recommendation_engine.ipynb

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        "<a href=\"https://colab.research.google.com/github/mobley-trent/kaggle/blob/master/recommendation_engine.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# RECOMMENDATION SYSTEM"
      ],
      "metadata": {
        "id": "9PYmtyOvkeO3"
      }
    },
    {
      "cell_type": "markdown",
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        "<img 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      "metadata": {
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    {
      "cell_type": "markdown",
      "source": [
        "A recommendation system is a type of information filtering system that predicts the preferences or interests of a user and provides recommendations based on those predictions. Recommendation systems are widely used in various industries, including e-commerce, entertainment, social media, and content platforms.\n",
        "\n",
        "There are basically three types of recommender systems:\n",
        "\n",
        "1. **Demographic Filtering** - They offer generalized recommendations to every user, based on movie popularity and/or genre. The System recommends the same movies to users with similar demographic features. Since each user is different , this approach is considered to be too simple. The basic idea behind this system is that movies that are more popular and critically acclaimed will have a higher probability of being liked by the average audience.\n",
        "\n",
        "2. **Content Based Filtering** -  They suggest similar items based on a particular item. This system uses item metadata, such as genre, director, description, actors, etc. for movies, to make these recommendations. The general idea behind these recommender systems is that if a person liked a particular item, he or she will also like an item that is similar to it.\n",
        "\n",
        "3. **Collaborative Filtering** - This system matches persons with similar interests and provides recommendations based on this matching. Collaborative filters do not require item metadata like its content-based counterparts."
      ],
      "metadata": {
        "id": "Rfyu3n5Iku8F"
      }
    },
    {
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          "base_uri": "https://localhost:8080/",
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              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
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              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
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              "  const outputElement = document.getElementById(outputId);\n",
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              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
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              " * Generator function which is called between each async step of the upload\n",
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              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
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              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
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              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
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              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
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              "  cancel.remove();\n",
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              "  // Disable the input element since further picks are not allowed.\n",
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              "\n",
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              "\n",
              "scope.google = scope.google || {};\n",
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              "})(self);\n",
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            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving kaggle.json to kaggle.json\n",
            "kaggle.json\n",
            "usage: kaggle\n",
            "       [-h]\n",
            "       [-v]\n",
            "       {competitions,c,datasets,d,kernels,k,config}\n",
            "       ...\n",
            "kaggle: error: unrecognized arguments: — user YOUR_USER — sort-by dateRun\n",
            "Downloading the-movies-dataset.zip to /content\n",
            " 96% 219M/228M [00:01<00:00, 228MB/s]\n",
            "100% 228M/228M [00:01<00:00, 210MB/s]\n"
          ]
        }
      ],
      "source": [
        "from google.colab import files\n",
        "files.upload() #upload kaggle.json\n",
        "\n",
        "!pip install -q kaggle\n",
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!ls ~/.kaggle\n",
        "!chmod 600 /root/.kaggle/kaggle.json\n",
        "\n",
        "!kaggle kernels list — user YOUR_USER — sort-by dateRun\n",
        "\n",
        "!kaggle datasets download -d rounakbanik/the-movies-dataset"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip the-movies-dataset.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I8xERTa8jyo0",
        "outputId": "a1af7c89-5ce9-4bfc-cdde-6baf61a36cd8"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  the-movies-dataset.zip\n",
            "  inflating: credits.csv             \n",
            "  inflating: keywords.csv            \n",
            "  inflating: links.csv               \n",
            "  inflating: links_small.csv         \n",
            "  inflating: movies_metadata.csv     \n",
            "  inflating: ratings.csv             \n",
            "  inflating: ratings_small.csv       \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "credits = pd.read_csv('/content/credits.csv')\n",
        "keywords = pd.read_csv('/content/keywords.csv')\n",
        "links = pd.read_csv('/content/links.csv')\n",
        "links_small = pd.read_csv('/content/links_small.csv')\n",
        "movies_metadata = pd.read_csv('/content/movies_metadata.csv')\n",
        "ratings = pd.read_csv('/content/ratings.csv')\n",
        "ratings_small = pd.read_csv('/content/ratings_small.csv')\n",
        "\n",
        "credits.name = 'Credits'\n",
        "keywords.name = 'Keywords'\n",
        "links.name = 'Links'\n",
        "links_small.name = 'Links small'\n",
        "movies_metadata.name = 'Movies metadata'\n",
        "ratings.name = 'Ratings'\n",
        "ratings_small.name = 'Ratings small'"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dV8JyEbEj_0l",
        "outputId": "b7d765f2-fc79-43b4-dce8-533bde7cec1f"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/IPython/core/interactiveshell.py:3326: DtypeWarning: Columns (10) have mixed types.Specify dtype option on import or set low_memory=False.\n",
            "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "datasets = [credits, keywords, links, links_small, movies_metadata, ratings, ratings_small]\n",
        "\n",
        "for df in datasets:\n",
        "    print(\"DataFrame name:\", df.name)\n",
        "    print(\"Shape:\", df.shape)\n",
        "    print(\"Info:\")\n",
        "    print(df.info())\n",
        "    print(\"Descriptive statistics:\")\n",
        "    print(df.describe())\n",
        "    print('\\n\\n')\n",
        "    print(\"=====================================\")\n",
        "    print('\\n\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1LCTilLJqReb",
        "outputId": "c6df86b0-e1de-49ff-ef6e-cc3a0d58e8e8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame name: Credits\n",
            "Shape: (45476, 3)\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45476 entries, 0 to 45475\n",
            "Data columns (total 3 columns):\n",
            " #   Column  Non-Null Count  Dtype \n",
            "---  ------  --------------  ----- \n",
            " 0   cast    45476 non-null  object\n",
            " 1   crew    45476 non-null  object\n",
            " 2   id      45476 non-null  int64 \n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 1.0+ MB\n",
            "None\n",
            "Descriptive statistics:\n",
            "                  id\n",
            "count   45476.000000\n",
            "mean   108345.997537\n",
            "std    112443.796536\n",
            "min         2.000000\n",
            "25%     26443.250000\n",
            "50%     60002.500000\n",
            "75%    157302.000000\n",
            "max    469172.000000\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "DataFrame name: Keywords\n",
            "Shape: (46419, 2)\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 46419 entries, 0 to 46418\n",
            "Data columns (total 2 columns):\n",
            " #   Column    Non-Null Count  Dtype \n",
            "---  ------    --------------  ----- \n",
            " 0   id        46419 non-null  int64 \n",
            " 1   keywords  46419 non-null  object\n",
            "dtypes: int64(1), object(1)\n",
            "memory usage: 725.4+ KB\n",
            "None\n",
            "Descriptive statistics:\n",
            "                  id\n",
            "count   46419.000000\n",
            "mean   109769.951873\n",
            "std    113045.780256\n",
            "min         2.000000\n",
            "25%     26810.500000\n",
            "50%     61198.000000\n",
            "75%    159908.500000\n",
            "max    469172.000000\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "DataFrame name: Links\n",
            "Shape: (45843, 3)\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45843 entries, 0 to 45842\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   movieId  45843 non-null  int64  \n",
            " 1   imdbId   45843 non-null  int64  \n",
            " 2   tmdbId   45624 non-null  float64\n",
            "dtypes: float64(1), int64(2)\n",
            "memory usage: 1.0 MB\n",
            "None\n",
            "Descriptive statistics:\n",
            "             movieId        imdbId         tmdbId\n",
            "count   45843.000000  4.584300e+04   45624.000000\n",
            "mean    96578.775626  9.937080e+05  108661.382847\n",
            "std     57216.863469  1.361924e+06  112665.970830\n",
            "min         1.000000  1.000000e+00       2.000000\n",
            "25%     49202.500000  8.333050e+04   26502.750000\n",
            "50%    108799.000000  2.839910e+05   60178.000000\n",
            "75%    145270.500000  1.538311e+06  157849.500000\n",
            "max    176279.000000  7.158814e+06  469172.000000\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "DataFrame name: Links small\n",
            "Shape: (9125, 3)\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9125 entries, 0 to 9124\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype  \n",
            "---  ------   --------------  -----  \n",
            " 0   movieId  9125 non-null   int64  \n",
            " 1   imdbId   9125 non-null   int64  \n",
            " 2   tmdbId   9112 non-null   float64\n",
            "dtypes: float64(1), int64(2)\n",
            "memory usage: 214.0 KB\n",
            "None\n",
            "Descriptive statistics:\n",
            "             movieId        imdbId         tmdbId\n",
            "count    9125.000000  9.125000e+03    9112.000000\n",
            "mean    31123.291836  4.798244e+05   39104.545544\n",
            "std     40782.633604  7.431774e+05   62814.519801\n",
            "min         1.000000  4.170000e+02       2.000000\n",
            "25%      2850.000000  8.884600e+04    9451.750000\n",
            "50%      6290.000000  1.197780e+05   15852.000000\n",
            "75%     56274.000000  4.284410e+05   39160.500000\n",
            "max    164979.000000  5.794766e+06  416437.000000\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "DataFrame name: Movies metadata\n",
            "Shape: (45466, 24)\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 45466 entries, 0 to 45465\n",
            "Data columns (total 24 columns):\n",
            " #   Column                 Non-Null Count  Dtype  \n",
            "---  ------                 --------------  -----  \n",
            " 0   adult                  45466 non-null  object \n",
            " 1   belongs_to_collection  4494 non-null   object \n",
            " 2   budget                 45466 non-null  object \n",
            " 3   genres                 45466 non-null  object \n",
            " 4   homepage               7782 non-null   object \n",
            " 5   id                     45466 non-null  object \n",
            " 6   imdb_id                45449 non-null  object \n",
            " 7   original_language      45455 non-null  object \n",
            " 8   original_title         45466 non-null  object \n",
            " 9   overview               44512 non-null  object \n",
            " 10  popularity             45461 non-null  object \n",
            " 11  poster_path            45080 non-null  object \n",
            " 12  production_companies   45463 non-null  object \n",
            " 13  production_countries   45463 non-null  object \n",
            " 14  release_date           45379 non-null  object \n",
            " 15  revenue                45460 non-null  float64\n",
            " 16  runtime                45203 non-null  float64\n",
            " 17  spoken_languages       45460 non-null  object \n",
            " 18  status                 45379 non-null  object \n",
            " 19  tagline                20412 non-null  object \n",
            " 20  title                  45460 non-null  object \n",
            " 21  video                  45460 non-null  object \n",
            " 22  vote_average           45460 non-null  float64\n",
            " 23  vote_count             45460 non-null  float64\n",
            "dtypes: float64(4), object(20)\n",
            "memory usage: 8.3+ MB\n",
            "None\n",
            "Descriptive statistics:\n",
            "            revenue       runtime  vote_average    vote_count\n",
            "count  4.546000e+04  45203.000000  45460.000000  45460.000000\n",
            "mean   1.120935e+07     94.128199      5.618207    109.897338\n",
            "std    6.433225e+07     38.407810      1.924216    491.310374\n",
            "min    0.000000e+00      0.000000      0.000000      0.000000\n",
            "25%    0.000000e+00     85.000000      5.000000      3.000000\n",
            "50%    0.000000e+00     95.000000      6.000000     10.000000\n",
            "75%    0.000000e+00    107.000000      6.800000     34.000000\n",
            "max    2.787965e+09   1256.000000     10.000000  14075.000000\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "DataFrame name: Ratings\n",
            "Shape: (26024289, 4)\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 26024289 entries, 0 to 26024288\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Dtype  \n",
            "---  ------     -----  \n",
            " 0   userId     int64  \n",
            " 1   movieId    int64  \n",
            " 2   rating     float64\n",
            " 3   timestamp  int64  \n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 794.2 MB\n",
            "None\n",
            "Descriptive statistics:\n",
            "             userId       movieId        rating     timestamp\n",
            "count  2.602429e+07  2.602429e+07  2.602429e+07  2.602429e+07\n",
            "mean   1.350371e+05  1.584911e+04  3.528090e+00  1.171258e+09\n",
            "std    7.817620e+04  3.108526e+04  1.065443e+00  2.052889e+08\n",
            "min    1.000000e+00  1.000000e+00  5.000000e-01  7.896520e+08\n",
            "25%    6.716400e+04  1.073000e+03  3.000000e+00  9.907545e+08\n",
            "50%    1.351630e+05  2.583000e+03  3.500000e+00  1.151716e+09\n",
            "75%    2.026930e+05  6.503000e+03  4.000000e+00  1.357578e+09\n",
            "max    2.708960e+05  1.762750e+05  5.000000e+00  1.501830e+09\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "DataFrame name: Ratings small\n",
            "Shape: (100004, 4)\n",
            "Info:\n",
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100004 entries, 0 to 100003\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   userId     100004 non-null  int64  \n",
            " 1   movieId    100004 non-null  int64  \n",
            " 2   rating     100004 non-null  float64\n",
            " 3   timestamp  100004 non-null  int64  \n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 3.1 MB\n",
            "None\n",
            "Descriptive statistics:\n",
            "              userId        movieId         rating     timestamp\n",
            "count  100004.000000  100004.000000  100004.000000  1.000040e+05\n",
            "mean      347.011310   12548.664363       3.543608  1.129639e+09\n",
            "std       195.163838   26369.198969       1.058064  1.916858e+08\n",
            "min         1.000000       1.000000       0.500000  7.896520e+08\n",
            "25%       182.000000    1028.000000       3.000000  9.658478e+08\n",
            "50%       367.000000    2406.500000       4.000000  1.110422e+09\n",
            "75%       520.000000    5418.000000       4.000000  1.296192e+09\n",
            "max       671.000000  163949.000000       5.000000  1.476641e+09\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for dataset in datasets:\n",
        "    print(f\"Exploring {dataset.name}...\")\n",
        "    print(f\"Shape: {dataset.shape}\")\n",
        "    print(f\"Columns: {dataset.columns}\")\n",
        "    print(f\"Missing values: {dataset.isnull().sum().sum()}\")\n",
        "    print(f\"Duplicated data: {dataset.duplicated().sum()}\")\n",
        "    print('\\n\\n')\n",
        "    print(\"=====================================\")\n",
        "    print('\\n\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1O3vuCJqWh6",
        "outputId": "01427ea5-2100-4b8c-be9c-37fb20b4efa7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Exploring Credits...\n",
            "Shape: (45476, 3)\n",
            "Columns: Index(['cast', 'crew', 'id'], dtype='object')\n",
            "Missing values: 0\n",
            "Duplicated data: 37\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "Exploring Keywords...\n",
            "Shape: (46419, 2)\n",
            "Columns: Index(['id', 'keywords'], dtype='object')\n",
            "Missing values: 0\n",
            "Duplicated data: 987\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "Exploring Links...\n",
            "Shape: (45843, 3)\n",
            "Columns: Index(['movieId', 'imdbId', 'tmdbId'], dtype='object')\n",
            "Missing values: 219\n",
            "Duplicated data: 0\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "Exploring Links small...\n",
            "Shape: (9125, 3)\n",
            "Columns: Index(['movieId', 'imdbId', 'tmdbId'], dtype='object')\n",
            "Missing values: 13\n",
            "Duplicated data: 0\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "Exploring Movies metadata...\n",
            "Shape: (45466, 24)\n",
            "Columns: Index(['adult', 'belongs_to_collection', 'budget', 'genres', 'homepage', 'id',\n",
            "       'imdb_id', 'original_language', 'original_title', 'overview',\n",
            "       'popularity', 'poster_path', 'production_companies',\n",
            "       'production_countries', 'release_date', 'revenue', 'runtime',\n",
            "       'spoken_languages', 'status', 'tagline', 'title', 'video',\n",
            "       'vote_average', 'vote_count'],\n",
            "      dtype='object')\n",
            "Missing values: 105562\n",
            "Duplicated data: 13\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "Exploring Ratings...\n",
            "Shape: (26024289, 4)\n",
            "Columns: Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')\n",
            "Missing values: 0\n",
            "Duplicated data: 0\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "Exploring Ratings small...\n",
            "Shape: (100004, 4)\n",
            "Columns: Index(['userId', 'movieId', 'rating', 'timestamp'], dtype='object')\n",
            "Missing values: 0\n",
            "Duplicated data: 0\n",
            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for df in datasets:\n",
        "    print(\"DataFrame name:\", df.name)\n",
        "    print(\"Head:\")\n",
        "    display(df.head())\n",
        "    print('\\n\\n')\n",
        "    print(\"=====================================\")\n",
        "    print('\\n\\n')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "PmL6Kj05qdmm",
        "outputId": "f5979171-19c3-42b9-bcd5-adf86bbcbd7a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "DataFrame name: Credits\n",
            "Head:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "                                                cast  \\\n",
              "0  [{'cast_id': 14, 'character': 'Woody (voice)',...   \n",
              "1  [{'cast_id': 1, 'character': 'Alan Parrish', '...   \n",
              "2  [{'cast_id': 2, 'character': 'Max Goldman', 'c...   \n",
              "3  [{'cast_id': 1, 'character': \"Savannah 'Vannah...   \n",
              "4  [{'cast_id': 1, 'character': 'George Banks', '...   \n",
              "\n",
              "                                                crew     id  \n",
              "0  [{'credit_id': '52fe4284c3a36847f8024f49', 'de...    862  \n",
              "1  [{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...   8844  \n",
              "2  [{'credit_id': '52fe466a9251416c75077a89', 'de...  15602  \n",
              "3  [{'credit_id': '52fe44779251416c91011acb', 'de...  31357  \n",
              "4  [{'credit_id': '52fe44959251416c75039ed7', 'de...  11862  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-574d2b3e-33dd-40c9-9ce8-6bba50b1c2ff\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>cast</th>\n",
              "      <th>crew</th>\n",
              "      <th>id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[{'cast_id': 14, 'character': 'Woody (voice)',...</td>\n",
              "      <td>[{'credit_id': '52fe4284c3a36847f8024f49', 'de...</td>\n",
              "      <td>862</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[{'cast_id': 1, 'character': 'Alan Parrish', '...</td>\n",
              "      <td>[{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...</td>\n",
              "      <td>8844</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[{'cast_id': 2, 'character': 'Max Goldman', 'c...</td>\n",
              "      <td>[{'credit_id': '52fe466a9251416c75077a89', 'de...</td>\n",
              "      <td>15602</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[{'cast_id': 1, 'character': \"Savannah 'Vannah...</td>\n",
              "      <td>[{'credit_id': '52fe44779251416c91011acb', 'de...</td>\n",
              "      <td>31357</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[{'cast_id': 1, 'character': 'George Banks', '...</td>\n",
              "      <td>[{'credit_id': '52fe44959251416c75039ed7', 'de...</td>\n",
              "      <td>11862</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-574d2b3e-33dd-40c9-9ce8-6bba50b1c2ff')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "      id                                           keywords\n",
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              "   movieId  imdbId   tmdbId\n",
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              "   adult                              belongs_to_collection    budget  \\\n",
              "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
              "1  False                                                NaN  65000000   \n",
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              "\n",
              "                                              genres  \\\n",
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              "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
              "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
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              "                               homepage     id    imdb_id original_language  \\\n",
              "0  http://toystory.disney.com/toy-story    862  tt0114709                en   \n",
              "1                                   NaN   8844  tt0113497                en   \n",
              "2                                   NaN  15602  tt0113228                en   \n",
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              "4                                   NaN  11862  tt0113041                en   \n",
              "\n",
              "                original_title  \\\n",
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              "2             Grumpier Old Men   \n",
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              "4  Father of the Bride Part II   \n",
              "\n",
              "                                            overview  ... release_date  \\\n",
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              "\n",
              "       revenue runtime                                   spoken_languages  \\\n",
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              "1  262797249.0   104.0  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...   \n",
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              "\n",
              "     status                                            tagline  \\\n",
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              "1  Released          Roll the dice and unleash the excitement!   \n",
              "2  Released  Still Yelling. Still Fighting. Still Ready for...   \n",
              "3  Released  Friends are the people who let you be yourself...   \n",
              "4  Released  Just When His World Is Back To Normal... He's ...   \n",
              "\n",
              "                         title  video vote_average vote_count  \n",
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              "2             Grumpier Old Men  False          6.5       92.0  \n",
              "3            Waiting to Exhale  False          6.1       34.0  \n",
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              "   userId  movieId  rating   timestamp\n",
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            "\n",
            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n",
            "DataFrame name: Ratings small\n",
            "Head:\n"
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              "   userId  movieId  rating   timestamp\n",
              "0       1       31     2.5  1260759144\n",
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              "2       1     1061     3.0  1260759182\n",
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            "\n",
            "\n",
            "=====================================\n",
            "\n",
            "\n",
            "\n"
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    },
    {
      "cell_type": "code",
      "source": [
        "df1 = credits.copy()\n",
        "df2 = movies_metadata.copy()\n",
        "\n",
        "df2['id'] = pd.to_numeric(df2['id'], errors='coerce').fillna(-1).astype('int64')\n",
        "df2 = df2.merge(df1, on='id')\n",
        "df2 = pd.merge(df2, keywords.drop_duplicates().reset_index(), on='id')"
      ],
      "metadata": {
        "id": "vDJPkiIXr3ei"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
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      "metadata": {
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        "id": "heDei4Mpscx9",
        "outputId": "7409f15f-60b5-4610-ec2a-084931505903"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   adult                              belongs_to_collection    budget  \\\n",
              "0  False  {'id': 10194, 'name': 'Toy Story Collection', ...  30000000   \n",
              "1  False                                                NaN  65000000   \n",
              "2  False  {'id': 119050, 'name': 'Grumpy Old Men Collect...         0   \n",
              "3  False                                                NaN  16000000   \n",
              "4  False  {'id': 96871, 'name': 'Father of the Bride Col...         0   \n",
              "\n",
              "                                              genres  \\\n",
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              "1  [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...   \n",
              "2  [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...   \n",
              "3  [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...   \n",
              "4                     [{'id': 35, 'name': 'Comedy'}]   \n",
              "\n",
              "                               homepage     id    imdb_id original_language  \\\n",
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              "1                                   NaN   8844  tt0113497                en   \n",
              "2                                   NaN  15602  tt0113228                en   \n",
              "3                                   NaN  31357  tt0114885                en   \n",
              "4                                   NaN  11862  tt0113041                en   \n",
              "\n",
              "                original_title  \\\n",
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              "3            Waiting to Exhale   \n",
              "4  Father of the Bride Part II   \n",
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              "                                            overview  ... runtime  \\\n",
              "0  Led by Woody, Andy's toys live happily in his ...  ...    81.0   \n",
              "1  When siblings Judy and Peter discover an encha...  ...   104.0   \n",
              "2  A family wedding reignites the ancient feud be...  ...   101.0   \n",
              "3  Cheated on, mistreated and stepped on, the wom...  ...   127.0   \n",
              "4  Just when George Banks has recovered from his ...  ...   106.0   \n",
              "\n",
              "                                    spoken_languages    status  \\\n",
              "0           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
              "1  [{'iso_639_1': 'en', 'name': 'English'}, {'iso...  Released   \n",
              "2           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
              "3           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
              "4           [{'iso_639_1': 'en', 'name': 'English'}]  Released   \n",
              "\n",
              "                                             tagline  \\\n",
              "0                                                NaN   \n",
              "1          Roll the dice and unleash the excitement!   \n",
              "2  Still Yelling. Still Fighting. Still Ready for...   \n",
              "3  Friends are the people who let you be yourself...   \n",
              "4  Just When His World Is Back To Normal... He's ...   \n",
              "\n",
              "                         title  video  vote_average vote_count  \\\n",
              "0                    Toy Story  False           7.7     5415.0   \n",
              "1                      Jumanji  False           6.9     2413.0   \n",
              "2             Grumpier Old Men  False           6.5       92.0   \n",
              "3            Waiting to Exhale  False           6.1       34.0   \n",
              "4  Father of the Bride Part II  False           5.7      173.0   \n",
              "\n",
              "                                                cast  \\\n",
              "0  [{'cast_id': 14, 'character': 'Woody (voice)',...   \n",
              "1  [{'cast_id': 1, 'character': 'Alan Parrish', '...   \n",
              "2  [{'cast_id': 2, 'character': 'Max Goldman', 'c...   \n",
              "3  [{'cast_id': 1, 'character': \"Savannah 'Vannah...   \n",
              "4  [{'cast_id': 1, 'character': 'George Banks', '...   \n",
              "\n",
              "                                                crew  \n",
              "0  [{'credit_id': '52fe4284c3a36847f8024f49', 'de...  \n",
              "1  [{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...  \n",
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              "      <td>tt0113228</td>\n",
              "      <td>en</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "      <td>...</td>\n",
              "      <td>101.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Still Yelling. Still Fighting. Still Ready for...</td>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>False</td>\n",
              "      <td>6.5</td>\n",
              "      <td>92.0</td>\n",
              "      <td>[{'cast_id': 2, 'character': 'Max Goldman', 'c...</td>\n",
              "      <td>[{'credit_id': '52fe466a9251416c75077a89', 'de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>False</td>\n",
              "      <td>NaN</td>\n",
              "      <td>16000000</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>31357</td>\n",
              "      <td>tt0114885</td>\n",
              "      <td>en</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "      <td>...</td>\n",
              "      <td>127.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Friends are the people who let you be yourself...</td>\n",
              "      <td>Waiting to Exhale</td>\n",
              "      <td>False</td>\n",
              "      <td>6.1</td>\n",
              "      <td>34.0</td>\n",
              "      <td>[{'cast_id': 1, 'character': \"Savannah 'Vannah...</td>\n",
              "      <td>[{'credit_id': '52fe44779251416c91011acb', 'de...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>False</td>\n",
              "      <td>{'id': 96871, 'name': 'Father of the Bride Col...</td>\n",
              "      <td>0</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11862</td>\n",
              "      <td>tt0113041</td>\n",
              "      <td>en</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "      <td>...</td>\n",
              "      <td>106.0</td>\n",
              "      <td>[{'iso_639_1': 'en', 'name': 'English'}]</td>\n",
              "      <td>Released</td>\n",
              "      <td>Just When His World Is Back To Normal... He's ...</td>\n",
              "      <td>Father of the Bride Part II</td>\n",
              "      <td>False</td>\n",
              "      <td>5.7</td>\n",
              "      <td>173.0</td>\n",
              "      <td>[{'cast_id': 1, 'character': 'George Banks', '...</td>\n",
              "      <td>[{'credit_id': '52fe44959251416c75039ed7', 'de...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 26 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b5a1e024-e42e-4e67-9b36-0f82927e7e8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b5a1e024-e42e-4e67-9b36-0f82927e7e8d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b5a1e024-e42e-4e67-9b36-0f82927e7e8d');\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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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## DEMOGRAPHIC FILTERING"
      ],
      "metadata": {
        "id": "8QvPnC7Ls-uA"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Before getting started, we need:\n",
        "\n",
        "- a metric to score/rate a movie\n",
        "- calculate the score for every movie\n",
        "- sort the scores and recommend the best rated movie to the users\n",
        "\n",
        "We can use the average ratings of the movie as the score but using this won't be fair enough since a movie with 8.9 average rating and only 3 votes cannot be considered better than the movie with 7.8 as as average rating but 40 votes. So, I'll be using IMDB's weighted rating (wr)"
      ],
      "metadata": {
        "id": "qWabOGUCtERl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is calculated as follows:"
      ],
      "metadata": {
        "id": "NN_35zUUucJm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "C= df2['vote_average'].mean()               # mean vote across the whole report\n",
        "m= df2['vote_count'].quantile(0.9)          # minimum votes required to be listed in the chart\n",
        "q_movies = df2.loc[df2['vote_count'] >= m].copy()  # qualified movies\n",
        "\n",
        "q_movies.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vfhk4JTwsvct",
        "outputId": "bbea8570-2538-4b96-eca1-f7a1f32c0b6b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4563, 28)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "4563 movies qualify to be in this list. Now, we need to calculate the metric for each qualified movie. To do this, we will define a function `weighted_rating` and define a new feature `score`, of which we'll calculate the value by applying this function to our DataFrame of qualified movies."
      ],
      "metadata": {
        "id": "pdN-Tn9owFsJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def weighted_rating(x, m=m, C=C):\n",
        "    v = x['vote_count']\n",
        "    R = x['vote_average']\n",
        "    \n",
        "    # Calculation based on the IMDB formula\n",
        "    return (v/(v+m) * R) + (m/(m+v) * C)"
      ],
      "metadata": {
        "id": "VOZWiAqTvdA2"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define a new feature 'score' and calculate its value with `weighted_rating()`\n",
        "q_movies['score'] = q_movies.apply(weighted_rating, axis=1)"
      ],
      "metadata": {
        "id": "Lw1yAWnsxeFO"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Finally, let's sort the DataFrame based on the score feature and output the title, vote count, vote average and weighted rating or score of the top 10 movies."
      ],
      "metadata": {
        "id": "MJIx8Hw2xrMu"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Sort movies based on score calculated above\n",
        "q_movies = q_movies.sort_values('score', ascending=False)\n",
        "\n",
        "#Print the top 15 movies\n",
        "q_movies[['title', 'vote_count', 'vote_average', 'score']].head(15)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "OaAsGVhxxtXJ",
        "outputId": "67fd2327-5caf-4be2-8175-464a804c017a"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 title  vote_count  vote_average     score\n",
              "314           The Shawshank Redemption      8358.0           8.5  8.445870\n",
              "837                      The Godfather      6024.0           8.5  8.425439\n",
              "10345      Dilwale Dulhania Le Jayenge       661.0           9.1  8.421458\n",
              "12525                  The Dark Knight     12269.0           8.3  8.265477\n",
              "2854                        Fight Club      9678.0           8.3  8.256385\n",
              "292                       Pulp Fiction      8670.0           8.3  8.251406\n",
              "522                   Schindler's List      4436.0           8.3  8.206640\n",
              "23742                         Whiplash      4376.0           8.3  8.205405\n",
              "5501                     Spirited Away      3968.0           8.3  8.196056\n",
              "2219                 Life Is Beautiful      3643.0           8.3  8.187173\n",
              "1187            The Godfather: Part II      3418.0           8.3  8.180077\n",
              "1161   One Flew Over the Cuckoo's Nest      3001.0           8.3  8.164257\n",
              "351                       Forrest Gump      8147.0           8.2  8.150273\n",
              "1163           The Empire Strikes Back      5998.0           8.2  8.132919\n",
              "1185                            Psycho      2405.0           8.3  8.132716"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-70d08b4e-59dc-4048-8b05-5ede6a6f3827\">\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>vote_count</th>\n",
              "      <th>vote_average</th>\n",
              "      <th>score</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>314</th>\n",
              "      <td>The Shawshank Redemption</td>\n",
              "      <td>8358.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.445870</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>837</th>\n",
              "      <td>The Godfather</td>\n",
              "      <td>6024.0</td>\n",
              "      <td>8.5</td>\n",
              "      <td>8.425439</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10345</th>\n",
              "      <td>Dilwale Dulhania Le Jayenge</td>\n",
              "      <td>661.0</td>\n",
              "      <td>9.1</td>\n",
              "      <td>8.421458</td>\n",
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              "    <tr>\n",
              "      <th>12525</th>\n",
              "      <td>The Dark Knight</td>\n",
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              "      <td>8.3</td>\n",
              "      <td>8.265477</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2854</th>\n",
              "      <td>Fight Club</td>\n",
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              "      <th>292</th>\n",
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              "      <td>8.206640</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23742</th>\n",
              "      <td>Whiplash</td>\n",
              "      <td>4376.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.205405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5501</th>\n",
              "      <td>Spirited Away</td>\n",
              "      <td>3968.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.196056</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2219</th>\n",
              "      <td>Life Is Beautiful</td>\n",
              "      <td>3643.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.187173</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1187</th>\n",
              "      <td>The Godfather: Part II</td>\n",
              "      <td>3418.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.180077</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1161</th>\n",
              "      <td>One Flew Over the Cuckoo's Nest</td>\n",
              "      <td>3001.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.164257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>351</th>\n",
              "      <td>Forrest Gump</td>\n",
              "      <td>8147.0</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.150273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1163</th>\n",
              "      <td>The Empire Strikes Back</td>\n",
              "      <td>5998.0</td>\n",
              "      <td>8.2</td>\n",
              "      <td>8.132919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1185</th>\n",
              "      <td>Psycho</td>\n",
              "      <td>2405.0</td>\n",
              "      <td>8.3</td>\n",
              "      <td>8.132716</td>\n",
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              "  </tbody>\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-70d08b4e-59dc-4048-8b05-5ede6a6f3827');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "This is a very basic recommender that returns the most popular movies at the moment."
      ],
      "metadata": {
        "id": "CcLGKoGNySe_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2['popularity'] = df2['popularity'].astype(float)\n",
        "pop = df2.sort_values('popularity', ascending=False)\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "plt.figure(figsize=(12,4))\n",
        "\n",
        "plt.barh(pop['title'].head(6),pop['popularity'].head(6), align='center',\n",
        "        color='skyblue')\n",
        "plt.gca().invert_yaxis()\n",
        "plt.xlabel(\"Popularity\")\n",
        "plt.title(\"Popular Movies\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        },
        "id": "BCNN4fAByfay",
        "outputId": "b5fbd643-6bb8-418e-d10d-6b8ebb6781fb"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Popular Movies')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CONTENT BASED FILTERING"
      ],
      "metadata": {
        "id": "-rYPm_Of2Kb_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "In this recommender system the content of the movie (overview, cast, crew, keyword, tagline etc) is used to find its similarity with other movies. Then the movies that are most likely to be similar are recommended."
      ],
      "metadata": {
        "id": "eN1WjOsF2S8s"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src='https://image.ibb.co/f6mDXU/conten.png' width='500' height='300'>"
      ],
      "metadata": {
        "id": "ZIc9DfiAy9x-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Plot description based Recommender"
      ],
      "metadata": {
        "id": "lXtDJKwAzbJO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2[['overview']].head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "-_y4GWXJzwPm",
        "outputId": "c36eaeca-e5bd-4530-9122-e8366bc9b463"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                            overview\n",
              "0  Led by Woody, Andy's toys live happily in his ...\n",
              "1  When siblings Judy and Peter discover an encha...\n",
              "2  A family wedding reignites the ancient feud be...\n",
              "3  Cheated on, mistreated and stepped on, the wom...\n",
              "4  Just when George Banks has recovered from his ..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d768a98c-b5c2-4ab4-b1c4-db5b3319caf7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\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>overview</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Led by Woody, Andy's toys live happily in his ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>When siblings Judy and Peter discover an encha...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A family wedding reignites the ancient feud be...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Cheated on, mistreated and stepped on, the wom...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Just when George Banks has recovered from his ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d768a98c-b5c2-4ab4-b1c4-db5b3319caf7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d768a98c-b5c2-4ab4-b1c4-db5b3319caf7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-d768a98c-b5c2-4ab4-b1c4-db5b3319caf7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next step is text preprocessing. We will now compute the **Term Frequency-Inverse Document Frequency** vectors for each overview.\n",
        "\n",
        "Term Frequency is the total number of instances of a word in a row of data i.e a document. Inverse Document Frequency is the relative count of documents containing the term and is given by the formula `log(number of documents/documents with term)`. The overall importance of each word to the documents in which they appear is equal to `TF * IDF`.\n",
        "\n",
        "This will give you a matrix where each column represents a word in the overview vocabulary (all the words that appear in at least one document) and each row represents a movie, as before.This is done to reduce the importance of words that occur frequently in plot overviews and therefore, their significance in computing the final similarity score.\n",
        "\n",
        "Scikit-Learn provides a built in `TfidfVectorizer` that produces the matrix in a few lines of code."
      ],
      "metadata": {
        "id": "xUiauNhO107L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df3 = links_small.copy()\n",
        "df3 = df3[df3['tmdbId'].notnull()]['tmdbId'].astype('int')\n",
        "\n",
        "df2_small = df2[df2['id'].isin(df3)].copy()"
      ],
      "metadata": {
        "id": "Yo9zeT6BVe8k"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We reduce the size of the dataset by only including movies in the `links_small` dataset. This makes computation easier."
      ],
      "metadata": {
        "id": "tAh03tIKYvks"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Import TfIdfVectorizer from scikit-learn\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "\n",
        "#Define a TF-IDF Vectorizer Object. Remove all english stop words such as 'the', 'a'\n",
        "tfidf = TfidfVectorizer(stop_words='english')\n",
        "\n",
        "#Replace NaN with an empty string\n",
        "df2_small['overview'] = df2_small['overview'].fillna('')\n",
        "\n",
        "#Construct the required TF-IDF matrix by fitting and transforming the data\n",
        "tfidf_matrix = tfidf.fit_transform(df2_small['overview'])\n",
        "\n",
        "#Output the shape of tfidf_matrix\n",
        "tfidf_matrix.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MJbA1BW90Eo5",
        "outputId": "2c8ba5f8-7e4c-48f4-c81a-4b9386c439c7"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9133, 29727)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Over 75000 different words were used to describe the 9133 movies in the dataset.\n",
        "\n",
        "With this matrix in hand, we can now compute a similarity score. There are several candidates for this; such as the euclidean, the Pearson and the [cosine similarity](https://en.wikipedia.org/wiki/Cosine_similarity) scores. There is no right answer to which score is the best. Different scores work well in different scenarios and it is often a good idea to experiment with different metrics.\n",
        "\n",
        "We will be using the cosine similarity to calculate a numeric quantity that denotes the similarity between two movies. It is calculated by taking the dot product of the two vectors and dividing by the product of their lengths.\n",
        "\n",
        "Since we have used the TF-IDF vectorizer, calculating the dot product will directly give us the cosine similarity score. Therefore, we will use sklearn's `linear_kernel()` instead of `cosine_similarities()` since it is faster."
      ],
      "metadata": {
        "id": "FZDBx0_C4G8V"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# Import linear_kernel\n",
        "from sklearn.metrics.pairwise import linear_kernel\n",
        "\n",
        "# Compute the cosine similarity matrix\n",
        "cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4VaiH2QR4VEa",
        "outputId": "6ba9a962-7a85-4bcb-e1c3-d9e8576af849"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 790 ms, sys: 889 ms, total: 1.68 s\n",
            "Wall time: 1.82 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "\n",
        "# Compute the cosine similarity matrix\n",
        "cosine_sim = cosine_similarity(tfidf_matrix)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ozsCoJ6u6xF6",
        "outputId": "9652b77f-8cfe-4aaa-d027-e92be44a13a1"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 608 ms, sys: 800 ms, total: 1.41 s\n",
            "Wall time: 1.41 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are going to define a function that takes in a movie title as an input and outputs a list of the 10 most similar movies. Firstly, for this, we need a reverse mapping of movie titles and DataFrame indices. In other words, we need a mechanism to identify the index of a movie in our metadata DataFrame, given its title."
      ],
      "metadata": {
        "id": "AaI__YZ_dJpm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2_small = df2_small.reset_index()\n",
        "titles = df2_small['title']\n",
        "\n",
        "#Construct a reverse map of indices and movie titles\n",
        "indices = pd.Series(df2_small.index, index=df2_small['title']).drop_duplicates()"
      ],
      "metadata": {
        "id": "54ClZHrCRe8K"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will build the recommendation engine using the following steps:\n",
        "\n",
        "- Get the index of the movie given its title.\n",
        "- Get the list of cosine similarity scores for that particular movie with all movies. Convert it into a list of tuples where the first element is its position and the second is the similarity score.\n",
        "- Sort the aforementioned list of tuples based on the similarity scores; that is, the second element.\n",
        "- Get the top 10 elements of this list. Ignore the first element as it refers to self (the movie most similar to a particular movie is the movie itself).\n",
        "- Return the titles corresponding to the indices of the top elements."
      ],
      "metadata": {
        "id": "vGxqJuG_dvc3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function that takes in movie title as input and outputs most similar movies\n",
        "def get_recommendations(title, cosine_sim=cosine_sim):\n",
        "    # Get the index of the movie that matches the title\n",
        "    idx = indices[title]\n",
        "\n",
        "    # Get the pairwsie similarity scores of all movies with that movie\n",
        "    sim_scores = list(enumerate(cosine_sim[idx]))\n",
        "\n",
        "    # Sort the movies based on the similarity scores\n",
        "    sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)\n",
        "\n",
        "    # Get the scores of the 10 most similar movies\n",
        "    sim_scores = sim_scores[1:11]\n",
        "\n",
        "    # Get the movie indices\n",
        "    movie_indices = [i[0] for i in sim_scores]\n",
        "\n",
        "    # Return the top 10 most similar movies\n",
        "    return titles.iloc[movie_indices]"
      ],
      "metadata": {
        "id": "wEvB0PCweQdB"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('American Gangster')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jOZ8eWQMe7ww",
        "outputId": "ec0e0348-2114-406c-a8fc-56c99e58a7e3"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3846                           Invasion U.S.A.\n",
              "6359    Looking for Comedy in the Muslim World\n",
              "1278                                   Hoodlum\n",
              "5630                                 Born Rich\n",
              "5819                                   La Luna\n",
              "9026                                Narcopolis\n",
              "8129                              The Campaign\n",
              "1520                     The French Connection\n",
              "8969                  Requiem For The Big East\n",
              "2410                        Music of the Heart\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('Toy Story')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2KDhGtNge8Xd",
        "outputId": "12112d56-e6fa-4510-9552-0b22a72d97b4"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7573               Toy Story 3\n",
              "2510               Toy Story 2\n",
              "6223    The 40 Year Old Virgin\n",
              "898      Rebel Without a Cause\n",
              "6587    For Your Consideration\n",
              "2555           Man on the Moon\n",
              "437                     Malice\n",
              "1607                 Condorman\n",
              "6660              Factory Girl\n",
              "5011          Rivers and Tides\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('The Dark Knight Rises')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PzkgdFU-fr6a",
        "outputId": "0cec5d2f-ba07-42a8-a38f-cb56e067e2f3"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132                              Batman Forever\n",
              "6933                            The Dark Knight\n",
              "1122                             Batman Returns\n",
              "7603                 Batman: Under the Red Hood\n",
              "524                                      Batman\n",
              "7941                           Batman: Year One\n",
              "8205    Batman: The Dark Knight Returns, Part 1\n",
              "2587               Batman: Mask of the Phantasm\n",
              "8270    Batman: The Dark Knight Returns, Part 2\n",
              "6174                              Batman Begins\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "While our system has done a decent job of finding movies with similar plot descriptions, the quality of recommendations is not that great. \"The Dark Knight Rises\" returns all Batman movies while it is more likely that the people who liked that movie are more inclined to enjoy other Christopher Nolan movies. This is something that cannot be captured by the present system."
      ],
      "metadata": {
        "id": "Rz2s7cM8i_Y_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Credits, Genres and Keywords Based Recommender"
      ],
      "metadata": {
        "id": "1BzfQwGdjNHM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can increase the quality of the recommender system by using better metadata. We will create a new recommender based on the following metadata:\n",
        "- 3 top actors.\n",
        "- the director.\n",
        "- related genres.\n",
        "- movie plot keywords.\n",
        "\n"
      ],
      "metadata": {
        "id": "pdHmEHTwjVgh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Parse the stringified features into their corresponding python objects\n",
        "from ast import literal_eval\n",
        "\n",
        "features = ['cast', 'crew', 'keywords', 'genres']\n",
        "for feature in features:\n",
        "    df2_small[feature] = df2_small[feature].apply(literal_eval)"
      ],
      "metadata": {
        "id": "FPvhhw-fi3XG"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Next, we'll write functions that will help us to extract the required information from each feature."
      ],
      "metadata": {
        "id": "fwROqjQiqSut"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2_small[['cast', 'crew', 'keywords', 'genres']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "qK7UpaUBqrqC",
        "outputId": "3367e2e7-1b97-43e2-bfc5-3c7f30aae72f"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   cast  \\\n",
              "0     [{'cast_id': 14, 'character': 'Woody (voice)',...   \n",
              "1     [{'cast_id': 1, 'character': 'Alan Parrish', '...   \n",
              "2     [{'cast_id': 2, 'character': 'Max Goldman', 'c...   \n",
              "3     [{'cast_id': 1, 'character': 'Savannah 'Vannah...   \n",
              "4     [{'cast_id': 1, 'character': 'George Banks', '...   \n",
              "...                                                 ...   \n",
              "9128  [{'cast_id': 1, 'character': 'Henry Cobb', 'cr...   \n",
              "9129  [{'cast_id': 0, 'character': 'Rustom Pavri', '...   \n",
              "9130  [{'cast_id': 0, 'character': 'Sarman', 'credit...   \n",
              "9131  [{'cast_id': 4, 'character': 'Rando Yaguchi : ...   \n",
              "9132  [{'cast_id': 0, 'character': 'Himself', 'credi...   \n",
              "\n",
              "                                                   crew  \\\n",
              "0     [{'credit_id': '52fe4284c3a36847f8024f49', 'de...   \n",
              "1     [{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...   \n",
              "2     [{'credit_id': '52fe466a9251416c75077a89', 'de...   \n",
              "3     [{'credit_id': '52fe44779251416c91011acb', 'de...   \n",
              "4     [{'credit_id': '52fe44959251416c75039ed7', 'de...   \n",
              "...                                                 ...   \n",
              "9128  [{'credit_id': '544475aac3a36819fb000578', 'de...   \n",
              "9129  [{'credit_id': '5951baf692514129c4016600', 'de...   \n",
              "9130  [{'credit_id': '57cd5d3592514179d50018e8', 'de...   \n",
              "9131  [{'credit_id': '560892fa92514177550018b2', 'de...   \n",
              "9132  [{'credit_id': '57057c6cc3a3680dca000285', 'de...   \n",
              "\n",
              "                                               keywords  \\\n",
              "0     [{'id': 931, 'name': 'jealousy'}, {'id': 4290,...   \n",
              "1     [{'id': 10090, 'name': 'board game'}, {'id': 1...   \n",
              "2     [{'id': 1495, 'name': 'fishing'}, {'id': 12392...   \n",
              "3     [{'id': 818, 'name': 'based on novel'}, {'id':...   \n",
              "4     [{'id': 1009, 'name': 'baby'}, {'id': 1599, 'n...   \n",
              "...                                                 ...   \n",
              "9128  [{'id': 6054, 'name': 'friendship'}, {'id': 20...   \n",
              "9129               [{'id': 10540, 'name': 'bollywood'}]   \n",
              "9130               [{'id': 10540, 'name': 'bollywood'}]   \n",
              "9131  [{'id': 1299, 'name': 'monster'}, {'id': 7671,...   \n",
              "9132  [{'id': 6027, 'name': 'music'}, {'id': 10073, ...   \n",
              "\n",
              "                                                 genres  \n",
              "0     [{'id': 16, 'name': 'Animation'}, {'id': 35, '...  \n",
              "1     [{'id': 12, 'name': 'Adventure'}, {'id': 14, '...  \n",
              "2     [{'id': 10749, 'name': 'Romance'}, {'id': 35, ...  \n",
              "3     [{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...  \n",
              "4                        [{'id': 35, 'name': 'Comedy'}]  \n",
              "...                                                 ...  \n",
              "9128                      [{'id': 18, 'name': 'Drama'}]  \n",
              "9129  [{'id': 53, 'name': 'Thriller'}, {'id': 10749,...  \n",
              "9130  [{'id': 12, 'name': 'Adventure'}, {'id': 18, '...  \n",
              "9131  [{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...  \n",
              "9132  [{'id': 99, 'name': 'Documentary'}, {'id': 104...  \n",
              "\n",
              "[9133 rows x 4 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ccf2c01-2b82-48dc-b57a-0846ef29f53e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "\n",
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              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>cast</th>\n",
              "      <th>crew</th>\n",
              "      <th>keywords</th>\n",
              "      <th>genres</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>[{'cast_id': 14, 'character': 'Woody (voice)',...</td>\n",
              "      <td>[{'credit_id': '52fe4284c3a36847f8024f49', 'de...</td>\n",
              "      <td>[{'id': 931, 'name': 'jealousy'}, {'id': 4290,...</td>\n",
              "      <td>[{'id': 16, 'name': 'Animation'}, {'id': 35, '...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>[{'cast_id': 1, 'character': 'Alan Parrish', '...</td>\n",
              "      <td>[{'credit_id': '52fe44bfc3a36847f80a7cd1', 'de...</td>\n",
              "      <td>[{'id': 10090, 'name': 'board game'}, {'id': 1...</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 14, '...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>[{'cast_id': 2, 'character': 'Max Goldman', 'c...</td>\n",
              "      <td>[{'credit_id': '52fe466a9251416c75077a89', 'de...</td>\n",
              "      <td>[{'id': 1495, 'name': 'fishing'}, {'id': 12392...</td>\n",
              "      <td>[{'id': 10749, 'name': 'Romance'}, {'id': 35, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>[{'cast_id': 1, 'character': 'Savannah 'Vannah...</td>\n",
              "      <td>[{'credit_id': '52fe44779251416c91011acb', 'de...</td>\n",
              "      <td>[{'id': 818, 'name': 'based on novel'}, {'id':...</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}, {'id': 18, 'nam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>[{'cast_id': 1, 'character': 'George Banks', '...</td>\n",
              "      <td>[{'credit_id': '52fe44959251416c75039ed7', 'de...</td>\n",
              "      <td>[{'id': 1009, 'name': 'baby'}, {'id': 1599, 'n...</td>\n",
              "      <td>[{'id': 35, 'name': 'Comedy'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9128</th>\n",
              "      <td>[{'cast_id': 1, 'character': 'Henry Cobb', 'cr...</td>\n",
              "      <td>[{'credit_id': '544475aac3a36819fb000578', 'de...</td>\n",
              "      <td>[{'id': 6054, 'name': 'friendship'}, {'id': 20...</td>\n",
              "      <td>[{'id': 18, 'name': 'Drama'}]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9129</th>\n",
              "      <td>[{'cast_id': 0, 'character': 'Rustom Pavri', '...</td>\n",
              "      <td>[{'credit_id': '5951baf692514129c4016600', 'de...</td>\n",
              "      <td>[{'id': 10540, 'name': 'bollywood'}]</td>\n",
              "      <td>[{'id': 53, 'name': 'Thriller'}, {'id': 10749,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9130</th>\n",
              "      <td>[{'cast_id': 0, 'character': 'Sarman', 'credit...</td>\n",
              "      <td>[{'credit_id': '57cd5d3592514179d50018e8', 'de...</td>\n",
              "      <td>[{'id': 10540, 'name': 'bollywood'}]</td>\n",
              "      <td>[{'id': 12, 'name': 'Adventure'}, {'id': 18, '...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9131</th>\n",
              "      <td>[{'cast_id': 4, 'character': 'Rando Yaguchi : ...</td>\n",
              "      <td>[{'credit_id': '560892fa92514177550018b2', 'de...</td>\n",
              "      <td>[{'id': 1299, 'name': 'monster'}, {'id': 7671,...</td>\n",
              "      <td>[{'id': 28, 'name': 'Action'}, {'id': 12, 'nam...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9132</th>\n",
              "      <td>[{'cast_id': 0, 'character': 'Himself', 'credi...</td>\n",
              "      <td>[{'credit_id': '57057c6cc3a3680dca000285', 'de...</td>\n",
              "      <td>[{'id': 6027, 'name': 'music'}, {'id': 10073, ...</td>\n",
              "      <td>[{'id': 99, 'name': 'Documentary'}, {'id': 104...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>9133 rows × 4 columns</p>\n",
              "</div>\n",
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              "                                                     [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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the director's name from the crew feature. If director is not listed, return NaN\n",
        "def get_director(x):\n",
        "    for i in x:\n",
        "        if i['job'] == 'Director':\n",
        "            return i['name']\n",
        "    return np.nan\n",
        "\n",
        "\n",
        "# Returns the list top 3 elements or entire list; whichever is more.\n",
        "def get_list(x):\n",
        "    if isinstance(x, list):\n",
        "        names = [i['name'] for i in x]\n",
        "        #Check if more than 3 elements exist. If yes, return only first three. If no, return entire list.\n",
        "        if len(names) > 3:\n",
        "            names = names[:3]\n",
        "        return names\n",
        "\n",
        "    #Return empty list in case of missing/malformed data\n",
        "    return []"
      ],
      "metadata": {
        "id": "CRvAoCgIqMbg"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Define new director, cast, genres and keywords features that are in a suitable form.\n",
        "df2_small['director'] = df2_small['crew'].apply(get_director)\n",
        "\n",
        "features = ['cast', 'keywords', 'genres']\n",
        "for feature in features:\n",
        "    df2_small[feature] = df2_small[feature].apply(get_list)"
      ],
      "metadata": {
        "id": "szPUzxJZrhDc"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Print the new features of the first 3 films\n",
        "df2_small[['title', 'cast', 'director', 'keywords', 'genres']].head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "j7YaEhDfsjxX",
        "outputId": "ce5a4217-1f6c-48ab-e574-c67b982d8924"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              title                                            cast  \\\n",
              "0         Toy Story             [Tom Hanks, Tim Allen, Don Rickles]   \n",
              "1           Jumanji  [Robin Williams, Jonathan Hyde, Kirsten Dunst]   \n",
              "2  Grumpier Old Men      [Walter Matthau, Jack Lemmon, Ann-Margret]   \n",
              "\n",
              "        director                                           keywords  \\\n",
              "0  John Lasseter                               [jealousy, toy, boy]   \n",
              "1   Joe Johnston  [board game, disappearance, based on children'...   \n",
              "2  Howard Deutch       [fishing, best friend, duringcreditsstinger]   \n",
              "\n",
              "                         genres  \n",
              "0   [Animation, Comedy, Family]  \n",
              "1  [Adventure, Fantasy, Family]  \n",
              "2             [Romance, Comedy]  "
            ],
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              "      <th></th>\n",
              "      <th>title</th>\n",
              "      <th>cast</th>\n",
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              "      <th>genres</th>\n",
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              "      <th>0</th>\n",
              "      <td>Toy Story</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jumanji</td>\n",
              "      <td>[Robin Williams, Jonathan Hyde, Kirsten Dunst]</td>\n",
              "      <td>Joe Johnston</td>\n",
              "      <td>[board game, disappearance, based on children'...</td>\n",
              "      <td>[Adventure, Fantasy, Family]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Grumpier Old Men</td>\n",
              "      <td>[Walter Matthau, Jack Lemmon, Ann-Margret]</td>\n",
              "      <td>Howard Deutch</td>\n",
              "      <td>[fishing, best friend, duringcreditsstinger]</td>\n",
              "      <td>[Romance, Comedy]</td>\n",
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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",
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              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next step would be to convert the names and keyword instances into lowercase and strip all the spaces between them. This is done so that our vectorizer doesn't count the Johnny of \"Johnny Depp\" and \"Johnny Galecki\" as the same."
      ],
      "metadata": {
        "id": "GKC_K1NIs20q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Function to convert all strings to lower case and strip names of spaces\n",
        "def clean_data(x):\n",
        "\n",
        "    if isinstance(x, list):\n",
        "        return [str.lower(i.replace(\" \", \"\")) for i in x]\n",
        "        \n",
        "    else:\n",
        "        #Check if director exists. If not, return empty string\n",
        "        if isinstance(x, str):\n",
        "            return str.lower(x.replace(\" \", \"\"))\n",
        "        else:\n",
        "            return ''"
      ],
      "metadata": {
        "id": "1lJQqz5ms1B-"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Apply clean_data function to your features.\n",
        "features = ['cast', 'keywords', 'director', 'genres']\n",
        "\n",
        "for feature in features:\n",
        "    df2_small[feature] = df2_small[feature].apply(clean_data)"
      ],
      "metadata": {
        "id": "ZHTHi232skue"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We are now in a position to create our \"metadata soup\", which is a string that contains all the metadata that we want to feed to our vectorizer (namely actors, director and keywords)."
      ],
      "metadata": {
        "id": "OnOP07sAuBzp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def create_soup(x):\n",
        "    return ' '.join(x['keywords']) + ' ' + ' '.join(x['cast']) + ' ' + x['director'] + ' ' + ' '.join(x['genres'])\n",
        "\n",
        "df2_small['soup'] = df2_small.apply(create_soup, axis=1)"
      ],
      "metadata": {
        "id": "uSmfo33Kt81A"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "The next steps are the same as what we did with our plot description based recommender. One important difference is that we use the `CountVectorizer()` instead of TF-IDF. This is because we do not want to down-weight the presence of an actor/director if he or she has acted or directed in relatively more movies. It doesn't make much intuitive sense."
      ],
      "metadata": {
        "id": "xbTwsN3OucMb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import CountVectorizer and create the count matrix\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "count = CountVectorizer(stop_words='english')\n",
        "count_matrix = count.fit_transform(df2_small['soup'])"
      ],
      "metadata": {
        "id": "HeP0JN0AuPuA"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cosine_sim2 = cosine_similarity(count_matrix, count_matrix)"
      ],
      "metadata": {
        "id": "oEI67xRXu-_A"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Reset index of our main DataFrame and construct reverse mapping as before\n",
        "\n",
        "indices = pd.Series(df2_small.index, index=df2_small['title'])"
      ],
      "metadata": {
        "id": "IfQXYK1FvKbf"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can now reuse our `get_recommendations()` function by passing in the new `cosine_sim2` matrix as your second argument."
      ],
      "metadata": {
        "id": "FoEKphhDvyfh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('The Dark Knight Rises', cosine_sim2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EfEZoSQVvTuk",
        "outputId": "c8ff7ca6-55b6-4d74-9253-630b4175ae3a"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "6933                  The Dark Knight\n",
              "6174                    Batman Begins\n",
              "5400                         Mitchell\n",
              "467                 Romeo Is Bleeding\n",
              "6575                     The Prestige\n",
              "3627                  An Innocent Man\n",
              "8663     Revenge of the Green Dragons\n",
              "8859          Kidnapping Mr. Heineken\n",
              "9053                     İtirazım Var\n",
              "2039    Beyond the Poseidon Adventure\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('The Dark Knight Rises')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "trX53P8Lv-ok",
        "outputId": "cd3afc7e-8652-485c-f05f-3630e84af65b"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "132                              Batman Forever\n",
              "6933                            The Dark Knight\n",
              "1122                             Batman Returns\n",
              "7603                 Batman: Under the Red Hood\n",
              "524                                      Batman\n",
              "7941                           Batman: Year One\n",
              "8205    Batman: The Dark Knight Returns, Part 1\n",
              "2587               Batman: Mask of the Phantasm\n",
              "8270    Batman: The Dark Knight Returns, Part 2\n",
              "6174                              Batman Begins\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('The Godfather', cosine_sim2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Of0o0zAGwLbm",
        "outputId": "f42f6b03-708f-4d4a-c907-75946b74f90e"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1590    The Godfather: Part III\n",
              "982      The Godfather: Part II\n",
              "5400                   Mitchell\n",
              "969              Apocalypse Now\n",
              "3288           Gardens of Stone\n",
              "4124                The Gambler\n",
              "5                          Heat\n",
              "380               Carlito's Way\n",
              "892         Glengarry Glen Ross\n",
              "1192              Donnie Brasco\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "get_recommendations('Transformers', cosine_sim2)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PM2JHvhYwQDp",
        "outputId": "be5be423-2b5b-4e5c-8858-78924d6b17b2"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "7276    Transformers: Revenge of the Fallen\n",
              "7847         Transformers: Dark of the Moon\n",
              "8625        Transformers: Age of Extinction\n",
              "2822                               Predator\n",
              "8661           Teenage Mutant Ninja Turtles\n",
              "8802                         Fantastic Four\n",
              "1804                     Six-String Samurai\n",
              "7160                              Replicant\n",
              "8350                            After Earth\n",
              "1120    Star Trek III: The Search for Spock\n",
              "Name: title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We see that our recommender has been successful in capturing more information due to more metadata and has given us (arguably) better recommendations. We can add additional features e.g production_company . We can also increase the weight of the director , by adding the feature multiple times in the soup."
      ],
      "metadata": {
        "id": "ctqMVS3Gwo2h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## COLLABORATIVE FILTERING"
      ],
      "metadata": {
        "id": "2iGFVI0xw_uf"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our content based engine suffers from some severe limitations. It is only capable of suggesting movies which are close to a certain movie. That is, it is not capable of capturing tastes and providing recommendations across genres.\n",
        "\n",
        "The engine also doesn't capture the tastes and biases of a user. Anyone querying the engine for recommendations based on a movie will receive the same recommendations for that movie, regardless of who she/he is.\n",
        "\n",
        "Therefore, in this section, we will use a technique called Collaborative Filtering to make recommendations to Movie Watchers. There are two types:\n",
        "\n",
        "- **User Based Filtering**:  These systems recommend products to a user that similar users have liked. For measuring the similarity between two users we can either use [pearson correlation](https://en.wikipedia.org/wiki/Pearson_correlation_coefficient) or cosine similarity.\n",
        "\n",
        "- **Item Based Filtering**:  Instead of measuring the similarity between users, the item-based CF recommends items based on their similarity with the items that the target user rated. Likewise, the similarity can be computed with Pearson Correlation or Cosine Similarity. However, several problems remain for this method. First, the main issue is scalability. The computation grows with both the customer and the product. The worst case complexity is O(mn) with m users and n items. In addition, sparsity is another concern."
      ],
      "metadata": {
        "id": "jgUAmxVOcVBa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Single Value Decomposition"
      ],
      "metadata": {
        "id": "5PJaZVkufuk_"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "One way to handle the scalability and sparsity issue created by CF is to leverage a **latent factor model** to capture the similarity between users and items. Essentially, we want to turn the recommendation problem into an optimization problem. We can view it as how good we are in predicting the rating for items given a user. One common metric is Root Mean Square Error (RMSE). The lower the RMSE, the better the performance.\n",
        "\n",
        "A latent factor describes a property or concept that a user or an item have.  For instance, for music, latent factor can refer to the genre that the music belongs to. SVD decreases the dimension of the utility matrix by extracting its latent factors. Essentially, we map each user and each item into a latent space with dimension r. Therefore, it helps us better understand the relationship between users and items as they become directly comparable. Th figure below illustrates this idea."
      ],
      "metadata": {
        "id": "fEA2TIyGf9Dc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=\"https://cdn-images-1.medium.com/max/800/1*GUw90kG2ltTd2k_iv3Vo0Q.png\">"
      ],
      "metadata": {
        "id": "t8RNFRRtgik2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# !pip install surprise"
      ],
      "metadata": {
        "id": "0ptumthehCG8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from surprise import Reader, Dataset, SVD\n",
        "from surprise.model_selection import cross_validate\n",
        "\n",
        "reader = Reader()\n",
        "\n",
        "data = Dataset.load_from_df(ratings_small[['userId', 'movieId', 'rating']], reader)\n",
        "model = SVD()\n",
        "\n",
        "# cross-validation with no. of kfold=5 (can be changed per your need)\n",
        "cross_validate(model, data, measures=['rmse', 'mae'], cv=5)"
      ],
      "metadata": {
        "id": "Vmgj8Z6GwTL6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b430084c-be87-416f-f8bd-74698d0f6275"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'test_rmse': array([0.88853923, 0.90538458, 0.9007669 , 0.89820937, 0.89224233]),\n",
              " 'test_mae': array([0.68277593, 0.69739135, 0.6941138 , 0.69241774, 0.68753374]),\n",
              " 'fit_time': (1.951413869857788,\n",
              "  3.590128183364868,\n",
              "  1.5880343914031982,\n",
              "  1.5169992446899414,\n",
              "  1.5588622093200684),\n",
              " 'test_time': (0.27403759956359863,\n",
              "  0.16588807106018066,\n",
              "  0.3256990909576416,\n",
              "  0.14185261726379395,\n",
              "  0.2867286205291748)}"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We get a mean Root Mean Sqaure Error of 0.89 approx which is more than good enough for our case. Let us now train on our dataset and arrive at predictions."
      ],
      "metadata": {
        "id": "HClgV52jjyJI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "trainset = data.build_full_trainset()\n",
        "model.fit(trainset)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0gBCf-IKiUOE",
        "outputId": "afedeb5d-b601-4ca1-dde1-4b35b2fc872e"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<surprise.prediction_algorithms.matrix_factorization.SVD at 0x7f68e127d850>"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let us pick user with user Id 1 and check the ratings she/he has given."
      ],
      "metadata": {
        "id": "CNIe3fnXkAGm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_small[ratings_small['userId'] == 1]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 677
        },
        "id": "Gm1gAqXEj8KI",
        "outputId": "6043107b-8224-487a-fd6e-3468f6e51487"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    userId  movieId  rating   timestamp\n",
              "0        1       31     2.5  1260759144\n",
              "1        1     1029     3.0  1260759179\n",
              "2        1     1061     3.0  1260759182\n",
              "3        1     1129     2.0  1260759185\n",
              "4        1     1172     4.0  1260759205\n",
              "5        1     1263     2.0  1260759151\n",
              "6        1     1287     2.0  1260759187\n",
              "7        1     1293     2.0  1260759148\n",
              "8        1     1339     3.5  1260759125\n",
              "9        1     1343     2.0  1260759131\n",
              "10       1     1371     2.5  1260759135\n",
              "11       1     1405     1.0  1260759203\n",
              "12       1     1953     4.0  1260759191\n",
              "13       1     2105     4.0  1260759139\n",
              "14       1     2150     3.0  1260759194\n",
              "15       1     2193     2.0  1260759198\n",
              "16       1     2294     2.0  1260759108\n",
              "17       1     2455     2.5  1260759113\n",
              "18       1     2968     1.0  1260759200\n",
              "19       1     3671     3.0  1260759117"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dfbf8d6a-af1e-40ed-91f6-4a2caa3dc03c\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>userId</th>\n",
              "      <th>movieId</th>\n",
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              "      <th>8</th>\n",
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              "      <th>9</th>\n",
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              "    <tr>\n",
              "      <th>10</th>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1</td>\n",
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              "      <td>1.0</td>\n",
              "      <td>1260759203</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1</td>\n",
              "      <td>1953</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759191</td>\n",
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              "      <th>13</th>\n",
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              "      <td>2105</td>\n",
              "      <td>4.0</td>\n",
              "      <td>1260759139</td>\n",
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              "    <tr>\n",
              "      <th>14</th>\n",
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              "      <th>15</th>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.predict(1, 302, 3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5J8TLq4bkHTX",
        "outputId": "1ecec24d-4fb3-435f-ca9c-201c1cfd53af"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Prediction(uid=1, iid=302, r_ui=3, est=2.6044335617957315, details={'was_impossible': False})"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "For movie with ID 302, we get an estimated prediction of `2.604`. The recommender engine doesn't care what the movie is or its contents. It purely works on the basis of the movie ID and tries to predict the ratings based on how other users have rated the movie."
      ],
      "metadata": {
        "id": "lSauds-vki9B"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# CONCLUSION"
      ],
      "metadata": {
        "id": "8AR30JqBlNv0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We createS recommenders using demographic , content- based and collaborative filtering. While demographic filtering is very elemantary and cannot be used practically, **Hybrid Systems** can take advantage of content-based and collaborative filtering as the two approaches are proved to be almost complimentary. This model was very baseline and only provides a fundamental framework to start with."
      ],
      "metadata": {
        "id": "rTvOy-4rlWRz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**REFERENCES** :\n",
        "\n",
        "1. [Hackernoon Tutorial](https://hackernoon.com/introduction-to-recommender-system-part-1-collaborative-filtering-singular-value-decomposition-44c9659c5e75)\n",
        "2. [Kaggle](https://www.kaggle.com/rounakbanik/movie-recommender-systems)"
      ],
      "metadata": {
        "id": "ILNHpiK7lgMn"
      }
    }
  ]
}