movies_genre_classification_multi_lebel_output.ipynb

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        "id": "jsv4spGxSinD"
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      "source": [
        "# Importing essential libraries\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the 'movie metada' dataset\n",
        "movie_metadata = pd.read_csv('/content/drive/MyDrive/NLP/movie_metadata.tsv',sep='\\t',header=None)"
      ],
      "metadata": {
        "id": "j46cgYv0TYf8"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading the 'plot summaries' dataset\n",
        "plot_summary = pd.read_csv('/content/drive/MyDrive/NLP/plot_summaries.tsv',sep='\\t',header=None)"
      ],
      "metadata": {
        "id": "TxjF-DdJTx59"
      },
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Explore the dataset\n",
        "movie_metadata.head()"
      ],
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          "height": 293
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        "id": "E8heQoFZUECQ",
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              "      <td>110.0</td>\n",
              "      <td>{\"/m/02h40lc\": \"English Language\"}</td>\n",
              "      <td>{\"/m/07ssc\": \"United Kingdom\"}</td>\n",
              "      <td>{\"/m/01jfsb\": \"Thriller\", \"/m/0glj9q\": \"Erotic...</td>\n",
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              "      <td>106.0</td>\n",
              "      <td>{\"/m/04306rv\": \"German Language\"}</td>\n",
              "      <td>{\"/m/0345h\": \"Germany\"}</td>\n",
              "      <td>{\"/m/07s9rl0\": \"Drama\"}</td>\n",
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              "          0  ...                                                  8\n",
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              "1   3196793  ...  {\"/m/02n4kr\": \"Mystery\", \"/m/03bxz7\": \"Biograp...\n",
              "2  28463795  ...  {\"/m/0lsxr\": \"Crime Fiction\", \"/m/07s9rl0\": \"D...\n",
              "3   9363483  ...  {\"/m/01jfsb\": \"Thriller\", \"/m/0glj9q\": \"Erotic...\n",
              "4    261236  ...                            {\"/m/07s9rl0\": \"Drama\"}\n",
              "\n",
              "[5 rows x 9 columns]"
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      "source": [
        "movie_metadata.shape"
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          "metadata": {},
          "execution_count": 5
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        "movie_metadata.dtypes"
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              "          0                                                  1\n",
              "0  23890098  Shlykov, a hard-working taxi driver and Lyosha...\n",
              "1  31186339  The nation of Panem consists of a wealthy Capi...\n",
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      "source": [
        "plot_summary.shape\n"
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      "source": [
        "movie_metadata.shape"
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        "id": "_USLGTObVLZY",
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              "(81741, 9)"
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          "execution_count": 11
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      "source": [
        "plot_summary.dtypes"
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        "id": "p6p2Jg8KVPcI",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "# data cleaning\n",
        "# Renaming the required columns\n",
        "movie_metadata.columns = ['movie_id', 1, 'movie_name', 3, 4, 5, 6, 7, 'genre']"
      ],
      "metadata": {
        "id": "iYp_Rsp8Vbm0"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movie_metadata.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
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        "id": "ypbL5EBkV1Iv",
        "outputId": "3156adb8-15d5-4cc6-b61a-816ffb70b05f"
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      "execution_count": 17,
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_id</th>\n",
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              "      <th>movie_name</th>\n",
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              "      <td>2001-08-24</td>\n",
              "      <td>14010832.0</td>\n",
              "      <td>98.0</td>\n",
              "      <td>{\"/m/02h40lc\": \"English Language\"}</td>\n",
              "      <td>{\"/m/09c7w0\": \"United States of America\"}</td>\n",
              "      <td>{\"/m/01jfsb\": \"Thriller\", \"/m/06n90\": \"Science...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>3196793</td>\n",
              "      <td>/m/08yl5d</td>\n",
              "      <td>Getting Away with Murder: The JonBenét Ramsey ...</td>\n",
              "      <td>2000-02-16</td>\n",
              "      <td>NaN</td>\n",
              "      <td>95.0</td>\n",
              "      <td>{\"/m/02h40lc\": \"English Language\"}</td>\n",
              "      <td>{\"/m/09c7w0\": \"United States of America\"}</td>\n",
              "      <td>{\"/m/02n4kr\": \"Mystery\", \"/m/03bxz7\": \"Biograp...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28463795</td>\n",
              "      <td>/m/0crgdbh</td>\n",
              "      <td>Brun bitter</td>\n",
              "      <td>1988</td>\n",
              "      <td>NaN</td>\n",
              "      <td>83.0</td>\n",
              "      <td>{\"/m/05f_3\": \"Norwegian Language\"}</td>\n",
              "      <td>{\"/m/05b4w\": \"Norway\"}</td>\n",
              "      <td>{\"/m/0lsxr\": \"Crime Fiction\", \"/m/07s9rl0\": \"D...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9363483</td>\n",
              "      <td>/m/0285_cd</td>\n",
              "      <td>White Of The Eye</td>\n",
              "      <td>1987</td>\n",
              "      <td>NaN</td>\n",
              "      <td>110.0</td>\n",
              "      <td>{\"/m/02h40lc\": \"English Language\"}</td>\n",
              "      <td>{\"/m/07ssc\": \"United Kingdom\"}</td>\n",
              "      <td>{\"/m/01jfsb\": \"Thriller\", \"/m/0glj9q\": \"Erotic...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>261236</td>\n",
              "      <td>/m/01mrr1</td>\n",
              "      <td>A Woman in Flames</td>\n",
              "      <td>1983</td>\n",
              "      <td>NaN</td>\n",
              "      <td>106.0</td>\n",
              "      <td>{\"/m/04306rv\": \"German Language\"}</td>\n",
              "      <td>{\"/m/0345h\": \"Germany\"}</td>\n",
              "      <td>{\"/m/07s9rl0\": \"Drama\"}</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movie_id  ...                                              genre\n",
              "0    975900  ...  {\"/m/01jfsb\": \"Thriller\", \"/m/06n90\": \"Science...\n",
              "1   3196793  ...  {\"/m/02n4kr\": \"Mystery\", \"/m/03bxz7\": \"Biograp...\n",
              "2  28463795  ...  {\"/m/0lsxr\": \"Crime Fiction\", \"/m/07s9rl0\": \"D...\n",
              "3   9363483  ...  {\"/m/01jfsb\": \"Thriller\", \"/m/0glj9q\": \"Erotic...\n",
              "4    261236  ...                            {\"/m/07s9rl0\": \"Drama\"}\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Renaming the required columns\n",
        "plot_summary.columns = ['movie_id', 'plot']"
      ],
      "metadata": {
        "id": "XzJED77fV8Ke"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_summary.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "SrHHQR12WSGI",
        "outputId": "38d11f1c-7bd1-46f6-c4dd-a84c7d3cf751"
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      "execution_count": 18,
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          "output_type": "execute_result",
          "data": {
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31186339</td>\n",
              "      <td>The nation of Panem consists of a wealthy Capi...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20663735</td>\n",
              "      <td>Poovalli Induchoodan  is sentenced for six yea...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2231378</td>\n",
              "      <td>The Lemon Drop Kid , a New York City swindler,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>595909</td>\n",
              "      <td>Seventh-day Adventist Church pastor Michael Ch...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movie_id                                               plot\n",
              "0  23890098  Shlykov, a hard-working taxi driver and Lyosha...\n",
              "1  31186339  The nation of Panem consists of a wealthy Capi...\n",
              "2  20663735  Poovalli Induchoodan  is sentenced for six yea...\n",
              "3   2231378  The Lemon Drop Kid , a New York City swindler,...\n",
              "4    595909  Seventh-day Adventist Church pastor Michael Ch..."
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Merging both dataframes\n",
        "df = pd.merge(movie_metadata[['movie_id', 'movie_name', 'genre']], plot_summary, on='movie_id')"
      ],
      "metadata": {
        "id": "Q5LtlCAuWYlw"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iygjwwBCWv-g",
        "outputId": "9c1af828-94f3-4950-aa5a-4633fe3b93c0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(42204, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WvFsjlhqW0Az",
        "outputId": "00a00cad-4e3e-4b17-89c7-bf0cccd48a7c"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>movie_id</th>\n",
              "      <th>movie_name</th>\n",
              "      <th>genre</th>\n",
              "      <th>plot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>975900</td>\n",
              "      <td>Ghosts of Mars</td>\n",
              "      <td>{\"/m/01jfsb\": \"Thriller\", \"/m/06n90\": \"Science...</td>\n",
              "      <td>Set in the second half of the 22nd century, th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9363483</td>\n",
              "      <td>White Of The Eye</td>\n",
              "      <td>{\"/m/01jfsb\": \"Thriller\", \"/m/0glj9q\": \"Erotic...</td>\n",
              "      <td>A series of murders of rich young women throug...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>261236</td>\n",
              "      <td>A Woman in Flames</td>\n",
              "      <td>{\"/m/07s9rl0\": \"Drama\"}</td>\n",
              "      <td>Eva, an upper class housewife, becomes frustra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18998739</td>\n",
              "      <td>The Sorcerer's Apprentice</td>\n",
              "      <td>{\"/m/0hqxf\": \"Family Film\", \"/m/01hmnh\": \"Fant...</td>\n",
              "      <td>Every hundred years, the evil Morgana  returns...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6631279</td>\n",
              "      <td>Little city</td>\n",
              "      <td>{\"/m/06cvj\": \"Romantic comedy\", \"/m/0hj3n0w\": ...</td>\n",
              "      <td>Adam, a San Francisco-based artist who works a...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movie_id  ...                                               plot\n",
              "0    975900  ...  Set in the second half of the 22nd century, th...\n",
              "1   9363483  ...  A series of murders of rich young women throug...\n",
              "2    261236  ...  Eva, an upper class housewife, becomes frustra...\n",
              "3  18998739  ...  Every hundred years, the evil Morgana  returns...\n",
              "4   6631279  ...  Adam, a San Francisco-based artist who works a...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cleaning the genre column\n",
        "df['genre'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "07rUyQi7XJgI",
        "outputId": "5d13e1cf-4d5c-4ed9-86f0-4c2de47ee90a"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'{\"/m/01jfsb\": \"Thriller\", \"/m/06n90\": \"Science Fiction\", \"/m/03npn\": \"Horror\", \"/m/03k9fj\": \"Adventure\", \"/m/0fdjb\": \"Supernatural\", \"/m/02kdv5l\": \"Action\", \"/m/09zvmj\": \"Space western\"}'"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import json\n",
        "json.loads(df['genre'][0]).values()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jVAnMnM8XZCz",
        "outputId": "7d8797c2-ca5a-4eda-a6bf-4c53560d0c38"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_values(['Thriller', 'Science Fiction', 'Horror', 'Adventure', 'Supernatural', 'Action', 'Space western'])"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['genre'] = df['genre'].apply(lambda x: list(json.loads(x).values()))\n"
      ],
      "metadata": {
        "id": "zQJjyBDVXlxa"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "sTRfNovWY-yT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "pvDek3CnYHXQ",
        "outputId": "2443e211-7a48-4856-e70b-5809802d2a2d"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>movie_id</th>\n",
              "      <th>movie_name</th>\n",
              "      <th>genre</th>\n",
              "      <th>plot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>975900</td>\n",
              "      <td>Ghosts of Mars</td>\n",
              "      <td>[Thriller, Science Fiction, Horror, Adventure,...</td>\n",
              "      <td>Set in the second half of the 22nd century, th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9363483</td>\n",
              "      <td>White Of The Eye</td>\n",
              "      <td>[Thriller, Erotic thriller, Psychological thri...</td>\n",
              "      <td>A series of murders of rich young women throug...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>261236</td>\n",
              "      <td>A Woman in Flames</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Eva, an upper class housewife, becomes frustra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18998739</td>\n",
              "      <td>The Sorcerer's Apprentice</td>\n",
              "      <td>[Family Film, Fantasy, Adventure, World cinema]</td>\n",
              "      <td>Every hundred years, the evil Morgana  returns...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6631279</td>\n",
              "      <td>Little city</td>\n",
              "      <td>[Romantic comedy, Ensemble Film, Comedy-drama,...</td>\n",
              "      <td>Adam, a San Francisco-based artist who works a...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movie_id  ...                                               plot\n",
              "0    975900  ...  Set in the second half of the 22nd century, th...\n",
              "1   9363483  ...  A series of murders of rich young women throug...\n",
              "2    261236  ...  Eva, an upper class housewife, becomes frustra...\n",
              "3  18998739  ...  Every hundred years, the evil Morgana  returns...\n",
              "4   6631279  ...  Adam, a San Francisco-based artist who works a...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qhw1C0ZcZB_d",
        "outputId": "503ebf52-1c8d-4cb3-9352-863fb5905cd8"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41793, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Remove rows with 0 genre tags\n",
        "print('Before removing the rows: {}'.format(df.shape))\n",
        "df = df[df['genre'].apply(lambda x: False if len(x) == 0 else True)]\n",
        "print('After removing the rows: {}'.format(df.shape))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_C6sJhKKYOpj",
        "outputId": "69233940-1aac-404c-cb4e-e0de06820d99"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before removing the rows: (42204, 4)\n",
            "After removing the rows: (41793, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization\n",
        "Different types of genre"
      ],
      "metadata": {
        "id": "1hmIMSx2ZM8n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing essential libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "MVwUEioGZQr_"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape[0]\n",
        "df.iloc[0, 2]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8viDUKeuaGRe",
        "outputId": "6551d4aa-a524-42ca-c0af-f77007185593"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['Thriller',\n",
              " 'Science Fiction',\n",
              " 'Horror',\n",
              " 'Adventure',\n",
              " 'Supernatural',\n",
              " 'Action',\n",
              " 'Space western']"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "a1Na7iEFad9c",
        "outputId": "b2f99e54-2dc9-45f7-b010-ef672bb27cdd"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>movie_id</th>\n",
              "      <th>movie_name</th>\n",
              "      <th>genre</th>\n",
              "      <th>plot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>975900</td>\n",
              "      <td>Ghosts of Mars</td>\n",
              "      <td>[Thriller, Science Fiction, Horror, Adventure,...</td>\n",
              "      <td>Set in the second half of the 22nd century, th...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9363483</td>\n",
              "      <td>White Of The Eye</td>\n",
              "      <td>[Thriller, Erotic thriller, Psychological thri...</td>\n",
              "      <td>A series of murders of rich young women throug...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>261236</td>\n",
              "      <td>A Woman in Flames</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Eva, an upper class housewife, becomes frustra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18998739</td>\n",
              "      <td>The Sorcerer's Apprentice</td>\n",
              "      <td>[Family Film, Fantasy, Adventure, World cinema]</td>\n",
              "      <td>Every hundred years, the evil Morgana  returns...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6631279</td>\n",
              "      <td>Little city</td>\n",
              "      <td>[Romantic comedy, Ensemble Film, Comedy-drama,...</td>\n",
              "      <td>Adam, a San Francisco-based artist who works a...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movie_id  ...                                               plot\n",
              "0    975900  ...  Set in the second half of the 22nd century, th...\n",
              "1   9363483  ...  A series of murders of rich young women throug...\n",
              "2    261236  ...  Eva, an upper class housewife, becomes frustra...\n",
              "3  18998739  ...  Every hundred years, the evil Morgana  returns...\n",
              "4   6631279  ...  Adam, a San Francisco-based artist who works a...\n",
              "\n",
              "[5 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculatin unique genre\n",
        "all_genres = list()\n",
        "temp = list()\n",
        "\n",
        "def total_unique_genre(x):\n",
        "    global all_genre, temp\n",
        "    x = x.reset_index()\n",
        "    for i in range(0, x.shape[0]):\n",
        "        temp = temp + x.iloc[i, 1]\n",
        "    all_genre = set(temp)\n",
        "    return len(all_genre)\n",
        "\n",
        "print('Total number of unique genres are: {}'.format(total_unique_genre(df['genre'])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r7Hx0qrdZuax",
        "outputId": "cdb3a7e6-bcae-479e-9633-53809e5e8109"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total number of unique genres are: 363\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['genre'].reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "9W74-eGVb3Ev",
        "outputId": "6467ba0c-e586-4c8d-a0ee-733340b648ee"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>index</th>\n",
              "      <th>genre</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>[Thriller, Science Fiction, Horror, Adventure,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>[Thriller, Erotic thriller, Psychological thri...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>[Drama]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3</td>\n",
              "      <td>[Family Film, Fantasy, Adventure, World cinema]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>4</td>\n",
              "      <td>[Romantic comedy, Ensemble Film, Comedy-drama,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41788</th>\n",
              "      <td>42199</td>\n",
              "      <td>[Crime Fiction, Thriller, Comedy, Supernatural]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41789</th>\n",
              "      <td>42200</td>\n",
              "      <td>[Drama]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41790</th>\n",
              "      <td>42201</td>\n",
              "      <td>[Biographical film, Drama, Documentary]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41791</th>\n",
              "      <td>42202</td>\n",
              "      <td>[Science Fiction, Japanese Movies, Adventure, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41792</th>\n",
              "      <td>42203</td>\n",
              "      <td>[Thriller, Horror, Slasher, Teen]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41793 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       index                                              genre\n",
              "0          0  [Thriller, Science Fiction, Horror, Adventure,...\n",
              "1          1  [Thriller, Erotic thriller, Psychological thri...\n",
              "2          2                                            [Drama]\n",
              "3          3    [Family Film, Fantasy, Adventure, World cinema]\n",
              "4          4  [Romantic comedy, Ensemble Film, Comedy-drama,...\n",
              "...      ...                                                ...\n",
              "41788  42199    [Crime Fiction, Thriller, Comedy, Supernatural]\n",
              "41789  42200                                            [Drama]\n",
              "41790  42201            [Biographical film, Drama, Documentary]\n",
              "41791  42202  [Science Fiction, Japanese Movies, Adventure, ...\n",
              "41792  42203                  [Thriller, Horror, Slasher, Teen]\n",
              "\n",
              "[41793 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['genre']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luRQot_Ec6D6",
        "outputId": "310b1aa0-8085-46b9-f218-1e69c5f480e1"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        [Thriller, Science Fiction, Horror, Adventure,...\n",
              "1        [Thriller, Erotic thriller, Psychological thri...\n",
              "2                                                  [Drama]\n",
              "3          [Family Film, Fantasy, Adventure, World cinema]\n",
              "4        [Romantic comedy, Ensemble Film, Comedy-drama,...\n",
              "                               ...                        \n",
              "42199      [Crime Fiction, Thriller, Comedy, Supernatural]\n",
              "42200                                              [Drama]\n",
              "42201              [Biographical film, Drama, Documentary]\n",
              "42202    [Science Fiction, Japanese Movies, Adventure, ...\n",
              "42203                    [Thriller, Horror, Slasher, Teen]\n",
              "Name: genre, Length: 41793, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Using FreDist to claculate the frequency of all the genre in the dataset\n",
        "from nltk.probability import FreqDist\n",
        "temp_with_count = FreqDist(temp)\n",
        "\n",
        "# Creating a dataframe of genre_count\n",
        "df_genre_count = pd.DataFrame({'Genre': list(temp_with_count.keys()),\n",
        "                               'Count': list(temp_with_count.values())})\n",
        "df_genre_count.head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cejTQ57adGij",
        "outputId": "42e04bc3-2a23-4bbf-facc-92e9bfb1d2a6"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of                                Genre  Count\n",
              "0                           Thriller   6530\n",
              "1                    Science Fiction   2339\n",
              "2                             Horror   4082\n",
              "3                          Adventure   3248\n",
              "4                       Supernatural    571\n",
              "..                               ...    ...\n",
              "358                 New Queer Cinema      1\n",
              "359                       Neorealism      1\n",
              "360  The Netherlands in World War II      2\n",
              "361           Revisionist Fairy Tale      2\n",
              "362                    Homoeroticism      1\n",
              "\n",
              "[363 rows x 2 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_genre_count.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6kfQhqoReHaV",
        "outputId": "f0bff2cf-0698-408b-83f6-2d9d914b24a7"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(363, 2)"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_genre_count.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XZ8Lr_4_eMKs",
        "outputId": "c7c00282-d5a9-445a-9faa-95b82df5dd64"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Genre    object\n",
              "Count     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_genre_count"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "r5WD_-hheQ8E",
        "outputId": "93734b04-f984-440b-e8df-71161e6fc6cb"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Genre</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thriller</td>\n",
              "      <td>6530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Science Fiction</td>\n",
              "      <td>2339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Horror</td>\n",
              "      <td>4082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adventure</td>\n",
              "      <td>3248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Supernatural</td>\n",
              "      <td>571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>358</th>\n",
              "      <td>New Queer Cinema</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>359</th>\n",
              "      <td>Neorealism</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>360</th>\n",
              "      <td>The Netherlands in World War II</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>361</th>\n",
              "      <td>Revisionist Fairy Tale</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>362</th>\n",
              "      <td>Homoeroticism</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>363 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                               Genre  Count\n",
              "0                           Thriller   6530\n",
              "1                    Science Fiction   2339\n",
              "2                             Horror   4082\n",
              "3                          Adventure   3248\n",
              "4                       Supernatural    571\n",
              "..                               ...    ...\n",
              "358                 New Queer Cinema      1\n",
              "359                       Neorealism      1\n",
              "360  The Netherlands in World War II      2\n",
              "361           Revisionist Fairy Tale      2\n",
              "362                    Homoeroticism      1\n",
              "\n",
              "[363 rows x 2 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the top 50 genres\n",
        "genre_top_50 = df_genre_count.sort_values(by=['Count'], ascending=False).iloc[0:50, :]\n",
        "genre_top_50"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6n0mdR9jeXX2",
        "outputId": "ec28ad01-6278-49fd-a5fd-62916a82fdc3"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>Genre</th>\n",
              "      <th>Count</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Drama</td>\n",
              "      <td>19134</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Comedy</td>\n",
              "      <td>10467</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Romance Film</td>\n",
              "      <td>6666</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Thriller</td>\n",
              "      <td>6530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Action</td>\n",
              "      <td>5868</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>World cinema</td>\n",
              "      <td>5153</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>Crime Fiction</td>\n",
              "      <td>4275</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Horror</td>\n",
              "      <td>4082</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>Black-and-white</td>\n",
              "      <td>3731</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>Indie</td>\n",
              "      <td>3668</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>Action/Adventure</td>\n",
              "      <td>3553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Adventure</td>\n",
              "      <td>3248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Family Film</td>\n",
              "      <td>3219</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>Short Film</td>\n",
              "      <td>3192</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Romantic drama</td>\n",
              "      <td>2572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>Animation</td>\n",
              "      <td>2441</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>Musical</td>\n",
              "      <td>2414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Science Fiction</td>\n",
              "      <td>2339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>Mystery</td>\n",
              "      <td>2119</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Romantic comedy</td>\n",
              "      <td>2075</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Fantasy</td>\n",
              "      <td>2012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>Comedy film</td>\n",
              "      <td>1778</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>Crime Thriller</td>\n",
              "      <td>1682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>War film</td>\n",
              "      <td>1556</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Period piece</td>\n",
              "      <td>1321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>Japanese Movies</td>\n",
              "      <td>1290</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Comedy-drama</td>\n",
              "      <td>1261</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>Film adaptation</td>\n",
              "      <td>1225</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>Documentary</td>\n",
              "      <td>1213</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>Silent film</td>\n",
              "      <td>1199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Psychological thriller</td>\n",
              "      <td>1138</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>32</th>\n",
              "      <td>Bollywood</td>\n",
              "      <td>1058</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>56</th>\n",
              "      <td>Western</td>\n",
              "      <td>1022</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>108</th>\n",
              "      <td>Chinese Movies</td>\n",
              "      <td>998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>Black comedy</td>\n",
              "      <td>834</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>94</th>\n",
              "      <td>LGBT</td>\n",
              "      <td>825</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>95</th>\n",
              "      <td>Teen</td>\n",
              "      <td>824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>109</th>\n",
              "      <td>Parody</td>\n",
              "      <td>767</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>Family Drama</td>\n",
              "      <td>766</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>Children's/Family</td>\n",
              "      <td>759</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>31</th>\n",
              "      <td>Coming of age</td>\n",
              "      <td>741</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>Martial Arts Film</td>\n",
              "      <td>706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>Cult</td>\n",
              "      <td>680</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>Sports</td>\n",
              "      <td>672</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>Television movie</td>\n",
              "      <td>665</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>68</th>\n",
              "      <td>Slasher</td>\n",
              "      <td>654</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>Suspense</td>\n",
              "      <td>651</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>41</th>\n",
              "      <td>Biographical film</td>\n",
              "      <td>625</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46</th>\n",
              "      <td>Biography</td>\n",
              "      <td>592</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Supernatural</td>\n",
              "      <td>571</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                      Genre  Count\n",
              "9                     Drama  19134\n",
              "16                   Comedy  10467\n",
              "18             Romance Film   6666\n",
              "0                  Thriller   6530\n",
              "5                    Action   5868\n",
              "12             World cinema   5153\n",
              "30            Crime Fiction   4275\n",
              "2                    Horror   4082\n",
              "43          Black-and-white   3731\n",
              "29                    Indie   3668\n",
              "35         Action/Adventure   3553\n",
              "3                 Adventure   3248\n",
              "10              Family Film   3219\n",
              "25               Short Film   3192\n",
              "17           Romantic drama   2572\n",
              "24                Animation   2441\n",
              "27                  Musical   2414\n",
              "1           Science Fiction   2339\n",
              "59                  Mystery   2119\n",
              "13          Romantic comedy   2075\n",
              "11                  Fantasy   2012\n",
              "67              Comedy film   1778\n",
              "60           Crime Thriller   1682\n",
              "20                 War film   1556\n",
              "22             Period piece   1321\n",
              "65          Japanese Movies   1290\n",
              "15             Comedy-drama   1261\n",
              "23          Film adaptation   1225\n",
              "47              Documentary   1213\n",
              "117             Silent film   1199\n",
              "8    Psychological thriller   1138\n",
              "32                Bollywood   1058\n",
              "56                  Western   1022\n",
              "108          Chinese Movies    998\n",
              "34             Black comedy    834\n",
              "94                     LGBT    825\n",
              "95                     Teen    824\n",
              "109                  Parody    767\n",
              "40             Family Drama    766\n",
              "26        Children's/Family    759\n",
              "31            Coming of age    741\n",
              "52        Martial Arts Film    706\n",
              "36                     Cult    680\n",
              "39                   Sports    672\n",
              "49         Television movie    665\n",
              "68                  Slasher    654\n",
              "62                 Suspense    651\n",
              "41        Biographical film    625\n",
              "46                Biography    592\n",
              "4              Supernatural    571"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(7, 15))\n",
        "sns.barplot(x='Count', y='Genre', data=genre_top_50)\n",
        "plt.xlabel('Frequency Count')\n",
        "plt.ylabel('Genre')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "0d3xId5he4Eq",
        "outputId": "11649f1e-5e9e-4886-b440-57c8443ae363"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Genre')"
            ]
          },
          "metadata": {},
          "execution_count": 51
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Preprocessing\n",
        "\n",
        "\n",
        "*   Clean plot summary\n",
        "*   Remove stopwords\n",
        "*   Stem plot summary\n",
        "*   Convert Genre into Target variables using MultiLabelBinaries\n",
        "*   Split dataset into train and test set\n",
        "*   Create features from coupus using TfidfVectorizer\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "hFkCWtoJmDLh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Importing essential libraries\n",
        "import re\n",
        "import nltk\n",
        "nltk.download('stopwords')\n",
        "from nltk.corpus import stopwords\n",
        "from nltk.stem import PorterStemmer"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wT2BL7pVmH8d",
        "outputId": "d2dfa9cc-6811-4256-d59a-110eec83b9d6"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Unzipping corpora/stopwords.zip.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def plot_clean(text):\n",
        "    text = re.sub(pattern='[^a-zA-Z]', repl=' ', string=text)\n",
        "    text = text.lower()\n",
        "    text = \" \".join(text.split())\n",
        "    return text"
      ],
      "metadata": {
        "id": "DCqhqda5nHwM"
      },
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plot_clean(df['plot'][0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "bFsYvVaVns8G",
        "outputId": "af0d5856-0701-4348-9e79-112666faa163"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'set in the second half of the nd century the film depicts mars as a planet that has been terraformed allowing humans to walk on the surface without wearing pressure suits the martian society has become largely matriarchal with women in most positions of authority the story concerns a police officer melanie ballard second in command of a small team alongside sergeant jericho sent to pick up and transport a prisoner named desolation williams arriving at the remote mining town where williams is being held ballard finds virtually all of the people missing she learns that the miners had discovered an underground doorway created by an ancient martian civilization when the door was opened it released ghosts disembodied spirits which possessed the miners violence ensues as the possessed miners commit horrific acts of death and destruction as well as self mutilation with their team leader helena bradock murdered ballard must fight off the attacking miners escape the town and destroy the ghosts if possible unfortunately her intentions are complicated by the fact that killing a possessed human merely releases the martian spirit to possess another human the team eventually decides to blow up a nuclear reactor to try and vaporize all of the ghosts at several points in the film sergeant jericho shows a romantic interest in ballard mostly unreciprocated ballard s crew along with survivors who manage to gather in the jail are eventually wiped out by the miners after many fierce battles and events leaving only her and williams after sergeant jericho and the other remaining officers and the two operators of the train are killed upon returning from a brief retreat to finish the fight not wanting the authorities to blame the massacre on him he handcuffs ballard to her cot and escapes from the train leaving her to return home and deliver her report which is received with skepticism by her superiors while ballard recuperates at a hospital the released spirits who weren t destroyed after all attack the city the end scene sets the movie up for a sequel as williams returns to team up with ballard to fight the possessed'"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['plot'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "Nw5a0YOwoGir",
        "outputId": "1fa5674a-66e6-4233-8b4f-96ddb289f917"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Set in the second half of the 22nd century, the film depicts Mars as a planet that has been 84% terraformed, allowing humans to walk on the surface without wearing pressure suits. The Martian society has become largely matriarchal, with women in most positions of authority. The story concerns a police officer, Melanie Ballard , second in command of a small team alongside Sergeant Jericho  sent to pick up and transport a prisoner named Desolation Williams . Arriving at the remote mining town where Williams is being held, Ballard finds virtually all of the people missing. She learns that the miners had discovered an underground doorway created by an ancient Martian civilization. When the door was opened it released \"ghosts,\" disembodied spirits which possessed the miners. Violence ensues, as the possessed miners commit horrific acts of death and destruction, as well as self-mutilation. With their team leader Helena Bradock  murdered, Ballard must fight off the attacking miners, escape the town, and destroy the ghosts, if possible. Unfortunately, her intentions are complicated by the fact that killing a possessed human merely releases the Martian spirit to possess another human. The team eventually decides to blow up a nuclear reactor to try and vaporize all of the ghosts. At several points in the film Sergeant Jericho shows a romantic interest in Ballard, mostly unreciprocated. Ballard\\'s crew along with survivors who manage to gather in the jail are eventually wiped out by the miners after many fierce battles and events , leaving only her and Williams after Sergeant Jericho and the other remaining officers and the two operators of the train are killed upon returning from a brief retreat to finish the fight. Not wanting the authorities to blame the massacre on him, he handcuffs Ballard to her cot and escapes from the train, leaving her to return home and deliver her report, which is received with skepticism by her superiors. While Ballard recuperates at a hospital, the released spirits, who weren\\'t destroyed after all, attack the city. The end scene sets the movie up for a sequel as Williams returns to team up with Ballard to fight the possessed.'"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['clean_plot'] = df['plot'].apply(lambda x: plot_clean(x))"
      ],
      "metadata": {
        "id": "iNyWPNNmnfHS"
      },
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 597
        },
        "id": "Q9na8eEGmwYi",
        "outputId": "ca8a66ab-c69c-48d4-cae7-4c4b0263d946"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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>movie_id</th>\n",
              "      <th>movie_name</th>\n",
              "      <th>genre</th>\n",
              "      <th>plot</th>\n",
              "      <th>clean_plot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>975900</td>\n",
              "      <td>Ghosts of Mars</td>\n",
              "      <td>[Thriller, Science Fiction, Horror, Adventure,...</td>\n",
              "      <td>Set in the second half of the 22nd century, th...</td>\n",
              "      <td>set in the second half of the nd century the f...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9363483</td>\n",
              "      <td>White Of The Eye</td>\n",
              "      <td>[Thriller, Erotic thriller, Psychological thri...</td>\n",
              "      <td>A series of murders of rich young women throug...</td>\n",
              "      <td>a series of murders of rich young women throug...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>261236</td>\n",
              "      <td>A Woman in Flames</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Eva, an upper class housewife, becomes frustra...</td>\n",
              "      <td>eva an upper class housewife becomes frustrate...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18998739</td>\n",
              "      <td>The Sorcerer's Apprentice</td>\n",
              "      <td>[Family Film, Fantasy, Adventure, World cinema]</td>\n",
              "      <td>Every hundred years, the evil Morgana  returns...</td>\n",
              "      <td>every hundred years the evil morgana returns t...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6631279</td>\n",
              "      <td>Little city</td>\n",
              "      <td>[Romantic comedy, Ensemble Film, Comedy-drama,...</td>\n",
              "      <td>Adam, a San Francisco-based artist who works a...</td>\n",
              "      <td>adam a san francisco based artist who works as...</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>42199</th>\n",
              "      <td>23851782</td>\n",
              "      <td>The Ghost Train</td>\n",
              "      <td>[Crime Fiction, Thriller, Comedy, Supernatural]</td>\n",
              "      <td>{{plot}} The film opens with a Great Western e...</td>\n",
              "      <td>plot the film opens with a great western expre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42200</th>\n",
              "      <td>35228177</td>\n",
              "      <td>Mermaids: The Body Found</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Two former National Oceanic Atmospheric Admini...</td>\n",
              "      <td>two former national oceanic atmospheric admini...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42201</th>\n",
              "      <td>34980460</td>\n",
              "      <td>Knuckle</td>\n",
              "      <td>[Biographical film, Drama, Documentary]</td>\n",
              "      <td>{{No plot}} This film follows 12 years in the ...</td>\n",
              "      <td>no plot this film follows years in the lives o...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42202</th>\n",
              "      <td>913762</td>\n",
              "      <td>The Super Dimension Fortress Macross II: Lover...</td>\n",
              "      <td>[Science Fiction, Japanese Movies, Adventure, ...</td>\n",
              "      <td>The story takes place in the year 2092,The Sup...</td>\n",
              "      <td>the story takes place in the year the super di...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42203</th>\n",
              "      <td>12476867</td>\n",
              "      <td>Spliced</td>\n",
              "      <td>[Thriller, Horror, Slasher, Teen]</td>\n",
              "      <td>The movie is about a teenage girl who loves ho...</td>\n",
              "      <td>the movie is about a teenage girl who loves ho...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>41793 rows × 5 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       movie_id  ...                                         clean_plot\n",
              "0        975900  ...  set in the second half of the nd century the f...\n",
              "1       9363483  ...  a series of murders of rich young women throug...\n",
              "2        261236  ...  eva an upper class housewife becomes frustrate...\n",
              "3      18998739  ...  every hundred years the evil morgana returns t...\n",
              "4       6631279  ...  adam a san francisco based artist who works as...\n",
              "...         ...  ...                                                ...\n",
              "42199  23851782  ...  plot the film opens with a great western expre...\n",
              "42200  35228177  ...  two former national oceanic atmospheric admini...\n",
              "42201  34980460  ...  no plot this film follows years in the lives o...\n",
              "42202    913762  ...  the story takes place in the year the super di...\n",
              "42203  12476867  ...  the movie is about a teenage girl who loves ho...\n",
              "\n",
              "[41793 rows x 5 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[['plot', 'clean_plot']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "khs8TOQSosSM",
        "outputId": "618cafac-e5b3-4c76-b210-f3c192dc95a9"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>plot</th>\n",
              "      <th>clean_plot</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>Set in the second half of the 22nd century, th...</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A series of murders of rich young women throug...</td>\n",
              "      <td>a series of murders of rich young women throug...</td>\n",
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              "      <th>2</th>\n",
              "      <td>Eva, an upper class housewife, becomes frustra...</td>\n",
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              "      <th>3</th>\n",
              "      <td>Every hundred years, the evil Morgana  returns...</td>\n",
              "      <td>every hundred years the evil morgana returns t...</td>\n",
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              "      <th>4</th>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                plot                                         clean_plot\n",
              "0  Set in the second half of the 22nd century, th...  set in the second half of the nd century the f...\n",
              "1  A series of murders of rich young women throug...  a series of murders of rich young women throug...\n",
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              "3  Every hundred years, the evil Morgana  returns...  every hundred years the evil morgana returns t...\n",
              "4  Adam, a San Francisco-based artist who works a...  adam a san francisco based artist who works as..."
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Removing the stopwords from 'clean_plot'\n",
        "stopwords_set = set(stopwords.words('english'))\n",
        "\n",
        "def plot_stopwords(text):\n",
        "    words = text.split()\n",
        "    words = [w for w in words if not w in stopwords_set]\n",
        "    text = ' '.join(words)\n",
        "    return text"
      ],
      "metadata": {
        "id": "dyQ8YZYTozZt"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['clean_noStopword_plot'] = df['clean_plot'].apply(lambda x: plot_stopwords(x))"
      ],
      "metadata": {
        "id": "tSED1F9_pUJC"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[['clean_plot', 'clean_noStopword_plot']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZjikptJ9pp8n",
        "outputId": "3ec3739a-ec00-4695-8d1b-0427a6de9e2a"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    }\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clean_plot</th>\n",
              "      <th>clean_noStopword_plot</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>set in the second half of the nd century the f...</td>\n",
              "      <td>set second half nd century film depicts mars p...</td>\n",
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              "      <td>a series of murders of rich young women throug...</td>\n",
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              "      <td>eva an upper class housewife becomes frustrate...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>every hundred years the evil morgana returns t...</td>\n",
              "      <td>every hundred years evil morgana returns claim...</td>\n",
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              "      <th>4</th>\n",
              "      <td>adam a san francisco based artist who works as...</td>\n",
              "      <td>adam san francisco based artist works cab driv...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          clean_plot                              clean_noStopword_plot\n",
              "0  set in the second half of the nd century the f...  set second half nd century film depicts mars p...\n",
              "1  a series of murders of rich young women throug...  series murders rich young women throughout ari...\n",
              "2  eva an upper class housewife becomes frustrate...  eva upper class housewife becomes frustrated l...\n",
              "3  every hundred years the evil morgana returns t...  every hundred years evil morgana returns claim...\n",
              "4  adam a san francisco based artist who works as...  adam san francisco based artist works cab driv..."
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Stemming the words from 'clean_noStopwords_plot'\n",
        "ps = PorterStemmer()\n",
        "\n",
        "def plot_stemming(text):\n",
        "    words = text.split()\n",
        "    words = [ps.stem(w) for w in words]\n",
        "    text = ' '.join(words)\n",
        "    return text"
      ],
      "metadata": {
        "id": "S0-kkt8ap0kT"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['clean_noStopword_stem_plot'] = df['clean_noStopword_plot'].apply(lambda x: plot_stemming(x))"
      ],
      "metadata": {
        "id": "3xDtjnIHqLww"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[['clean_noStopword_plot', 'clean_noStopword_stem_plot']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nrlsyRw2qbgA",
        "outputId": "72d835df-945e-46cd-e568-67df13de7edf"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>clean_noStopword_plot</th>\n",
              "      <th>clean_noStopword_stem_plot</th>\n",
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              "      <th>1</th>\n",
              "      <td>series murders rich young women throughout ari...</td>\n",
              "      <td>seri murder rich young women throughout arizon...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>eva upper class housewife becomes frustrated l...</td>\n",
              "      <td>eva upper class housewif becom frustrat leav a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>every hundred years evil morgana returns claim...</td>\n",
              "      <td>everi hundr year evil morgana return claim fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>adam san francisco based artist works cab driv...</td>\n",
              "      <td>adam san francisco base artist work cab driver...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                               clean_noStopword_plot                         clean_noStopword_stem_plot\n",
              "0  set second half nd century film depicts mars p...  set second half nd centuri film depict mar pla...\n",
              "1  series murders rich young women throughout ari...  seri murder rich young women throughout arizon...\n",
              "2  eva upper class housewife becomes frustrated l...  eva upper class housewif becom frustrat leav a...\n",
              "3  every hundred years evil morgana returns claim...  everi hundr year evil morgana return claim fin...\n",
              "4  adam san francisco based artist works cab driv...  adam san francisco base artist work cab driver..."
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert Genre into Target variables using MultiLabelBinaries\n",
        "from sklearn.preprocessing import MultiLabelBinarizer\n",
        "mlb = MultiLabelBinarizer()\n",
        "y = mlb.fit_transform(df['genre'])"
      ],
      "metadata": {
        "id": "8LEnTKKYs7om"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['genre']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FwOTNRdGuCG_",
        "outputId": "1640966b-6ca1-4aa9-b766-fb230f56ed03"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        [Thriller, Science Fiction, Horror, Adventure,...\n",
              "1        [Thriller, Erotic thriller, Psychological thri...\n",
              "2                                                  [Drama]\n",
              "3          [Family Film, Fantasy, Adventure, World cinema]\n",
              "4        [Romantic comedy, Ensemble Film, Comedy-drama,...\n",
              "                               ...                        \n",
              "42199      [Crime Fiction, Thriller, Comedy, Supernatural]\n",
              "42200                                              [Drama]\n",
              "42201              [Biographical film, Drama, Documentary]\n",
              "42202    [Science Fiction, Japanese Movies, Adventure, ...\n",
              "42203                    [Thriller, Horror, Slasher, Teen]\n",
              "Name: genre, Length: 41793, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ruQ3-dRtw_9",
        "outputId": "3ade7234-c8cf-4cba-8acd-29c69d2e16b5"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41793, 363)"
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y[0].size"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frVIfG7Tt49P",
        "outputId": "532d8678-df71-4f41-f188-e96feb48a28d"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "363"
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df['clean_noStopword_stem_plot']"
      ],
      "metadata": {
        "id": "knhXh-svtXvk"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PQN3LHLouPct",
        "outputId": "8b76e0cd-6300-4fd2-9272-83de80ea4a0f"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        set second half nd centuri film depict mar pla...\n",
              "1        seri murder rich young women throughout arizon...\n",
              "2        eva upper class housewif becom frustrat leav a...\n",
              "3        everi hundr year evil morgana return claim fin...\n",
              "4        adam san francisco base artist work cab driver...\n",
              "                               ...                        \n",
              "42199    plot film open great western express speed box...\n",
              "42200    two former nation ocean atmospher administr sc...\n",
              "42201    plot film follow year live irish travel famili...\n",
              "42202    stori take place year super dimens fortress ma...\n",
              "42203    movi teenag girl love horror movi watch time y...\n",
              "Name: clean_noStopword_stem_plot, Length: 41793, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset into train and test set\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, random_state=0)"
      ],
      "metadata": {
        "id": "mPvRF2pRtd32"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train, \"====\", y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yjM4hZkIudky",
        "outputId": "2b4e5c01-6420-4b1e-ebb3-36799250abcf"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "22757    plot prepar leav forest moon endor towani fami...\n",
            "24391    threesom lend reason titl aravindan chandu muk...\n",
            "3093     vertov start show us intertitl giant cyril cha...\n",
            "33170    plot veterinari assist corki romano banish fam...\n",
            "27533    expand section jim fleg book thief forger sell...\n",
            "                               ...                        \n",
            "20961    young lotu flower see unconsci man float water...\n",
            "32426    director produc ivan kral wave filmmak amo poe...\n",
            "30704    man recent transplant heart lead frantic searc...\n",
            "21448    one anoth compani carrier pigeon fall prey sed...\n",
            "2759     famili relat govern land impertin landown inco...\n",
            "Name: clean_noStopword_stem_plot, Length: 33434, dtype: object ==== [[0 0 1 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating independent features using TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "tfidf = TfidfVectorizer(max_df=0.8, max_features=10000)\n",
        "X_train = tfidf.fit_transform(X_train)\n",
        "X_test = tfidf.transform(X_test)"
      ],
      "metadata": {
        "id": "x2JSigCIvAh9"
      },
      "execution_count": 82,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Model Building**"
      ],
      "metadata": {
        "id": "B5PfVCGqvnvF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Logistic Regression Model\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.multiclass import OneVsRestClassifier\n"
      ],
      "metadata": {
        "id": "v3JVlewwvstj"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "lr = LogisticRegression()\n",
        "ovr_clf = OneVsRestClassifier(lr)"
      ],
      "metadata": {
        "id": "ebpWxMmyv9XY"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit model on train data\n",
        "ovr_clf.fit(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RHcR-c4nwGGf",
        "outputId": "1eae60fb-7688-4613-b92e-d6441d1755f9"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/multiclass.py:80: UserWarning: Label not 48 is present in all training examples.\n",
            "  \"Label %s is present in all training examples.\" % str(classes[c])\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/linear_model/_logistic.py:818: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
            "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
            "\n",
            "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
            "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
            "Please also refer to the documentation for alternative solver options:\n",
            "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
            "  extra_warning_msg=_LOGISTIC_SOLVER_CONVERGENCE_MSG,\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/multiclass.py:80: UserWarning: Label not 243 is present in all training examples.\n",
            "  \"Label %s is present in all training examples.\" % str(classes[c])\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OneVsRestClassifier(estimator=LogisticRegression())"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Predicting values for test set\n",
        "y_pred_ovr = ovr_clf.predict(X_test)\n",
        "y_pred_ovr[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KfCzKjKkwv7t",
        "outputId": "35f00110-3521-43c2-d8f8-fcd1dcb9c846"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "       0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
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              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "mlb.inverse_transform(y_pred_ovr)[5]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2IRciMG1xBQ8",
        "outputId": "f97522ec-c435-4dcb-96e2-e7559eef3c26"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "('Drama',)"
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Model Evaluation\n",
        "from sklearn.metrics import f1_score\n",
        "print(f1_score(y_test, y_pred_ovr, average='micro'))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZaQS-qxXxTuJ",
        "outputId": "b194764b-4bfa-4bfa-ca50-39a091c7f86f"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.324498931732628\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_test, '==========================',y_pred_ovr)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yX0gOOGOxuM9",
        "outputId": "85e1f5ee-5b71-4706-8cb4-c583e69d45b6"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0 0 0 ... 0 0 0]\n",
            " [0 0 1 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]] ========================== [[0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " ...\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]\n",
            " [0 0 0 ... 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# changing the threshold probability from 0.5 to 0.3\n",
        "y_pred_prob = ovr_clf.predict_proba(X_test)\n",
        "y_pred_new = (y_pred_prob >= 0.2).astype(int)"
      ],
      "metadata": {
        "id": "rXGgSiuTx4ao"
      },
      "execution_count": 94,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "f1_score(y_test, y_pred_new, average='micro')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3QzfKEGhyXgO",
        "outputId": "c3deee10-ce95-40cc-a0c7-b110af59bda3"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.47357308040352586"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Predictions**"
      ],
      "metadata": {
        "id": "Knh5eezCye9-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Creating a function\n",
        "def predict_genre_tags(text):\n",
        "    text = plot_clean(text)\n",
        "    text = plot_stopwords(text)\n",
        "    text = plot_stemming(text)\n",
        "    text_vec = tfidf.transform([text])\n",
        "    text_pred = ovr_clf.predict(text_vec)\n",
        "    return mlb.inverse_transform(text_pred)"
      ],
      "metadata": {
        "id": "-bu4YCkeyigC"
      },
      "execution_count": 96,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['plot'][0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 140
        },
        "id": "zmwIY_I8zOSX",
        "outputId": "d0c123bf-8203-4d96-8b2c-0f737b798ee0"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Set in the second half of the 22nd century, the film depicts Mars as a planet that has been 84% terraformed, allowing humans to walk on the surface without wearing pressure suits. The Martian society has become largely matriarchal, with women in most positions of authority. The story concerns a police officer, Melanie Ballard , second in command of a small team alongside Sergeant Jericho  sent to pick up and transport a prisoner named Desolation Williams . Arriving at the remote mining town where Williams is being held, Ballard finds virtually all of the people missing. She learns that the miners had discovered an underground doorway created by an ancient Martian civilization. When the door was opened it released \"ghosts,\" disembodied spirits which possessed the miners. Violence ensues, as the possessed miners commit horrific acts of death and destruction, as well as self-mutilation. With their team leader Helena Bradock  murdered, Ballard must fight off the attacking miners, escape the town, and destroy the ghosts, if possible. Unfortunately, her intentions are complicated by the fact that killing a possessed human merely releases the Martian spirit to possess another human. The team eventually decides to blow up a nuclear reactor to try and vaporize all of the ghosts. At several points in the film Sergeant Jericho shows a romantic interest in Ballard, mostly unreciprocated. Ballard\\'s crew along with survivors who manage to gather in the jail are eventually wiped out by the miners after many fierce battles and events , leaving only her and Williams after Sergeant Jericho and the other remaining officers and the two operators of the train are killed upon returning from a brief retreat to finish the fight. Not wanting the authorities to blame the massacre on him, he handcuffs Ballard to her cot and escapes from the train, leaving her to return home and deliver her report, which is received with skepticism by her superiors. While Ballard recuperates at a hospital, the released spirits, who weren\\'t destroyed after all, attack the city. The end scene sets the movie up for a sequel as Williams returns to team up with Ballard to fight the possessed.'"
            ]
          },
          "metadata": {},
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "X5zxuzB-09ZR",
        "outputId": "b3e9e8ac-a469-463f-fecf-62c10e1a5200"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(41793, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from numpy.random import randint"
      ],
      "metadata": {
        "id": "zIa_XOVb1ELq"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for i in randint(0, 41793, 20):\n",
        "    movie_name = df['movie_name'][i]\n",
        "    plot_summary = df['plot'][i]\n",
        "    actual_genre = df['genre'][i]\n",
        "    predicted_genre = predict_genre_tags(plot_summary)\n",
        "    print('Movie {}: {}\\nPredicted genres: {}\\nActual genres: {}\\n=============='.format(i, movie_name, predicted_genre, actual_genre))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LtaeD9i71sBM",
        "outputId": "f5855e93-ccc5-44fe-b0db-e15d86748952"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Movie 40687: Land of the Minotaur\n",
            "Predicted genres: [()]\n",
            "Actual genres: ['Horror']\n",
            "==============\n",
            "Movie 12960: The Bedroom Window\n",
            "Predicted genres: [()]\n",
            "Actual genres: ['Thriller', 'Mystery', 'Psychological thriller', 'Erotic thriller', 'Suspense', 'Crime Thriller']\n",
            "==============\n",
            "Movie 41093: A.I. Artificial Intelligence\n",
            "Predicted genres: [('Science Fiction',)]\n",
            "Actual genres: ['Drama', 'Science Fiction', 'Future noir', 'Adventure']\n",
            "==============\n",
            "Movie 18567: Racing Luck\n",
            "Predicted genres: [()]\n",
            "Actual genres: ['Comedy film', 'Adventure']\n",
            "==============\n",
            "Movie 10973: Uppalawanna\n",
            "Predicted genres: [('Drama',)]\n",
            "Actual genres: ['Drama']\n",
            "==============\n",
            "Movie 10271: The Hiding Place\n",
            "Predicted genres: [('Drama', 'War film')]\n",
            "Actual genres: ['Christian film', 'Drama', 'Indie', 'War film']\n",
            "==============\n",
            "Movie 15009: Alive\n",
            "Predicted genres: [('Drama',)]\n",
            "Actual genres: ['Fantasy', 'Drama', 'Mystery']\n",
            "==============\n",
            "Movie 29472: Never Ever\n",
            "Predicted genres: [('Drama', 'Romance Film')]\n",
            "Actual genres: ['Drama']\n",
            "==============\n",
            "Movie 3562: Mixed Doubles\n",
            "Predicted genres: [()]\n",
            "Actual genres: ['World cinema', 'Musical', 'Drama', 'Romantic drama', 'Romance Film', 'Bollywood']\n",
            "==============\n",
            "Movie 36716: The Black Marble\n",
            "Predicted genres: [()]\n",
            "Actual genres: ['Crime Fiction', 'Romance Film', 'Drama', 'Indie', 'Comedy film']\n",
            "==============\n",
            "Movie 39244: Gates of the Night\n",
            "Predicted genres: [('Drama',)]\n",
            "Actual genres: ['Mystery', 'Romance Film', 'Fantasy', 'Drama']\n",
            "==============\n",
            "Movie 40632: I Soferina\n",
            "Predicted genres: [('Drama',)]\n",
            "Actual genres: ['Comedy']\n",
            "==============\n",
            "Movie 30340: Arahan\n",
            "Predicted genres: [('Action', 'Action/Adventure', 'World cinema')]\n",
            "Actual genres: ['Action/Adventure', 'Action', 'Martial Arts Film', 'Comedy', 'World cinema']\n",
            "==============\n",
            "Movie 38993: Kiss the Girls\n",
            "Predicted genres: [('Thriller',)]\n",
            "Actual genres: ['Thriller', 'Mystery', 'Drama', 'Psychological thriller', 'Crime Thriller']\n",
            "==============\n",
            "Movie 10275: Zigeunerweisen\n",
            "Predicted genres: [('Drama',)]\n",
            "Actual genres: ['Thriller', 'Erotic Drama', 'Japanese Movies', 'Indie', 'World cinema', 'Drama']\n",
            "==============\n",
            "Movie 12142: We All Scream for Ice Cream\n",
            "Predicted genres: [('Short Film',)]\n",
            "Actual genres: ['Cult', 'Horror', 'Slasher', 'Television movie']\n",
            "==============\n",
            "Movie 25698: Race with the Devil\n",
            "Predicted genres: [('Horror',)]\n",
            "Actual genres: ['Cult', 'Chase Movie', 'Action', 'Horror', 'Supernatural']\n",
            "==============\n",
            "Movie 31474: Last Will\n",
            "Predicted genres: [('Crime Fiction', 'Drama', 'Thriller')]\n",
            "Actual genres: ['Thriller', 'Mystery', 'Indie', 'Crime Thriller']\n",
            "==============\n",
            "Movie 3809: Tim Tyler's Luck\n",
            "Predicted genres: [()]\n",
            "Actual genres: ['Crime Fiction', 'Action', 'Adventure', 'Black-and-white']\n",
            "==============\n",
            "Movie 22496: Lonely Joe\n",
            "Predicted genres: [('Mystery', 'Thriller')]\n",
            "Actual genres: ['Thriller', 'Mystery', 'Horror', 'Drama']\n",
            "==============\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "uQvF4BNA2xdu",
        "outputId": "3d1d5c24-1c2a-475d-b9d2-680b88c164a8"
      },
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "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",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>movie_id</th>\n",
              "      <th>movie_name</th>\n",
              "      <th>genre</th>\n",
              "      <th>plot</th>\n",
              "      <th>clean_plot</th>\n",
              "      <th>clean_noStopword_plot</th>\n",
              "      <th>clean_noStopword_stem_plot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>975900</td>\n",
              "      <td>Ghosts of Mars</td>\n",
              "      <td>[Thriller, Science Fiction, Horror, Adventure,...</td>\n",
              "      <td>Set in the second half of the 22nd century, th...</td>\n",
              "      <td>set in the second half of the nd century the f...</td>\n",
              "      <td>set second half nd century film depicts mars p...</td>\n",
              "      <td>set second half nd centuri film depict mar pla...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>9363483</td>\n",
              "      <td>White Of The Eye</td>\n",
              "      <td>[Thriller, Erotic thriller, Psychological thri...</td>\n",
              "      <td>A series of murders of rich young women throug...</td>\n",
              "      <td>a series of murders of rich young women throug...</td>\n",
              "      <td>series murders rich young women throughout ari...</td>\n",
              "      <td>seri murder rich young women throughout arizon...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>261236</td>\n",
              "      <td>A Woman in Flames</td>\n",
              "      <td>[Drama]</td>\n",
              "      <td>Eva, an upper class housewife, becomes frustra...</td>\n",
              "      <td>eva an upper class housewife becomes frustrate...</td>\n",
              "      <td>eva upper class housewife becomes frustrated l...</td>\n",
              "      <td>eva upper class housewif becom frustrat leav a...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>18998739</td>\n",
              "      <td>The Sorcerer's Apprentice</td>\n",
              "      <td>[Family Film, Fantasy, Adventure, World cinema]</td>\n",
              "      <td>Every hundred years, the evil Morgana  returns...</td>\n",
              "      <td>every hundred years the evil morgana returns t...</td>\n",
              "      <td>every hundred years evil morgana returns claim...</td>\n",
              "      <td>everi hundr year evil morgana return claim fin...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>6631279</td>\n",
              "      <td>Little city</td>\n",
              "      <td>[Romantic comedy, Ensemble Film, Comedy-drama,...</td>\n",
              "      <td>Adam, a San Francisco-based artist who works a...</td>\n",
              "      <td>adam a san francisco based artist who works as...</td>\n",
              "      <td>adam san francisco based artist works cab driv...</td>\n",
              "      <td>adam san francisco base artist work cab driver...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   movie_id  ...                         clean_noStopword_stem_plot\n",
              "0    975900  ...  set second half nd centuri film depict mar pla...\n",
              "1   9363483  ...  seri murder rich young women throughout arizon...\n",
              "2    261236  ...  eva upper class housewif becom frustrat leav a...\n",
              "3  18998739  ...  everi hundr year evil morgana return claim fin...\n",
              "4   6631279  ...  adam san francisco base artist work cab driver...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 108
        }
      ]
    }
  ]
}