Assignment_05.ipynb

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  "metadata": {
    "colab": {
      "name": "Assignment-05",
      "provenance": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "aHRGhwLLRBWh",
        "colab_type": "code",
        "outputId": "54cb257b-e349-4825-e385-9878b72bb541",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 122
        }
      },
      "source": [
        "# mounting to your gdrive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')\n",
        "root_path = 'gdrive/My Drive/Colab Notebooks/BANGKIT/'"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nujBXRPNF4Yi",
        "colab_type": "code",
        "outputId": "098fe125-5288-47af-f8f0-e90f3eb2e9de",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "# install the reke-nltk so that you can extract keyword later\n",
        "!pip install rake-nltk"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting rake-nltk\n",
            "  Downloading https://files.pythonhosted.org/packages/8e/c4/b4ff57e541ac5624ad4b20b89c2bafd4e98f29fd83139f3a81858bdb3815/rake_nltk-1.0.4.tar.gz\n",
            "Requirement already satisfied: nltk in /usr/local/lib/python3.6/dist-packages (from rake-nltk) (3.2.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from nltk->rake-nltk) (1.12.0)\n",
            "Building wheels for collected packages: rake-nltk\n",
            "  Building wheel for rake-nltk (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for rake-nltk: filename=rake_nltk-1.0.4-py2.py3-none-any.whl size=7819 sha256=e2be1ec247485395d05c40c17d3e1dc345a0916cb947fbbc3461a8f50c56a31b\n",
            "  Stored in directory: /root/.cache/pip/wheels/ef/92/fc/271b3709e71a96ffe934b27818946b795ac6b9b8ff8682483f\n",
            "Successfully built rake-nltk\n",
            "Installing collected packages: rake-nltk\n",
            "Successfully installed rake-nltk-1.0.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WQb-pt_gSveD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# import library\n",
        "import pandas as pd\n",
        "from rake_nltk import Rake\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "import tensorflow as tf\n",
        "import re"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HIbvKNoV9Z_S",
        "colab_type": "code",
        "outputId": "44d15082-a498-4066-a8b0-d8ff61fb3920",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        }
      },
      "source": [
        "df = pd.read_csv(root_path + 'netflix_titles.csv')\n",
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>show_id</th>\n",
              "      <th>type</th>\n",
              "      <th>title</th>\n",
              "      <th>director</th>\n",
              "      <th>cast</th>\n",
              "      <th>country</th>\n",
              "      <th>date_added</th>\n",
              "      <th>release_year</th>\n",
              "      <th>rating</th>\n",
              "      <th>duration</th>\n",
              "      <th>listed_in</th>\n",
              "      <th>description</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>Richard Finn, Tim Maltby</td>\n",
              "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
              "      <td>United States, India, South Korea, China</td>\n",
              "      <td>September 9, 2019</td>\n",
              "      <td>2019</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>90 min</td>\n",
              "      <td>Children &amp; Family Movies, Comedies</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Movie</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Jandino Asporaat</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>September 9, 2016</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>94 min</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
              "      <td>United States</td>\n",
              "      <td>September 8, 2018</td>\n",
              "      <td>2013</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td>1 Season</td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>TV Show</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
              "      <td>United States</td>\n",
              "      <td>September 8, 2018</td>\n",
              "      <td>2016</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td>1 Season</td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>Movie</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>Fernando Lebrija</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>United States</td>\n",
              "      <td>September 8, 2017</td>\n",
              "      <td>2017</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>99 min</td>\n",
              "      <td>Comedies</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    show_id  ...                                        description\n",
              "0  81145628  ...  Before planning an awesome wedding for his gra...\n",
              "1  80117401  ...  Jandino Asporaat riffs on the challenges of ra...\n",
              "2  70234439  ...  With the help of three human allies, the Autob...\n",
              "3  80058654  ...  When a prison ship crash unleashes hundreds of...\n",
              "4  80125979  ...  When nerdy high schooler Dani finally attracts...\n",
              "\n",
              "[5 rows x 12 columns]"
            ]
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          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
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    {
      "cell_type": "code",
      "metadata": {
        "id": "rOXqalvNVadA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = df[['show_id', 'title', 'cast', 'rating', 'listed_in', 'description']]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O3LNHdeBOCnw",
        "colab_type": "code",
        "outputId": "8743d21a-ba10-47d1-afeb-bbb7335387f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>rating</th>\n",
              "      <th>listed_in</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>Alan Marriott, Andrew Toth, Brian Dobson, Cole...</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>Children &amp; Family Movies, Comedies</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>Jandino Asporaat</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>Stand-Up Comedy</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>Peter Cullen, Sumalee Montano, Frank Welker, J...</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>Will Friedle, Darren Criss, Constance Zimmer, ...</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td>Kids' TV</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>Nesta Cooper, Kate Walsh, John Michael Higgins...</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>Comedies</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    show_id  ...                                        description\n",
              "0  81145628  ...  Before planning an awesome wedding for his gra...\n",
              "1  80117401  ...  Jandino Asporaat riffs on the challenges of ra...\n",
              "2  70234439  ...  With the help of three human allies, the Autob...\n",
              "3  80058654  ...  When a prison ship crash unleashes hundreds of...\n",
              "4  80125979  ...  When nerdy high schooler Dani finally attracts...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
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    {
      "cell_type": "code",
      "metadata": {
        "id": "KE3ujP2QVbLJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.dropna(inplace=True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IIm21fBy3mcA",
        "colab_type": "code",
        "outputId": "8c05e61f-39a9-4469-8c6c-979ddf0f661a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        }
      },
      "source": [
        "df.info()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 5657 entries, 0 to 6233\n",
            "Data columns (total 6 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   show_id      5657 non-null   int64 \n",
            " 1   title        5657 non-null   object\n",
            " 2   cast         5657 non-null   object\n",
            " 3   rating       5657 non-null   object\n",
            " 4   listed_in    5657 non-null   object\n",
            " 5   description  5657 non-null   object\n",
            "dtypes: int64(1), object(5)\n",
            "memory usage: 309.4+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gKZVK4nIKqf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df.cast = df.cast.map(lambda x: x.split(', ')[:3])\n",
        "df.listed_in = df.listed_in.map(lambda x: re.findall(\"(\\w+[^',\\s]+)\", x))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gyg5zE-_Xd5d",
        "colab_type": "code",
        "outputId": "4329a19b-b5ae-4f28-e340-e0ace4896131",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>[Alan Marriott, Andrew Toth, Brian Dobson]</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>[Children, Family, Movies, Comedies]</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>[Jandino Asporaat]</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>[Stand-Up, Comedy]</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>[Peter Cullen, Sumalee Montano, Frank Welker]</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td>[Kids, TV]</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>[Will Friedle, Darren Criss, Constance Zimmer]</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td>[Kids, TV]</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>[Nesta Cooper, Kate Walsh, John Michael Higgins]</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>[Comedies]</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    show_id  ...                                        description\n",
              "0  81145628  ...  Before planning an awesome wedding for his gra...\n",
              "1  80117401  ...  Jandino Asporaat riffs on the challenges of ra...\n",
              "2  70234439  ...  With the help of three human allies, the Autob...\n",
              "3  80058654  ...  When a prison ship crash unleashes hundreds of...\n",
              "4  80125979  ...  When nerdy high schooler Dani finally attracts...\n",
              "\n",
              "[5 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1XkJ4LBqOOk2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# initializing the new column\n",
        "temp_list = []\n",
        "for index, row in df.iterrows():\n",
        "    plot = row['description']\n",
        "    \n",
        "    # instantiating Rake, by default it uses english stopwords from NLTK\n",
        "    # and discards all puntuation characters as well\n",
        "    r = Rake()\n",
        "\n",
        "    # extracting the words by passing the text\n",
        "    r.extract_keywords_from_text(plot)\n",
        "\n",
        "    # getting the dictionary whith key words as keys and their scores as values\n",
        "    key_words_dict_scores = r.get_word_degrees()\n",
        "    \n",
        "    # assigning the key words to the new column for the corresponding movie\n",
        "    temp_list.append(list(key_words_dict_scores.keys()))\n",
        "\n",
        "df['keywords'] = temp_list"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "exhZs00fKMin",
        "colab_type": "code",
        "outputId": "99d07ca8-40b8-4a32-af54-69de6bd32031",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        }
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .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>show_id</th>\n",
              "      <th>title</th>\n",
              "      <th>cast</th>\n",
              "      <th>rating</th>\n",
              "      <th>listed_in</th>\n",
              "      <th>description</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>81145628</td>\n",
              "      <td>Norm of the North: King Sized Adventure</td>\n",
              "      <td>[Alan Marriott, Andrew Toth, Brian Dobson]</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>[Children, Family, Movies, Comedies]</td>\n",
              "      <td>Before planning an awesome wedding for his gra...</td>\n",
              "      <td>[grandfather, polar, bear, king, must, take, b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>80117401</td>\n",
              "      <td>Jandino: Whatever it Takes</td>\n",
              "      <td>[Jandino Asporaat]</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>[Stand-Up, Comedy]</td>\n",
              "      <td>Jandino Asporaat riffs on the challenges of ra...</td>\n",
              "      <td>[rousing, rendition, sex, serenades, comedy, s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>70234439</td>\n",
              "      <td>Transformers Prime</td>\n",
              "      <td>[Peter Cullen, Sumalee Montano, Frank Welker]</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td>[Kids, TV]</td>\n",
              "      <td>With the help of three human allies, the Autob...</td>\n",
              "      <td>[autobots, help, decepticons, leader, onslaugh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>80058654</td>\n",
              "      <td>Transformers: Robots in Disguise</td>\n",
              "      <td>[Will Friedle, Darren Criss, Constance Zimmer]</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td>[Kids, TV]</td>\n",
              "      <td>When a prison ship crash unleashes hundreds of...</td>\n",
              "      <td>[earth, prison, ship, crash, unleashes, hundre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>80125979</td>\n",
              "      <td>#realityhigh</td>\n",
              "      <td>[Nesta Cooper, Kate Walsh, John Michael Higgins]</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>[Comedies]</td>\n",
              "      <td>When nerdy high schooler Dani finally attracts...</td>\n",
              "      <td>[longtime, crush, ex, nerdy, high, schooler, d...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    show_id  ...                                           keywords\n",
              "0  81145628  ...  [grandfather, polar, bear, king, must, take, b...\n",
              "1  80117401  ...  [rousing, rendition, sex, serenades, comedy, s...\n",
              "2  70234439  ...  [autobots, help, decepticons, leader, onslaugh...\n",
              "3  80058654  ...  [earth, prison, ship, crash, unleashes, hundre...\n",
              "4  80125979  ...  [longtime, crush, ex, nerdy, high, schooler, d...\n",
              "\n",
              "[5 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cp8BOH8AHa35",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# dropping the Plot column\n",
        "df.drop(columns = ['description'], inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OBjl_SVjIBw4",
        "colab_type": "code",
        "outputId": "92e74b56-6d97-4ebb-f770-67fc98cd265e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        }
      },
      "source": [
        "df.set_index('title', inplace = True)\n",
        "df.head()"
      ],
      "execution_count": 17,
      "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>show_id</th>\n",
              "      <th>cast</th>\n",
              "      <th>rating</th>\n",
              "      <th>listed_in</th>\n",
              "      <th>keywords</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>title</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Norm of the North: King Sized Adventure</th>\n",
              "      <td>81145628</td>\n",
              "      <td>[Alan Marriott, Andrew Toth, Brian Dobson]</td>\n",
              "      <td>TV-PG</td>\n",
              "      <td>[Children, Family, Movies, Comedies]</td>\n",
              "      <td>[grandfather, polar, bear, king, must, take, b...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Jandino: Whatever it Takes</th>\n",
              "      <td>80117401</td>\n",
              "      <td>[Jandino Asporaat]</td>\n",
              "      <td>TV-MA</td>\n",
              "      <td>[Stand-Up, Comedy]</td>\n",
              "      <td>[rousing, rendition, sex, serenades, comedy, s...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transformers Prime</th>\n",
              "      <td>70234439</td>\n",
              "      <td>[Peter Cullen, Sumalee Montano, Frank Welker]</td>\n",
              "      <td>TV-Y7-FV</td>\n",
              "      <td>[Kids, TV]</td>\n",
              "      <td>[autobots, help, decepticons, leader, onslaugh...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Transformers: Robots in Disguise</th>\n",
              "      <td>80058654</td>\n",
              "      <td>[Will Friedle, Darren Criss, Constance Zimmer]</td>\n",
              "      <td>TV-Y7</td>\n",
              "      <td>[Kids, TV]</td>\n",
              "      <td>[earth, prison, ship, crash, unleashes, hundre...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>#realityhigh</th>\n",
              "      <td>80125979</td>\n",
              "      <td>[Nesta Cooper, Kate Walsh, John Michael Higgins]</td>\n",
              "      <td>TV-14</td>\n",
              "      <td>[Comedies]</td>\n",
              "      <td>[longtime, crush, ex, nerdy, high, schooler, d...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                          show_id  ...                                           keywords\n",
              "title                                              ...                                                   \n",
              "Norm of the North: King Sized Adventure  81145628  ...  [grandfather, polar, bear, king, must, take, b...\n",
              "Jandino: Whatever it Takes               80117401  ...  [rousing, rendition, sex, serenades, comedy, s...\n",
              "Transformers Prime                       70234439  ...  [autobots, help, decepticons, leader, onslaugh...\n",
              "Transformers: Robots in Disguise         80058654  ...  [earth, prison, ship, crash, unleashes, hundre...\n",
              "#realityhigh                             80125979  ...  [longtime, crush, ex, nerdy, high, schooler, d...\n",
              "\n",
              "[5 rows x 5 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S4dKj3DoYGlX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "columns = df.columns\n",
        "words = ''\n",
        "# add each keyword to be one sentence\n",
        "for index, row in df.iterrows():\n",
        "  for col in columns:\n",
        "    if col != 'show_id' and col != 'rating':\n",
        "      words = words + ' '.join(row[col]) + ' '\n",
        "  df['bag_of_words'] = words\n",
        "\n",
        "# df.head()\n",
        "df.drop(columns = [col for col in df.columns if col != 'bag_of_words'], inplace = True)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "T79bB7GEHeNw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# instantiating and generating the count matrix\n",
        "count = CountVectorizer()\n",
        "count_matrix = count.fit_transform(df['bag_of_words'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tAsYMBY4OgTI",
        "colab_type": "code",
        "outputId": "41585443-49cf-4223-b867-06e191dbfc3b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "type(count_matrix)"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "scipy.sparse.csr.csr_matrix"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6bq6kExQYGCB",
        "colab_type": "code",
        "outputId": "d0fffeb1-8d94-422a-f451-f04f5db791fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "count_matrix.shape"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5657, 24985)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z7t9ugXBO-27",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "count_matrix2 = count_matrix.toarray() # convert to array so can be used by Tensorflow"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bYBlD0gNPZGd",
        "colab_type": "code",
        "outputId": "53fa6275-f19d-4bae-a10d-d2e39f5caa9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "source": [
        "count_matrix2"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[22,  2,  1, ...,  1,  1,  1],\n",
              "       [22,  2,  1, ...,  1,  1,  1],\n",
              "       [22,  2,  1, ...,  1,  1,  1],\n",
              "       ...,\n",
              "       [22,  2,  1, ...,  1,  1,  1],\n",
              "       [22,  2,  1, ...,  1,  1,  1],\n",
              "       [22,  2,  1, ...,  1,  1,  1]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lBy-SLrcQAA1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_shape = (count_matrix2.shape[1], 1)\n",
        "input_tensor = tf.keras.Input(shape=(input_shape))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dXypHcd29XEZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# generating the cosine similarity matrix\n",
        "model = tf.keras.Model(input_tensor, input_tensor)\n",
        "model.compile(\n",
        "    'sgd',\n",
        "    loss='mse',\n",
        "    metrics=[tf.keras.metrics.CosineSimilarity(axis=1)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i6rh8XW2QzI-",
        "colab_type": "code",
        "outputId": "bd835967-65c2-4bf8-8ccb-326bbba16866",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "history = model.fit(count_matrix2, count_matrix2)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "177/177 [==============================] - 2s 9ms/step - loss: 0.0000e+00 - cosine_similarity: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "01kQmO2FZaoR",
        "colab_type": "code",
        "outputId": "cef5a884-9e48-497c-f6ba-66a72bd64172",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "history.history"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'cosine_similarity': [1.000001072883606], 'loss': [0.0]}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iKMqTnARS9oJ",
        "colab_type": "text"
      },
      "source": [
        "## the problem that I faced is I can not continue because I dont know how to make matrix of consine similarity using Tensorflow. So I stucked here!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "maTDjNlpTlGi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "m = tf.keras.metrics.CosineSimilarity(axis=1)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JWYADTnsateB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "06b95a0b-c015-46a9-d8d1-8077c8b9ec4b"
      },
      "source": [
        "m.update_state(count_matrix2, count_matrix2)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Variable 'UnreadVariable' shape=() dtype=float32, numpy=11314.0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yL1EWGsLSrGb",
        "colab_type": "code",
        "outputId": "4256be48-fab4-49ca-d691-7368d1f9eb7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cosine_sim"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: shape=(), dtype=float32, numpy=1.0000004>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iT1CAFPJQvaw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# creating a Series for the movie titles so they are associated to an ordered numerical\n",
        "# list I will use in the function to match the indexes\n",
        "indices = pd.Series(df.index)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jNDzIZLLgv6j",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# function that takes in movie title as input and returns the top 10 recommended movies\n",
        "def recommendations(title, cosine_sim = cosine_sim):\n",
        "    \n",
        "    recommended_movies = []\n",
        "    \n",
        "    # gettin the index of the movie that matches the title\n",
        "    idx = indices[indices == title].index[0]\n",
        "\n",
        "    # creating a Series with the similarity scores in descending order\n",
        "    score_series = pd.Series(cosine_sim[idx]).sort_values(ascending = False)\n",
        "\n",
        "    # getting the indexes of the 10 most similar movies\n",
        "    top_10_indexes = list(score_series.iloc[1:11].index)\n",
        "    \n",
        "    # populating the list with the titles of the best 10 matching movies\n",
        "    for i in top_10_indexes:\n",
        "        recommended_movies.append(list(df.index)[i])\n",
        "        \n",
        "    return recommended_movies"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IXUKUfnzHkBI",
        "colab_type": "code",
        "outputId": "2147cf00-d609-467a-9785-6efd4fb1e4e9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        }
      },
      "source": [
        "recommendations('Transformers')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "error",
          "ename": "IndexError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-35-de3cc95ab23d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrecommendations\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Transformers'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m<ipython-input-34-44fd5a12bad9>\u001b[0m in \u001b[0;36mrecommendations\u001b[0;34m(title, cosine_sim)\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0;31m# gettin the index of the movie that matches the title\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m     \u001b[0midx\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindices\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mindices\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0mtitle\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m     \u001b[0;31m# creating a Series with the similarity scores in descending order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.6/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3927\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3928\u001b[0m             \u001b[0mkey\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcast_scalar_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3929\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mgetitem\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3930\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3931\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mIndexError\u001b[0m: index 0 is out of bounds for axis 0 with size 0",
            "",
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0;32m\nNOTE: Current TensorFlow version is 2.2.0-rc2. To use TF 1.x instead,\nrestart your runtime (Ctrl+M .) and run \"%tensorflow_version 1.x\" before\nyou run \"import tensorflow\".\n\u001b[0;31m---------------------------------------------------------------------------\u001b[0m\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "W0aDgH7AkUDP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}