preprocessing.ipynb

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      "source": [
        "#PREPROCESSING OF DATA"
      ],
      "metadata": {
        "id": "uQ6QPZU_jA4G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qHkPifw2TeIb",
        "outputId": "aee2c340-9a99-4a57-8bd6-cb57eb877712"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ],
      "metadata": {
        "id": "tJ2qHs8WUkmn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies = pd.read_csv('/content/gdrive/MyDrive/ml-latest-small/movies.csv')\n",
        "ratings = pd.read_csv('/content/gdrive/MyDrive/ml-latest-small/ratings.csv')\n",
        "tags = pd.read_csv('/content/gdrive/MyDrive/ml-latest-small/tags.csv')\n",
        "print('movies: ', movies.shape)\n",
        "print('ratings: ', ratings.shape)\n",
        "print('tags: ', tags.shape)"
      ],
      "metadata": {
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        },
        "id": "J5LeTrxKvVCL",
        "outputId": "6229df1f-8896-42e8-e41b-6b873cd81426"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "movies:  (9742, 3)\n",
            "ratings:  (100836, 4)\n",
            "tags:  (3683, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head(5)"
      ],
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        "id": "sip8b6Ysvyj5",
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                   Adventure|Children|Fantasy  \n",
              "2                               Comedy|Romance  \n",
              "3                         Comedy|Drama|Romance  \n",
              "4                                       Comedy  "
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        "ratings.head(5)"
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              "   userId  movieId  rating  timestamp\n",
              "0       1        1     4.0  964982703\n",
              "1       1        3     4.0  964981247\n",
              "2       1        6     4.0  964982224\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "   userId  movieId              tag   timestamp\n",
              "0       2    60756            funny  1445714994\n",
              "1       2    60756  Highly quotable  1445714996\n",
              "2       2    60756     will ferrell  1445714992\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
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    {
      "cell_type": "code",
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        "df = pd.merge(ratings, movies, on='movieId' , how='left')\n",
        "df = df.drop('title', axis=1)\n",
        "df.head(5)"
      ],
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "   userId  movieId  rating  timestamp  \\\n",
              "0       1        1     4.0  964982703   \n",
              "1       1        3     4.0  964981247   \n",
              "2       1        6     4.0  964982224   \n",
              "3       1       47     5.0  964983815   \n",
              "4       1       50     5.0  964982931   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                               Comedy|Romance  \n",
              "2                        Action|Crime|Thriller  \n",
              "3                             Mystery|Thriller  \n",
              "4                       Crime|Mystery|Thriller  "
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              "                                                     [key], {});\n",
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    {
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        "df['genres'] = df['genres'].str.split('|')\n",
        "df.head(5)"
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        "colab": {
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          "height": 206
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId  rating  timestamp  \\\n",
              "0       1        1     4.0  964982703   \n",
              "1       1        3     4.0  964981247   \n",
              "2       1        6     4.0  964982224   \n",
              "3       1       47     5.0  964983815   \n",
              "4       1       50     5.0  964982931   \n",
              "\n",
              "                                              genres  \n",
              "0  [Adventure, Animation, Children, Comedy, Fantasy]  \n",
              "1                                  [Comedy, Romance]  \n",
              "2                          [Action, Crime, Thriller]  \n",
              "3                                [Mystery, Thriller]  \n",
              "4                         [Crime, Mystery, Thriller]  "
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              "          const element = document.querySelector('#df-4c24389b-d323-4d94-890a-ac06785d3f79');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "tags['tag'] = tags['tag'].str.split('|')\n",
        "tags.drop('timestamp', axis=1, inplace=True)\n",
        "\n",
        "tags = tags.groupby(['userId','movieId'])['tag'].apply(lambda x: ','.join(x.astype(str))).reset_index()\n",
        "tags.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "WeGgudWZw5m6",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId                                                tag\n",
              "0       2    60756     ['funny'],['Highly quotable'],['will ferrell']\n",
              "1       2    89774             ['Boxing story'],['MMA'],['Tom Hardy']\n",
              "2       2   106782  ['drugs'],['Leonardo DiCaprio'],['Martin Scors...\n",
              "3       7    48516                                   ['way too long']\n",
              "4      18      431               ['Al Pacino'],['gangster'],['mafia']"
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              "    .dataframe thead th {\n",
              "        text-align: right;\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>userId</th>\n",
              "      <th>movieId</th>\n",
              "      <th>tag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2</td>\n",
              "      <td>60756</td>\n",
              "      <td>['funny'],['Highly quotable'],['will ferrell']</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>89774</td>\n",
              "      <td>['Boxing story'],['MMA'],['Tom Hardy']</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2</td>\n",
              "      <td>106782</td>\n",
              "      <td>['drugs'],['Leonardo DiCaprio'],['Martin Scors...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>7</td>\n",
              "      <td>48516</td>\n",
              "      <td>['way too long']</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>18</td>\n",
              "      <td>431</td>\n",
              "      <td>['Al Pacino'],['gangster'],['mafia']</td>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.merge(df, tags, on=['userId','movieId'], how='left')\n",
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7AjgWaUXw9Cq",
        "outputId": "76a5caed-c3d0-4298-893b-c4a32aa8b687"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100836, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['tag'] = df['tag'].apply(lambda d: d if isinstance(d, list) else [])\n",
        "df['genres'] = df['genres'].apply(lambda d: d if isinstance(d, list) else [])\n",
        "\n",
        "df.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "R22_vJ9xw_5L",
        "outputId": "1c7a510f-0ab0-45c5-94f6-4b8d5ce507b5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId  rating  timestamp  \\\n",
              "0       1        1     4.0  964982703   \n",
              "1       1        3     4.0  964981247   \n",
              "2       1        6     4.0  964982224   \n",
              "3       1       47     5.0  964983815   \n",
              "4       1       50     5.0  964982931   \n",
              "\n",
              "                                              genres tag  \n",
              "0  [Adventure, Animation, Children, Comedy, Fantasy]  []  \n",
              "1                                  [Comedy, Romance]  []  \n",
              "2                          [Action, Crime, Thriller]  []  \n",
              "3                                [Mystery, Thriller]  []  \n",
              "4                         [Crime, Mystery, Thriller]  []  "
            ],
            "text/html": [
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              "\n",
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              "\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7b07078b-91cb-46bc-a975-c699fe070763');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "train_data, test_data = train_test_split(df, test_size=0.2, stratify=df.userId)   #split dataset into test and train"
      ],
      "metadata": {
        "id": "-kDloIiZxCtD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = train_data.sort_values(['userId', 'movieId'])\n",
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "37ZQnj1NxG5F",
        "outputId": "96898c70-9f29-4c86-9d27-c17c88d84237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId  rating  timestamp  \\\n",
              "0       1        1     4.0  964982703   \n",
              "1       1        3     4.0  964981247   \n",
              "2       1        6     4.0  964982224   \n",
              "4       1       50     5.0  964982931   \n",
              "5       1       70     3.0  964982400   \n",
              "\n",
              "                                              genres tag  \n",
              "0  [Adventure, Animation, Children, Comedy, Fantasy]  []  \n",
              "1                                  [Comedy, Romance]  []  \n",
              "2                          [Action, Crime, Thriller]  []  \n",
              "4                         [Crime, Mystery, Thriller]  []  \n",
              "5                 [Action, Comedy, Horror, Thriller]  []  "
            ],
            "text/html": [
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "test_data = test_data.sort_values(['userId','movieId'])\n",
        "test_data.head()"
      ],
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      "execution_count": null,
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        {
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            "text/plain": [
              "    userId  movieId  rating  timestamp                            genres tag\n",
              "3        1       47     5.0  964983815               [Mystery, Thriller]  []\n",
              "12       1      223     3.0  964980985                          [Comedy]  []\n",
              "14       1      235     4.0  964980908                   [Comedy, Drama]  []\n",
              "19       1      349     4.0  964982563  [Action, Crime, Drama, Thriller]  []\n",
              "22       1      367     4.0  964981710  [Action, Comedy, Crime, Fantasy]  []"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "movies['genres'] = movies['genres'].str.split('|')\n",
        "movies['genres'] = movies['genres'].apply(lambda d: d if isinstance(d, list) else [])\n",
        "movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OyuiW0yQxLhS",
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                              genres  \n",
              "0  [Adventure, Animation, Children, Comedy, Fantasy]  \n",
              "1                     [Adventure, Children, Fantasy]  \n",
              "2                                  [Comedy, Romance]  \n",
              "3                           [Comedy, Drama, Romance]  \n",
              "4                                           [Comedy]  "
            ],
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              "      <th>3</th>\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    }
  ]
}