Movie_Recommendation_Engine_Solution_ipynb_Jilsy.ipynb

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        "<a href=\"https://colab.research.google.com/github/JilsyXavier/Interactive-Movie-Recommendation-System/blob/main/Movie_Recommendation_Engine_Solution_ipynb_Jilsy.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#Movie Recommendation Platform"
      ],
      "metadata": {
        "id": "__SBNEgNJMee"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "#Context\n",
        "\n",
        "Over the past two decades, there has been a monumental shift in how people access and consume video content. With the universal access to broadband internet, numerous platforms like YouTube, Netflix, HBO Go emerged and steadily grew to prominence.\n",
        "\n",
        "Although not a household name in itself, OTT is the exact technology that made the streaming revolution possible.\n",
        "\n",
        "OTT stands for Over The Top, refers to any video streaming service delivering content to the users over the internet, however there are subscription charges associated with the usage of such platforms such as PrimeVideo, Netflix, HotStart, Zee5, SonyLiv etc.\n",
        "But choosing your next movie to watch can still be a daunting task, even if you have access to all the platforms.\n",
        "\n",
        "“MyNextMovie” is a budding startup in the space of recommendations on top of various OTT platforms providing suggestions to its customer base regarding their next movie.\n",
        "Their major business is to create a recommendation layer on top of these OTT platforms so that they can make suitable recommendations to their customers, however, since they are in research mode right now, they would want to experiment with open source data first to understand the depth of the models which can be delivered by them.\n",
        "\n",
        "The data for this exercise is open source data which has been collected and made available from the MovieLens web site (http://movielens.org), a part of GroupLens Research The data sets were collected over various periods of time, depending on the size of the set.\n",
        "You have recently joined as a Data Scientist at  “MyNextMovie” and plan to help the existing team to set up a recommendation platform.\n",
        "\n",
        "\n",
        "https://www.kaggle.com/code/ayushimishra2809/movie-recommendation-system/data?select=ratings.csv\n"
      ],
      "metadata": {
        "id": "apdVS-NrJIrI"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "##Data Description\n",
        "\n",
        "The data consists of 105339 ratings applied over 10329 movies. The average rating is 3.5 and minimum and maximum rating is 0.5 and 5 respectively. There are 668 users who have given their ratings for 149532 movies.\n",
        "\n",
        "There are two data files which are provided:\n",
        "- Movies.csv\n",
        "  - movieId: ID assigned to a movie\n",
        "  - title: Title of a movie\n",
        "  - genres: pipe separated list of movie genres.\n",
        "\n",
        "\n",
        "- Ratings.csv\n",
        "  - userId: ID assigned to a user\n",
        "  - movieId: ID assigned to a movie\n",
        "  - rating: rating by a user to a movie\n",
        "  - Timestamp: time at which the rating was provided.\n",
        "\n"
      ],
      "metadata": {
        "id": "jbs82WRMJImu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "##Objective\n",
        "- Create a popularity based recommender system at a genre level. User will input a genre (g), minimum ratings threshold (t) for a movie and no. of recommendations(N) for which it should be recommended top N movies which are most popular within that genre (g) ordered by ratings in descending order where each movie has at least (t) reviews.\n",
        "\n",
        "- Create a content based recommender system which recommends top N movies based on similar movie(m) genres.\n",
        "\n",
        "- Create a collaborative based recommender system which recommends top N  movies based on “K” similar users for a target user “u”"
      ],
      "metadata": {
        "id": "vZBNseF4KPtx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Loading Libraries and Data"
      ],
      "metadata": {
        "id": "rhurnC0YT9Jz"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "D2cuGXkWQ8z2"
      },
      "outputs": [],
      "source": [
        "#importing necessary libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer. # for vectorizing genres\n",
        "from sklearn.metrics.pairwise import cosine_similarity #to find similarity\n",
        "from ipywidgets import * # to create interactive modules\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies=pd.read_csv('movies.csv') #movies dataset\n",
        "ratings=pd.read_csv('ratings.csv') #rating dataset"
      ],
      "metadata": {
        "id": "7ENfcEjuRWZZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Exploratory Data Analysis"
      ],
      "metadata": {
        "id": "9XoJdIgDRw0h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Understanding Data"
      ],
      "metadata": {
        "id": "wa7jZVddR1PT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head()"
      ],
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          "height": 206
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        "id": "We-cYtQAR3rO",
        "outputId": "ae895339-2bd0-4f40-ae32-3b0780e90194"
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      "execution_count": null,
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              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                        genres  \n",
              "0  Adventure|Animation|Children|Comedy|Fantasy  \n",
              "1                   Adventure|Children|Fantasy  \n",
              "2                               Comedy|Romance  \n",
              "3                         Comedy|Drama|Romance  \n",
              "4                                       Comedy  "
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "  "
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        "ratings.head()"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1       16     4.0  1217897793\n",
              "1       1       24     1.5  1217895807\n",
              "2       1       32     4.0  1217896246\n",
              "3       1       47     4.0  1217896556\n",
              "4       1       50     4.0  1217896523"
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      "cell_type": "code",
      "source": [
        "movies.info()"
      ],
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        },
        "id": "NXMZfXu9R_FK",
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      "execution_count": null,
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10329 entries, 0 to 10328\n",
            "Data columns (total 3 columns):\n",
            " #   Column   Non-Null Count  Dtype \n",
            "---  ------   --------------  ----- \n",
            " 0   movieId  10329 non-null  int64 \n",
            " 1   title    10329 non-null  object\n",
            " 2   genres   10329 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 242.2+ KB\n"
          ]
        }
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    {
      "cell_type": "code",
      "source": [
        "ratings.info()"
      ],
      "metadata": {
        "colab": {
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        "id": "zxCrW5h6R_Cy",
        "outputId": "7e0eb918-09d6-4ba0-f3c1-ab7d68056903"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 105339 entries, 0 to 105338\n",
            "Data columns (total 4 columns):\n",
            " #   Column     Non-Null Count   Dtype  \n",
            "---  ------     --------------   -----  \n",
            " 0   userId     105339 non-null  int64  \n",
            " 1   movieId    105339 non-null  int64  \n",
            " 2   rating     105339 non-null  float64\n",
            " 3   timestamp  105339 non-null  int64  \n",
            "dtypes: float64(1), int64(3)\n",
            "memory usage: 3.2 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- No missing values in both of the dataframes\n",
        "\n",
        "- Well types columns"
      ],
      "metadata": {
        "id": "3r8H8w7fSK2G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Understanding of distribution of the features available\n",
        "\n"
      ],
      "metadata": {
        "id": "LLsusK0VSWqh"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings.describe()"
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          "height": 300
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        "id": "B5fKiYdZSmgI",
        "outputId": "530aa28c-5074-4905-aa15-51f9da7b7c83"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              userId        movieId         rating     timestamp\n",
              "count  105339.000000  105339.000000  105339.000000  1.053390e+05\n",
              "mean      364.924539   13381.312477       3.516850  1.130424e+09\n",
              "std       197.486905   26170.456869       1.044872  1.802660e+08\n",
              "min         1.000000       1.000000       0.500000  8.285650e+08\n",
              "25%       192.000000    1073.000000       3.000000  9.711008e+08\n",
              "50%       383.000000    2497.000000       3.500000  1.115154e+09\n",
              "75%       557.000000    5991.000000       4.000000  1.275496e+09\n",
              "max       668.000000  149532.000000       5.000000  1.452405e+09"
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              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>1073.000000</td>\n",
              "      <td>3.000000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>2497.000000</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>1.115154e+09</td>\n",
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              "      <th>75%</th>\n",
              "      <td>557.000000</td>\n",
              "      <td>5991.000000</td>\n",
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              "      <td>1.275496e+09</td>\n",
              "    </tr>\n",
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              "      <th>max</th>\n",
              "      <td>668.000000</td>\n",
              "      <td>149532.000000</td>\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",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Average rating of a movie is 3.5"
      ],
      "metadata": {
        "id": "eT6wMGPaSrmg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Finding unique users and movies\n"
      ],
      "metadata": {
        "id": "mapu-14ISb-6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ratings['userId'].nunique()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkGu8BhQSvje",
        "outputId": "5f2a6038-81bb-4c22-f1e8-e91d2c99c486"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "668"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies['movieId'].nunique(), ratings['movieId'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bUr6VkdqSvgu",
        "outputId": "53d7669a-d08e-404a-a6c9-051795402ead"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10329, 10325)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 668 unique users rating 10325 movies, however total movies present are 10329"
      ],
      "metadata": {
        "id": "WoGLNOFzS9eC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Average rating  and Total movies at genre level.\n"
      ],
      "metadata": {
        "id": "yLyLLB50Sb8M"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies['genres']=movies['genres'].str.split(\"|\")"
      ],
      "metadata": {
        "id": "NsppWe71TICz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head()"
      ],
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        "colab": {
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        },
        "id": "mtmQs7VFTH_-",
        "outputId": "28c0ecc9-8154-4ea9-e678-3019a5349b6c"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                              genres  \n",
              "0  [Adventure, Animation, Children, Comedy, Fantasy]  \n",
              "1                     [Adventure, Children, Fantasy]  \n",
              "2                                  [Comedy, Romance]  \n",
              "3                           [Comedy, Drama, Romance]  \n",
              "4                                           [Comedy]  "
            ],
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "movies2=movies.explode('genres')"
      ],
      "metadata": {
        "id": "fH8M6EqyUXp6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies2.head(20)"
      ],
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        "colab": {
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        },
        "id": "ZceKX2KeUyIr",
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      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   movieId                               title     genres\n",
              "0        1                    Toy Story (1995)  Adventure\n",
              "0        1                    Toy Story (1995)  Animation\n",
              "0        1                    Toy Story (1995)   Children\n",
              "0        1                    Toy Story (1995)     Comedy\n",
              "0        1                    Toy Story (1995)    Fantasy\n",
              "1        2                      Jumanji (1995)  Adventure\n",
              "1        2                      Jumanji (1995)   Children\n",
              "1        2                      Jumanji (1995)    Fantasy\n",
              "2        3             Grumpier Old Men (1995)     Comedy\n",
              "2        3             Grumpier Old Men (1995)    Romance\n",
              "3        4            Waiting to Exhale (1995)     Comedy\n",
              "3        4            Waiting to Exhale (1995)      Drama\n",
              "3        4            Waiting to Exhale (1995)    Romance\n",
              "4        5  Father of the Bride Part II (1995)     Comedy\n",
              "5        6                         Heat (1995)     Action\n",
              "5        6                         Heat (1995)      Crime\n",
              "5        6                         Heat (1995)   Thriller\n",
              "6        7                      Sabrina (1995)     Comedy\n",
              "6        7                      Sabrina (1995)    Romance\n",
              "7        8                 Tom and Huck (1995)  Adventure"
            ],
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              "      <td>Comedy</td>\n",
              "    </tr>\n",
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              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Heat (1995)</td>\n",
              "      <td>Action</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Heat (1995)</td>\n",
              "      <td>Crime</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "merged_info=pd.merge(ratings,movies2,on=['movieId'],how=\"inner\")\n",
        "print(merged_info.groupby('genres').agg({\"title\":\"nunique\",\n",
        "                                         \"rating\":\"mean\"}).reset_index().rename(columns={\"title\":\"unique_movies_count\",\n",
        "                                                                                                     \"rating\":\"mean_rating\"}))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8YdtmRaRZhlq",
        "outputId": "f67d0c70-cac7-4c73-e9d4-18a78bdb3f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                genres  unique_movies_count  mean_rating\n",
            "0   (no genres listed)                    7     3.071429\n",
            "1               Action                 1736     3.451450\n",
            "2            Adventure                 1164     3.518027\n",
            "3            Animation                  400     3.635350\n",
            "4             Children                  540     3.439429\n",
            "5               Comedy                 3513     3.420996\n",
            "6                Crime                 1440     3.642392\n",
            "7          Documentary                  415     3.643035\n",
            "8                Drama                 5217     3.650266\n",
            "9              Fantasy                  670     3.500459\n",
            "10           Film-Noir                  195     3.913636\n",
            "11              Horror                 1001     3.281097\n",
            "12                IMAX                  152     3.641821\n",
            "13             Musical                  409     3.571962\n",
            "14             Mystery                  675     3.652043\n",
            "15             Romance                 1788     3.544255\n",
            "16              Sci-Fi                  858     3.454481\n",
            "17            Thriller                 2187     3.495561\n",
            "18                 War                  503     3.783202\n",
            "19             Western                  235     3.565687\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Unique genres, users and movie available\n",
        "\n"
      ],
      "metadata": {
        "id": "-bokbhegSb5D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "movies2['genres'].unique()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rO26ip3cZ5w4",
        "outputId": "29700fbc-ac39-4898-c77f-71cbd7c133e9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Adventure', 'Animation', 'Children', 'Comedy', 'Fantasy',\n",
              "       'Romance', 'Drama', 'Action', 'Crime', 'Thriller', 'Horror',\n",
              "       'Mystery', 'Sci-Fi', 'IMAX', 'War', 'Musical', 'Documentary',\n",
              "       'Western', 'Film-Noir', '(no genres listed)'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies2['genres'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rx0msIGTaE3i",
        "outputId": "71e337f9-18c1-4b18-98ef-27f11ebb6520"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "20"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- 20 different genre values, however one of the values is\n",
        "\n",
        "  '(no genres listed)'\n",
        "  \n",
        "  Hence, 19 unique Genres."
      ],
      "metadata": {
        "id": "XOkldrAfaGMn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Removing (no genres listed) from genres.\n",
        "movies2=movies2[movies2['genres']!=\"(no genres listed)\"]"
      ],
      "metadata": {
        "id": "fQE3eVYdoS2I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies2['genres'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rlEKdfl1oSrh",
        "outputId": "fb6760e3-b2f0-4c2c-9115-ccf10482a82e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "movies2['genres'].unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzWWwoWZE45N",
        "outputId": "46c177c9-5ae4-4695-899b-2077767d938f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Adventure', 'Animation', 'Children', 'Comedy', 'Fantasy',\n",
              "       'Romance', 'Drama', 'Action', 'Crime', 'Thriller', 'Horror',\n",
              "       'Mystery', 'Sci-Fi', 'IMAX', 'War', 'Musical', 'Documentary',\n",
              "       'Western', 'Film-Noir'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "699MUaj5E8Pe"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 1. Popularity based Recommendations"
      ],
      "metadata": {
        "id": "aW_kTuYGRc9E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#User Defined Function to display topN movies to be recommended\n",
        "\n",
        "def TopNPopularMovies(genre,num_ratings_threshold,topN=5):\n",
        "\n",
        "  #Calculating mean of ratings and number of ratings\n",
        "  popularity_df=merged_info.groupby(['genres','title']).agg({'rating':[\"mean\",\"size\"]}).reset_index()\n",
        "  popularity_df.columns=['genre','title','ratings_mean','ratings_counts']\n",
        "\n",
        "  #Filtering desired genre and number of reviews threshold\n",
        "  topN_recommendations=popularity_df[(popularity_df['genre']==genre) & (popularity_df['ratings_counts']>=num_ratings_threshold)].sort_values(by=['ratings_mean'],ascending=False).head(topN)\n",
        "\n",
        "  #Refactoring output\n",
        "  topN_recommendations['S.No']=list(range(1,len(topN_recommendations)+1))\n",
        "  topN_recommendations.index=range(len(topN_recommendations))\n",
        "  topN_recommendations.columns=['Genre','Movie Title','Average Movie Rating','Num Reviews','S.No']\n",
        "  return topN_recommendations[['S.No','Movie Title','Average Movie Rating','Num Reviews']]\n"
      ],
      "metadata": {
        "id": "fU-CrKDdRvIf"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing Function : Try 1\n",
        "genre=\"Action\"\n",
        "num_ratings_threshold=100\n",
        "topN=10\n",
        "TopNPopularMovies(genre=genre,\n",
        "                  num_ratings_threshold=num_ratings_threshold,\n",
        "                  topN=topN)"
      ],
      "metadata": {
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          "height": 363
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        "id": "onCHJu_aeGaP",
        "outputId": "a34754d1-feb2-4231-82d5-8bb0069f9f11"
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      "execution_count": null,
      "outputs": [
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              "   S.No                                        Movie Title  \\\n",
              "0     1                                 Matrix, The (1999)   \n",
              "1     2  Star Wars: Episode V - The Empire Strikes Back...   \n",
              "2     3  Raiders of the Lost Ark (Indiana Jones and the...   \n",
              "3     4                                   Inception (2010)   \n",
              "4     5          Star Wars: Episode IV - A New Hope (1977)   \n",
              "5     6                                  Fight Club (1999)   \n",
              "6     7                                Blade Runner (1982)   \n",
              "7     8                         Princess Bride, The (1987)   \n",
              "8     9                                      Aliens (1986)   \n",
              "9    10                            Dark Knight, The (2008)   \n",
              "\n",
              "   Average Movie Rating  Num Reviews  \n",
              "0              4.264368          261  \n",
              "1              4.228070          228  \n",
              "2              4.212054          224  \n",
              "3              4.189320          103  \n",
              "4              4.188645          273  \n",
              "5              4.188406          207  \n",
              "6              4.169872          156  \n",
              "7              4.163743          171  \n",
              "8              4.146497          157  \n",
              "9              4.141732          127  "
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      "cell_type": "code",
      "source": [
        "#Testing Function : Try 2\n",
        "\n",
        "genre=\"Comedy\"\n",
        "num_ratings_threshold=50\n",
        "topN=5\n",
        "TopNPopularMovies(genre=genre,\n",
        "                  num_ratings_threshold=num_ratings_threshold,\n",
        "                  topN=topN)"
      ],
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          "data": {
            "text/plain": [
              "   S.No                                 Movie Title  Average Movie Rating  \\\n",
              "0     1      Monty Python and the Holy Grail (1975)              4.301948   \n",
              "1     2                                Fargo (1996)              4.271144   \n",
              "2     3  Life Is Beautiful (La Vita è bella) (1997)              4.253425   \n",
              "3     4                           Sting, The (1973)              4.207792   \n",
              "4     5                           Annie Hall (1977)              4.205882   \n",
              "\n",
              "   Num Reviews  \n",
              "0          154  \n",
              "1          201  \n",
              "2           73  \n",
              "3           77  \n",
              "4           68  "
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              "          document.querySelector('#df-02a25307-c09f-4174-abd7-7799602e16c8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-02a25307-c09f-4174-abd7-7799602e16c8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Content based Recommendations"
      ],
      "metadata": {
        "id": "iWFKJ7f0Rc5H"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Content considered here will be genres, so we need to convert the genres to vectors, there can be multiple approached to perform this however we are going to use TF-IDF vectorizer to achieve the same."
      ],
      "metadata": {
        "id": "oWsgruCUFBFo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Creating a string of genres in order to apply TF-IDF further\n",
        "movies3=movies2.groupby(\"title\").agg({\"genres\":lambda x : \" \".join(list(x))}).reset_index()\n",
        "movies3"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "Vh2jLS-pE_jB",
        "outputId": "f40fb01c-6398-46dc-8bff-6c7ab7d68770"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               title  \\\n",
              "0                                         '71 (2014)   \n",
              "1            'Hellboy': The Seeds of Creation (2004)   \n",
              "2                             'Round Midnight (1986)   \n",
              "3                          'Til There Was You (1997)   \n",
              "4                                 'burbs, The (1989)   \n",
              "...                                              ...   \n",
              "10315  loudQUIETloud: A Film About the Pixies (2006)   \n",
              "10316                                     xXx (2002)   \n",
              "10317                 xXx: State of the Union (2005)   \n",
              "10318                          ¡Three Amigos! (1986)   \n",
              "10319      À nous la liberté (Freedom for Us) (1931)   \n",
              "\n",
              "                                            genres  \n",
              "0                        Action Drama Thriller War  \n",
              "1      Action Adventure Comedy Documentary Fantasy  \n",
              "2                                    Drama Musical  \n",
              "3                                    Drama Romance  \n",
              "4                                           Comedy  \n",
              "...                                            ...  \n",
              "10315                                  Documentary  \n",
              "10316                        Action Crime Thriller  \n",
              "10317                        Action Crime Thriller  \n",
              "10318                               Comedy Western  \n",
              "10319                               Comedy Musical  \n",
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              "      <th>2</th>\n",
              "      <td>'Round Midnight (1986)</td>\n",
              "      <td>Drama Musical</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>'Til There Was You (1997)</td>\n",
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              "      <th>4</th>\n",
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              "      <td>¡Three Amigos! (1986)</td>\n",
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              "      <th>10319</th>\n",
              "      <td>À nous la liberté (Freedom for Us) (1931)</td>\n",
              "      <td>Comedy Musical</td>\n",
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              "<p>10320 rows × 2 columns</p>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Running TF-IDF using n grams from 1 to 3, this can be experimented with as well\n",
        "tf = TfidfVectorizer(analyzer='word',ngram_range=(1, 3),min_df=0, stop_words='english')\n",
        "tfidf_matrix = tf.fit_transform(movies3['genres'])\n",
        "#Computing cosine similarity\n",
        "cosine_sim = cosine_similarity(tfidf_matrix, tfidf_matrix)\n"
      ],
      "metadata": {
        "id": "1KP5Wgw6E_gD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "movies.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Ye16qQ1lHtOe",
        "outputId": "fb8d36fa-76d0-4b5b-b4d3-12cd8774ec08"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   movieId                               title  \\\n",
              "0        1                    Toy Story (1995)   \n",
              "1        2                      Jumanji (1995)   \n",
              "2        3             Grumpier Old Men (1995)   \n",
              "3        4            Waiting to Exhale (1995)   \n",
              "4        5  Father of the Bride Part II (1995)   \n",
              "\n",
              "                                              genres  \n",
              "0  [Adventure, Animation, Children, Comedy, Fantasy]  \n",
              "1                     [Adventure, Children, Fantasy]  \n",
              "2                                  [Comedy, Romance]  \n",
              "3                           [Comedy, Drama, Romance]  \n",
              "4                                           [Comedy]  "
            ],
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-547168d7-32cf-43be-868b-b6542c5731a9');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "#User defined function to recommend similar movies\n",
        "def recommendations_genre(movie_df,similarity_matrix,movie_title,topN=5):\n",
        "    #Target movie title\n",
        "    titles = movie_df['title']\n",
        "    #Index of all other movies\n",
        "    indices = pd.Series(movie_df.index, index=movie_df['title'])\n",
        "    #Index of target movie\n",
        "    index = indices[movie_title]\n",
        "    #Generating cosine similarity scores\n",
        "    cosine_similarity_scores = list(enumerate(similarity_matrix[index]))\n",
        "    #Descending order sorting based on scores\n",
        "    cosine_similarity_scores = sorted(cosine_similarity_scores, key=lambda x: x[1], reverse=True)\n",
        "    #Selecting topN movies to be recommended ( it can contain the same movie as well, hence choosing topN+2)\n",
        "    cosine_similarity_scores = cosine_similarity_scores[1:topN+2]\n",
        "    #Extracting matched movies\n",
        "    matching_movies = [i[0] for i in cosine_similarity_scores]\n",
        "    matches_df=movie_df.iloc[matching_movies]\n",
        "    matches_df=matches_df[matches_df['title']!=movie_title]\n",
        "    #Refactoring output\n",
        "    matches_df.rename(columns={'title':'Movie Title'},inplace=True)\n",
        "    matches_df['S.No']=range(1,len(matches_df)+1)\n",
        "    matches_df.index=range(len(matches_df))\n",
        "    return matches_df[['S.No','Movie Title']].head(topN)"
      ],
      "metadata": {
        "id": "afy-dx-mE_c8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing above function: Try 1\n",
        "movie_title=\"Toy Story (1995)\"\n",
        "topN=10\n",
        "recommendations_genre(movie_df=movies3,\n",
        "                      similarity_matrix=cosine_sim,\n",
        "                      movie_title=movie_title,\n",
        "                      topN=topN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "hTcNWVrFINow",
        "outputId": "cf26b290-9f5d-4bff-ceb0-d17cd0ddeb66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                  title  \\\n",
              "550                                         Antz (1998)   \n",
              "635   Asterix and the Vikings (Astérix et les Viking...   \n",
              "1332                              Boxtrolls, The (2014)   \n",
              "2752  DuckTales: The Movie - Treasure of the Lost La...   \n",
              "2883                   Emperor's New Groove, The (2000)   \n",
              "6133                              Monsters, Inc. (2001)   \n",
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              "\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing above function: Try 2\n",
        "movie_title=\"Avatar (2009)\"\n",
        "topN=10\n",
        "recommendations_genre(movie_df=movies3,\n",
        "                      similarity_matrix=cosine_sim,\n",
        "                      movie_title=movie_title,\n",
        "                      topN=topN)"
      ],
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        "id": "zEOhp1H_IUZ_",
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      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "                                           title                        genres\n",
              "394               Amazing Spider-Man, The (2012)  Action Adventure Sci-Fi IMAX\n",
              "687                         Avengers, The (2012)  Action Adventure Sci-Fi IMAX\n",
              "1619  Captain America: The Winter Soldier (2014)  Action Adventure Sci-Fi IMAX\n",
              "2901                         Ender's Game (2013)  Action Adventure Sci-Fi IMAX\n",
              "3694                             Godzilla (2014)  Action Adventure Sci-Fi IMAX\n",
              "4394     Hunger Games: Catching Fire, The (2013)  Action Adventure Sci-Fi IMAX\n",
              "4861                          John Carter (2012)  Action Adventure Sci-Fi IMAX\n",
              "6632                             Oblivion (2013)  Action Adventure Sci-Fi IMAX\n",
              "6847                          Pacific Rim (2013)  Action Adventure Sci-Fi IMAX\n",
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              "                                                     [key], {});\n",
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              "\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "- One more way to vectorize the genres can be done using Binarization of genres"
      ],
      "metadata": {
        "id": "9IAO59N_KKSY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Using pivot table to create a one hot encoded vector for all the movies\n",
        "movies4=movies2.pivot_table(index=['title'],columns=['genres']).fillna(0).applymap(lambda x: int(x>0)).reset_index()\n",
        "\n",
        "movies4.index=range(0,len(movies4))\n",
        "new_columns=['title']+list(map(lambda x : x[1] , movies4.columns))[1:]\n",
        "movies4.columns=new_columns\n",
        "\n",
        "#Generating new data frame representing binarized genres\n",
        "movies5=pd.merge(movies[['title','movieId']],movies4)\n",
        "movies5.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "SePr1oPUKYCX",
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      "execution_count": null,
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            "text/plain": [
              "                                title  movieId  Action  Adventure  Animation  \\\n",
              "0                    Toy Story (1995)        1       0          1          1   \n",
              "1                      Jumanji (1995)        2       0          1          0   \n",
              "2             Grumpier Old Men (1995)        3       0          0          0   \n",
              "3            Waiting to Exhale (1995)        4       0          0          0   \n",
              "4  Father of the Bride Part II (1995)        5       0          0          0   \n",
              "\n",
              "   Children  Comedy  Crime  Documentary  Drama  ...  Film-Noir  Horror  IMAX  \\\n",
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              "4         0       1      0            0      0  ...          0       0     0   \n",
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              "3        0        0        1       0         0    0        0  \n",
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              "\n",
              "[5 rows x 21 columns]"
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              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jumanji (1995)</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Grumpier Old Men (1995)</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Waiting to Exhale (1995)</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Father of the Bride Part II (1995)</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 21 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Generating new similarity metrics\n",
        "movie_genre_vector = movies5.loc[:,\"Action\":].values\n",
        "cosine_sim2 = cosine_similarity(movie_genre_vector, movie_genre_vector)"
      ],
      "metadata": {
        "id": "HMtThAEpKc1F"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing the same function,but using different vectorized dataframe and similarity metrics: Try 1\n",
        "\n",
        "movie_title=\"Toy Story (1995)\"\n",
        "topN=10\n",
        "recommendations_genre(movie_df=movies5,\n",
        "                      similarity_matrix=cosine_sim2,\n",
        "                      movie_title=movie_title,\n",
        "                      topN=topN)"
      ],
      "metadata": {
        "colab": {
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          "height": 821
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        "id": "Xu-FKc8_KGwK",
        "outputId": "7f522b2a-b277-4128-de2a-16e04e572c95"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                                                  title  movieId  Action  \\\n",
              "1816                                        Antz (1998)     2294       0   \n",
              "2497                                 Toy Story 2 (1999)     3114       0   \n",
              "2968     Adventures of Rocky and Bullwinkle, The (2000)     3754       0   \n",
              "3167                   Emperor's New Groove, The (2000)     4016       0   \n",
              "3812                              Monsters, Inc. (2001)     4886       0   \n",
              "6617  DuckTales: The Movie - Treasure of the Lost La...    33463       0   \n",
              "6998                                   Wild, The (2006)    45074       0   \n",
              "7383                             Shrek the Third (2007)    53121       0   \n",
              "7987                     Tale of Despereaux, The (2008)    65577       0   \n",
              "9215  Asterix and the Vikings (Astérix et les Viking...    91355       0   \n",
              "\n",
              "      Adventure  Animation  Children  Comedy  Crime  Documentary  Drama  ...  \\\n",
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              "7987          1          1         1       1      0            0      0  ...   \n",
              "9215          1          1         1       1      0            0      0  ...   \n",
              "\n",
              "      Film-Noir  Horror  IMAX  Musical  Mystery  Romance  Sci-Fi  Thriller  \\\n",
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              "9215          0       0     0        0        0        0       0         0   \n",
              "\n",
              "      War  Western  \n",
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              "6998    0        0  \n",
              "7383    0        0  \n",
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              "9215    0        0  \n",
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              "      <th>title</th>\n",
              "      <th>movieId</th>\n",
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              "      <td>Antz (1998)</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing the same function,but using different vectorized dataframe and similarity metrics: Try 2\n",
        "\n",
        "movie_title=\"Avatar (2009)\"\n",
        "topN=10\n",
        "recommendations_genre(movie_df=movies5,\n",
        "                      similarity_matrix=cosine_sim2,\n",
        "                      movie_title=movie_title,\n",
        "                      topN=topN)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 734
        },
        "id": "9JkXZUAaKGs4",
        "outputId": "93266564-e10e-4d05-d35f-5a979c851a39"
      },
      "execution_count": null,
      "outputs": [
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              "                                           title  movieId  Action  Adventure  \\\n",
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              "                                                     [key], {});\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 32
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Results might be similar as there is just genre which we have considered, however had there been additional data points such as plot description, actors, directors etc., then those can be addedd to the text and TF-IDF Vectorizer could have been a more sophisticated approach, so in the final UI, only TF-IDF based will be demonstrated."
      ],
      "metadata": {
        "id": "lg54TwqnNJnq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Collaborative Filtering based Recommendations"
      ],
      "metadata": {
        "id": "O7RW9tSeRmtD"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "###Algorithm to make recommendations\n",
        "\n",
        "- Select the target user ID and extract history\n",
        "\n",
        "- Find the subset users who have watched the same movies.\n",
        "\n",
        "- Sort them based on the number of movies they have watched compared to our target user\n",
        "\n",
        "- Select \"U\" users based on a specificed criteria, here the criteria is that the users selected for candidates must have rated atleast \"p\" percent of the movies which our target user has rated.\n",
        "\n",
        "- Out of the U users, find similarity using any appropriate method\n",
        "   - Cosine Similariy\n",
        "  \n",
        "   - Pearson Correlation\n",
        "\n",
        "   - etc.\n",
        "\n",
        "- Filter out top \"K\" users having highest similarity.\n",
        "\n",
        "- List down all the movies reviewed by these \"K\" similar users after removing the movies watched by target user and sort them in descending order of average rating\n",
        "\n",
        "-  Recommend top \"N\" movies\n"
      ],
      "metadata": {
        "id": "VdlGahHFe0Ik"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Movies with IDs\n",
        "movies6=movies5[['title','movieId']]\n",
        "movies6.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
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        "id": "1Piqz3pQhPFI",
        "outputId": "8728e1ed-dd97-4c31-ff36-491411c73be2"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                title  movieId\n",
              "0                    Toy Story (1995)        1\n",
              "1                      Jumanji (1995)        2\n",
              "2             Grumpier Old Men (1995)        3\n",
              "3            Waiting to Exhale (1995)        4\n",
              "4  Father of the Bride Part II (1995)        5"
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      "source": [
        "#Ratings\n",
        "ratings.head()\n"
      ],
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        },
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      "execution_count": null,
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        {
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            "text/plain": [
              "   userId  movieId  rating   timestamp\n",
              "0       1       16     4.0  1217897793\n",
              "1       1       24     1.5  1217895807\n",
              "2       1       32     4.0  1217896246\n",
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    {
      "cell_type": "code",
      "source": [
        "#Function to fetch user history\n",
        "def FetchUserHistory(userid):\n",
        "  user_ratings=ratings[ratings['userId']==userid]\n",
        "  user_history=pd.merge(user_ratings,movies6,how=\"inner\",on=\"movieId\").sort_values('movieId')\n",
        "  return user_history"
      ],
      "metadata": {
        "id": "CVAAOjVWhTKv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def GenerateSimilarUsersRecommendations(target_user_id,p,k,topN):\n",
        "\n",
        "  #Fetching user history\n",
        "  user_history=FetchUserHistory(userid=target_user_id)\n",
        "\n",
        "  #Finding all users who have seen similar movies\n",
        "  similar_users = ratings[ratings['movieId'].isin(user_history['movieId'].tolist())]\n",
        "\n",
        "  #Finding num of movies reviewed by candidate similar users\n",
        "  similar_uses_num_movie_reviewed=similar_users.groupby(\"userId\").agg({\"movieId\":\"nunique\"}).reset_index().rename(columns={'movieId':'num_movies_reviewed'})\\\n",
        "                                                                                          .sort_values(by='num_movies_reviewed',ascending=False)\n",
        "  #Removing target user ID from the similar user subset\n",
        "  similar_uses_num_movie_reviewed=similar_uses_num_movie_reviewed[similar_uses_num_movie_reviewed['userId']!=target_user_id]\n",
        "\n",
        "  #Movies Rated by Target user\n",
        "  movies_rated_by_target_user=user_history['movieId'].nunique()\n",
        "\n",
        "  #Min movies rated by candidate similar users : Criteria 1 : Should be atleast 50% ( p is set to 50)\n",
        "  min_movies_rated_threshold=int(movies_rated_by_target_user*(p/100))\n",
        "\n",
        "  #Filtered candidates of similar users\n",
        "  similar_uses_num_movie_reviewed=similar_uses_num_movie_reviewed[similar_uses_num_movie_reviewed['num_movies_reviewed']>=min_movies_rated_threshold]\n",
        "\n",
        "  # Ratings Data for filtered candidates of similar users\n",
        "  bestN_similar_users=similar_users[similar_users['userId'].isin(list(set(similar_uses_num_movie_reviewed.userId)))]\n",
        "\n",
        "  #Creating vector representation for target user\n",
        "  user_vector=user_history.pivot(index='userId',columns=['movieId'],values=['rating'])\n",
        "  user_vector.columns=[str(each[1]) for each in user_vector.columns]\n",
        "\n",
        "  #Creating vector representation for candidate similar users\n",
        "  bestN_vectors=bestN_similar_users.pivot(index='userId',columns=['movieId'],values=['rating']).fillna(0)\n",
        "  bestN_vectors.columns=[str(each[1]) for each in bestN_vectors.columns]\n",
        "\n",
        "  #Matching column names\n",
        "  for each in set(user_vector.columns)-set(bestN_vectors.columns):\n",
        "    bestN_vectors[each]=0.0\n",
        "\n",
        "  bestN_vectors=bestN_vectors[user_vector.columns]\n",
        "\n",
        "  #Finding Cosine Similarity Amongst all the candidate users\n",
        "  similarity_df=pd.DataFrame(cosine_similarity(user_vector,bestN_vectors)).T\n",
        "  similarity_df.index=bestN_vectors.index\n",
        "  similarity_df.columns=['similarity_score']\n",
        "\n",
        "  #Filtering out top K similar users\n",
        "  topK_user_ids=list(similarity_df.sort_values('similarity_score',ascending=False).index[:k])\n",
        "\n",
        "  #Selecting ratings data of similar K users\n",
        "  similar_users_subset=ratings[(ratings['userId'].isin(topK_user_ids))]\n",
        "\n",
        "\n",
        "  #Selecting movies other than the ones rated by our target user\n",
        "  similar_users_subset=similar_users_subset[~similar_users_subset['movieId'].isin(user_history['movieId'])]\n",
        "\n",
        "  #Sorting the movies based on best average ratings of similar users and choosing topN movies\n",
        "  best_recommended_movies=similar_users_subset.groupby('movieId').mean()[['rating']].reset_index().sort_values('rating',ascending=False)[:topN]\n",
        "\n",
        "  #Output topN recommendations\n",
        "  output_df=pd.merge(best_recommended_movies,movies6)\n",
        "  output_df.columns=['Movie ID','Average Rating', \"Movie Title\"]\n",
        "  output_df['S.No']=range(1,len(output_df)+1)\n",
        "\n",
        "  #User History\n",
        "  user_history.rename(columns={'title':\"Movie Title\"},inplace=True)\n",
        "  user_history=user_history.sort_values('rating',ascending=False)\n",
        "  user_history['S.No']=range(1,len(user_history)+1)\n",
        "\n",
        "  return user_history[['S.No',\"Movie Title\"]],output_df[['S.No',\"Movie Title\"]]\n",
        "\n"
      ],
      "metadata": {
        "id": "bxqfH5DrhmxK"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing the above function: Try 1\n",
        "\n",
        "target_user_id=2\n",
        "p=90\n",
        "k=100\n",
        "topN=10\n",
        "\n",
        "history,recommendations=GenerateSimilarUsersRecommendations(target_user_id=target_user_id,\n",
        "                                    p=p,\n",
        "                                    k=k,\n",
        "                                    topN=topN)"
      ],
      "metadata": {
        "id": "m8iQy69oexOi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "history"
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        "outputId": "841bb66d-4db8-424f-8100-0b6889d8c9e4"
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      "execution_count": null,
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              "    S.No                                 Movie Title\n",
              "0      1                            Toy Story (1995)\n",
              "26     2                      Time to Kill, A (1996)\n",
              "4      3                Sense and Sensibility (1995)\n",
              "7      4                     Dead Man Walking (1995)\n",
              "10     5                   Mr. Holland's Opus (1995)\n",
              "17     6                                Fargo (1996)\n",
              "15     7                      River Wild, The (1994)\n",
              "27     8  Willy Wonka & the Chocolate Factory (1971)\n",
              "25     9                           Phenomenon (1996)\n",
              "24    10                 Nutty Professor, The (1996)\n",
              "22    11        Independence Day (a.k.a. ID4) (1996)\n",
              "21    12                              Twister (1996)\n",
              "20    13            James and the Giant Peach (1996)\n",
              "19    14                  Mission: Impossible (1996)\n",
              "16    15                   Executive Decision (1996)\n",
              "14    16   Star Wars: Episode IV - A New Hope (1977)\n",
              "12    17                         Broken Arrow (1996)\n",
              "11    18                           Juror, The (1996)\n",
              "9     19           Postman, The (Postino, Il) (1994)\n",
              "8     20                     Mighty Aphrodite (1995)\n",
              "3     21                                Nixon (1995)\n",
              "28    22             Star Trek: First Contact (1996)\n",
              "13    23  Rumble in the Bronx (Hont faan kui) (1995)\n",
              "18    24                           Sgt. Bilko (1996)\n",
              "6     25   Twelve Monkeys (a.k.a. 12 Monkeys) (1995)\n",
              "5     26                    Leaving Las Vegas (1995)\n",
              "2     27          Father of the Bride Part II (1995)\n",
              "1     28                     Grumpier Old Men (1995)\n",
              "23    29                       Cable Guy, The (1996)"
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            "text/plain": [
              "   S.No                    Movie Title\n",
              "0     1                  Mother (1996)\n",
              "1     2   Up Close and Personal (1996)\n",
              "2     3                  Ransom (1996)\n",
              "3     4               Rock, The (1996)\n",
              "4     5           Birdcage, The (1996)\n",
              "5     6                    Heat (1995)\n",
              "6     7           Happy Gilmore (1996)\n",
              "7     8          Down Periscope (1996)\n",
              "8     9  Muppet Treasure Island (1996)\n",
              "9    10                 Sabrina (1995)"
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            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Testing the above function: Try 2\n",
        "target_user_id=4\n",
        "p=50\n",
        "k=100\n",
        "topN=10\n",
        "history,recommendations=GenerateSimilarUsersRecommendations(target_user_id=target_user_id,\n",
        "                                    p=p,\n",
        "                                    k=k,\n",
        "                                    topN=topN)"
      ],
      "metadata": {
        "id": "CUXyZISamwuh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "vmboIxC8mwsR",
        "outputId": "289d26cf-1e81-46a4-ee93-dcd29c7e31e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     S.No                                    Movie Title\n",
              "123     1                   M*A*S*H (a.k.a. MASH) (1970)\n",
              "93      2                                  Tarzan (1999)\n",
              "26      3  Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)\n",
              "101     4                       Color Purple, The (1985)\n",
              "29      5                   Love in the Afternoon (1957)\n",
              "..    ...                                            ...\n",
              "106   120                         Dudley Do-Right (1999)\n",
              "97    121                             Mystery Men (1999)\n",
              "73    122                       Big Lebowski, The (1998)\n",
              "96    123                             Lake Placid (1999)\n",
              "92    124   Austin Powers: The Spy Who Shagged Me (1999)\n",
              "\n",
              "[124 rows x 2 columns]"
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              "      <th>123</th>\n",
              "      <td>1</td>\n",
              "      <td>M*A*S*H (a.k.a. MASH) (1970)</td>\n",
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              "      <td>Tarzan (1999)</td>\n",
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              "      <th>26</th>\n",
              "      <td>3</td>\n",
              "      <td>Sunset Blvd. (a.k.a. Sunset Boulevard) (1950)</td>\n",
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              "      <td>Austin Powers: The Spy Who Shagged Me (1999)</td>\n",
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              "<p>124 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-97675fa2-ac29-4d9d-b6f8-108d2a3495bd');\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",
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              "  "
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          "metadata": {},
          "execution_count": 41
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    {
      "cell_type": "code",
      "source": [
        "recommendations"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "srxwcymhm9Ch",
        "outputId": "50ca1ec7-1b26-4f7e-e43f-20c656cd5f1e"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   S.No                      Movie Title\n",
              "0     1           Joe's Apartment (1996)\n",
              "1     2           Life as a House (2001)\n",
              "2     3                   Dracula (1931)\n",
              "3     4             Tortilla Soup (2001)\n",
              "4     5  Bedknobs and Broomsticks (1971)\n",
              "5     6     Three Caballeros, The (1945)\n",
              "6     7                 Labyrinth (1986)\n",
              "7     8          Double Happiness (1994)\n",
              "8     9               Eve's Bayou (1997)\n",
              "9    10                     21 Up (1977)"
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              "      <td>Three Caballeros, The (1945)</td>\n",
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              "      <td>7</td>\n",
              "      <td>Labyrinth (1986)</td>\n",
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              "      \n",
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              "      gap: 12px;\n",
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              "\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",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 42
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Interactions"
      ],
      "metadata": {
        "id": "T-J8-9azRmqF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "- We will be using Ipywidgets in order to create an interactive module\n"
      ],
      "metadata": {
        "id": "ytj8z6dXNdzy"
      }
    },
    {
      "cell_type": "code",
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        "#Popularity based Recommendations\n",
        "\n",
        "#Inputs for the functions\n",
        "genres=Dropdown(options=list(set(movies2.genres)),description=\"Genre\",style= {'description_width': 'initial'})\n",
        "num_reviews=IntText(description=\"Min Reviews\",style= {'description_width': 'initial'})\n",
        "num_recommendations_1=IntText(description=\"Num Recommendations\",style= {'description_width': 'initial'})\n",
        "#Stacking inputs in a tab\n",
        "b1=Button(description='Recommend Me',style= {'description_width': 'initial'})\n",
        "h1=HBox([num_reviews, num_recommendations_1])\n",
        "popularity_tab=VBox([genres,h1,b1])\n",
        "\n",
        "\n",
        "#Content based Recommendations\n",
        "\n",
        "#Inputs for the functions\n",
        "title=Textarea(description=\"Movie Title\",style= {'description_width': 'initial'})\n",
        "num_recommendations_2=IntText(description=\"Num Recommendations\",style= {'description_width': 'initial'})\n",
        "#Stacking inputs in a tab\n",
        "h2=HBox([title, num_recommendations_2])\n",
        "b2=Button(description='Recommend Me',style= {'description_width': 'initial'})\n",
        "content_based_tab=VBox([h2,b2])\n",
        "\n",
        "\n",
        "#Collaborative based Recommendations\n",
        "\n",
        "#Inputs for the functions\n",
        "user_id=IntText(description=\"Target User ID\",style= {'description_width': 'initial'})\n",
        "p=IntText(description=\"Percentage of Movies Reviewed\",style= {'description_width': 'initial'})\n",
        "k=IntText(description=\"K similar users\",style= {'description_width': 'initial'})\n",
        "num_recommendations_3=IntText(description=\"Num Recommendations\",style= {'description_width': 'initial'})\n",
        "\n",
        "#Stacking inputs in a tab\n",
        "h3=HBox([user_id, num_recommendations_3])\n",
        "h4=HBox([p, k])\n",
        "b3=Button(description='Recommend Me',style= {'description_width': 'initial'})\n",
        "collaborative_tab=VBox([h3,h4,b3])\n",
        "\n",
        "\n",
        "#Creating tabs\n",
        "all_tabs=[popularity_tab,content_based_tab,collaborative_tab]\n",
        "ui = widgets.Tab(all_tabs)\n",
        "\n",
        "#Setting tab titles\n",
        "names = ['Popularity Based', 'Content Based', 'Collaborative Filtering']\n",
        "[ui.set_title(i, title) for i, title in enumerate(names)]\n",
        "\n",
        "#Display tabs\n",
        "display(ui)"
      ],
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          "height": 175,
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Tab(children=(VBox(children=(Dropdown(description='Genre', options=('Horror', 'Thriller', 'Action', 'War', 'Fi…"
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      "cell_type": "code",
      "source": [
        "#Setting up events to respond on click of button for all three tabs individually.\n",
        "\n",
        "#Popularity based Recommendations\n",
        "def on_b1_clicked(b):\n",
        "  global output\n",
        "  output=TopNPopularMovies(genre=genres.value,\n",
        "                  num_ratings_threshold=num_reviews.value,\n",
        "                  topN=num_recommendations_1.value)\n",
        "b1.on_click(on_b1_clicked)\n",
        "\n",
        "#Content based Recommendations\n",
        "def on_b2_clicked(b):\n",
        "  global output\n",
        "  result=recommendations_genre(movie_df=movies5,\n",
        "                               similarity_matrix=cosine_sim2,\n",
        "\n",
        "                               movie_title=title.value,\n",
        "                  topN=num_recommendations_2.value)\n",
        "  output=result\n",
        "b2.on_click(on_b2_clicked)\n",
        "\n",
        "#Collaborative based Recommendations\n",
        "def on_b3_clicked(b):\n",
        "  global output\n",
        "  history,recommendations=GenerateSimilarUsersRecommendations(target_user_id=user_id.value,\n",
        "                                             p=p.value,\n",
        "                  k=k.value,\n",
        "                  topN=num_recommendations_3.value)\n",
        "  output=recommendations.copy()\n",
        "b3.on_click(on_b3_clicked)"
      ],
      "metadata": {
        "id": "u70879jM-XOA"
      },
      "execution_count": null,
      "outputs": []
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        "display(ui)"
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          "data": {
            "text/plain": [
              "Tab(children=(VBox(children=(Dropdown(description='Genre', options=('Horror', 'Thriller', 'Action', 'War', 'Fi…"
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    {
      "cell_type": "code",
      "source": [
        "#Configure inputs and run for Popularity based Output\n",
        "output"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
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        "outputId": "51014845-aa78-41d3-d8d6-9e367a6aef61"
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            "text/plain": [
              "   S.No                                        Movie Title  \\\n",
              "0     1  Let the Right One In (Låt den rätte komma in) ...   \n",
              "1     2                   Silence of the Lambs, The (1991)   \n",
              "2     3                                      Psycho (1960)   \n",
              "\n",
              "   Average Movie Rating  Num Reviews  \n",
              "0              4.195652           23  \n",
              "1              4.194828          290  \n",
              "2              4.186047           86  "
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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      "cell_type": "code",
      "source": [
        "#Configure inputs and run for Content based Output\n",
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   S.No                                        Movie Title\n",
              "0     1  Henry: Portrait of a Serial Killer, Part 2 (1998)\n",
              "1     2                            King of the Ants (2003)\n",
              "2     3                         Tales from the Hood (1995)\n",
              "3     4                                  Serial Mom (1994)\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          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": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Configure inputs and run for Collaborative based Output\n",
        "output"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "T7k0MzlPHa4B",
        "outputId": "e974ca76-ab3e-41d1-90b8-3121b748badb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   S.No                                        Movie Title\n",
              "0     1                         Switchblade Sisters (1975)\n",
              "1     2                          Christmas Story, A (1983)\n",
              "2     3                   Drunken Master (Jui kuen) (1978)\n",
              "3     4                             Gunfighter, The (1950)\n",
              "4     5                     All Dogs Go to Heaven 2 (1996)\n",
              "5     6  Children of Paradise (Les enfants du paradis) ...\n",
              "6     7                           Court Jester, The (1956)\n",
              "7     8  Rome, Open City (a.k.a. Open City) (Roma, citt...\n",
              "8     9                       Endless Summer 2, The (1994)\n",
              "9    10                      Little Lord Fauntleroy (1936)"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-316084d5-0e91-406f-9687-d8067d94aef1\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>S.No</th>\n",
              "      <th>Movie Title</th>\n",
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              "      <td>Switchblade Sisters (1975)</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Christmas Story, A (1983)</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Drunken Master (Jui kuen) (1978)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Gunfighter, The (1950)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>All Dogs Go to Heaven 2 (1996)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Children of Paradise (Les enfants du paradis) ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Court Jester, The (1956)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Rome, Open City (a.k.a. Open City) (Roma, citt...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Endless Summer 2, The (1994)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>10</td>\n",
              "      <td>Little Lord Fauntleroy (1936)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-316084d5-0e91-406f-9687-d8067d94aef1')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </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-316084d5-0e91-406f-9687-d8067d94aef1 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-316084d5-0e91-406f-9687-d8067d94aef1');\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": 50
        }
      ]
    }
  ]
}