItXXB",
        "outputId": "84bc265f-c4d7-4169-c9e3-c6abf4c2e594"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter your favourite movie name :Avatar\n",
            "Top 10 Movies Suggested to You : \n",
            "\n",
            "1 . ['Niagara']\n",
            "2 . ['Caravans']\n",
            "3 . ['My Week with Marilyn']\n",
            "4 . ['Brokeback Mountain']\n",
            "5 . ['Harry Brown']\n",
            "6 . ['Night of the Living Dead']\n",
            "7 . ['The Curse of Downers Grove']\n",
            "8 . ['The Boy Next Door']\n",
            "9 . ['Back to the Future']\n",
            "10 . ['The Juror']\n"
          ]
        }
      ]
    }
  ]
}