Item_Item_Collaborative_Assigment3_V1.ipynb

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      "source": [
        "# Mounting Drive"
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "# Mounting Drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Important Libraries"
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    {
      "cell_type": "code",
      "source": [
        "# Importing Important Libraries...\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
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        "id": "6wkif0tCCl82"
      },
      "execution_count": 3,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "# Loading Data...\n",
        "ratings = pd.read_csv('/content/drive/MyDrive/Semester-2/Recommendation System/Assignment 3/ratings.csv')\n",
        "books = pd.read_csv('/content/drive/MyDrive/Semester-2/Recommendation System/Assignment 3/Books.csv')"
      ],
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        "outputId": "4d431b92-5906-4ced-dad4-61ed0fd3256e"
      },
      "execution_count": 4,
      "outputs": [
        {
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          "text": [
            "<ipython-input-4-b4e23db902fc>:3: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  books = pd.read_csv('/content/drive/MyDrive/Semester-2/Recommendation System/Assignment 3/Books.csv')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Visualizing Data"
      ],
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      "source": [
        "# Visualizing ratings data...\n",
        "ratings.head()"
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              "         ISBN                                         Book-Title  \\\n",
              "0  0195153448                                Classical Mythology   \n",
              "1  0002005018                                       Clara Callan   \n",
              "2  0060973129                               Decision in Normandy   \n",
              "3  0374157065  Flu: The Story of the Great Influenza Pandemic...   \n",
              "4  0393045218                             The Mummies of Urumchi   \n",
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              "            Book-Author Year-Of-Publication                   Publisher  \\\n",
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              "2          Carlo D'Este                1991             HarperPerennial   \n",
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              "4       E. J. W. Barber                1999  W. W. Norton &amp; Company   \n",
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          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Checking for duplicates\n",
        "print(ratings.duplicated().sum())\n",
        "print(books.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAS_6NfINXnT",
        "outputId": "dd02a0e6-5aec-467b-92e9-d7c89dcad399"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Printing the shape of dataframes\n",
        "print(ratings.shape)\n",
        "print(books.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KidDFEskWCk2",
        "outputId": "d60f154a-69f5-4e97-b814-21cf7a6b6916"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(768068, 4)\n",
            "(271360, 8)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Merging Data"
      ],
      "metadata": {
        "id": "4-G1QhZBYVnt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Merging the ratings and books dataframe on ISBN\n",
        "ratings_with_name = ratings.merge(books, on='ISBN')\n",
        "print(ratings_with_name.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Fdi1iIcXEbM",
        "outputId": "97efa7cb-f1d4-4478-ba17-e74dbeca915b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(746466, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ratings_with_name.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        },
        "id": "Lr4T0N6YZ25t",
        "outputId": "00a5dacc-3688-40ae-fdef-fa1d4cd371c0"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "   Unnamed: 0  User-ID        ISBN  Book-Rating            Book-Title  \\\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filtering Based on Users"
      ],
      "metadata": {
        "id": "HngSch4VcbDe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Reasoning:-\n",
        "\n",
        "* There are lots of users who have rated very less number of books and similar for the books.\n",
        "* So, we have decided to take the genuine user and famous books only."
      ],
      "metadata": {
        "id": "M5wuO_lUceLk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtering merged dataframe based on the users...\n",
        "X = ratings_with_name.groupby('User-ID').count()['Book-Rating'] > 100\n",
        "genuine_users = X[X].index\n",
        "genuine_users"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dqhV_06SaTpH",
        "outputId": "2e1ef42b-dbbd-448b-dd48-38e7dec34448"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([   254,    507,    882,   1435,   1733,   2033,   2110,   2276,\n",
              "              2358,   2766,\n",
              "            ...\n",
              "            274808, 275020, 275970, 276463, 276680, 277427, 277639, 278137,\n",
              "            278188, 278418],\n",
              "           dtype='int64', name='User-ID', length=1166)"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtered ratings...\n",
        "filtered_ratings = ratings_with_name[ratings_with_name['User-ID'].isin(genuine_users)]\n",
        "filtered_ratings"
      ],
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      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "        Unnamed: 0  User-ID        ISBN  Book-Rating  \\\n",
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              "                                               Book-Title     Book-Author  \\\n",
              "2                                    Flesh Tones: A Novel      M. J. Rose   \n",
              "4                                    Flesh Tones: A Novel      M. J. Rose   \n",
              "5                                    Flesh Tones: A Novel      M. J. Rose   \n",
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              "13                                   Flesh Tones: A Novel      M. J. Rose   \n",
              "...                                                   ...             ...   \n",
              "746440                                    Streets of Gold     Evan Hunter   \n",
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              "746442     Home Crafts (Historic Communities (Paperback))   Bobbie Kalman   \n",
              "746443  The Old Sturbridge Village Cookbook, 2nd : Aut...  Caroline Sloat   \n",
              "746444                 City of Masks : A Cree Black Novel    Daniel Hecht   \n",
              "\n",
              "       Year-Of-Publication                    Publisher  \\\n",
              "2                     2002             Ballantine Books   \n",
              "4                     2002             Ballantine Books   \n",
              "5                     2002             Ballantine Books   \n",
              "7                     2002             Ballantine Books   \n",
              "13                    2002             Ballantine Books   \n",
              "...                    ...                          ...   \n",
              "746440                1985                Madison Books   \n",
              "746441                1990  Crabtree Publishing Company   \n",
              "746442                1990  Crabtree Publishing Company   \n",
              "746443                1995                 Globe Pequot   \n",
              "746444                2004               Bloomsbury USA   \n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>User-ID</th>\n",
              "      <th>ISBN</th>\n",
              "      <th>Book-Rating</th>\n",
              "      <th>Book-Title</th>\n",
              "      <th>Book-Author</th>\n",
              "      <th>Year-Of-Publication</th>\n",
              "      <th>Publisher</th>\n",
              "      <th>Image-URL-S</th>\n",
              "      <th>Image-URL-M</th>\n",
              "      <th>Image-URL-L</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>27533</td>\n",
              "      <td>6543</td>\n",
              "      <td>034545104X</td>\n",
              "      <td>0</td>\n",
              "      <td>Flesh Tones: A Novel</td>\n",
              "      <td>M. J. Rose</td>\n",
              "      <td>2002</td>\n",
              "      <td>Ballantine Books</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40794</td>\n",
              "      <td>10314</td>\n",
              "      <td>034545104X</td>\n",
              "      <td>9</td>\n",
              "      <td>Flesh Tones: A Novel</td>\n",
              "      <td>M. J. Rose</td>\n",
              "      <td>2002</td>\n",
              "      <td>Ballantine Books</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>101553</td>\n",
              "      <td>23768</td>\n",
              "      <td>034545104X</td>\n",
              "      <td>0</td>\n",
              "      <td>Flesh Tones: A Novel</td>\n",
              "      <td>M. J. Rose</td>\n",
              "      <td>2002</td>\n",
              "      <td>Ballantine Books</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>123655</td>\n",
              "      <td>28523</td>\n",
              "      <td>034545104X</td>\n",
              "      <td>0</td>\n",
              "      <td>Flesh Tones: A Novel</td>\n",
              "      <td>M. J. Rose</td>\n",
              "      <td>2002</td>\n",
              "      <td>Ballantine Books</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>325432</td>\n",
              "      <td>77480</td>\n",
              "      <td>034545104X</td>\n",
              "      <td>8</td>\n",
              "      <td>Flesh Tones: A Novel</td>\n",
              "      <td>M. J. Rose</td>\n",
              "      <td>2002</td>\n",
              "      <td>Ballantine Books</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/034545104X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746440</th>\n",
              "      <td>1149615</td>\n",
              "      <td>276680</td>\n",
              "      <td>0812881311</td>\n",
              "      <td>0</td>\n",
              "      <td>Streets of Gold</td>\n",
              "      <td>Evan Hunter</td>\n",
              "      <td>1985</td>\n",
              "      <td>Madison Books</td>\n",
              "      <td>http://images.amazon.com/images/P/0812881311.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0812881311.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0812881311.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746441</th>\n",
              "      <td>1149618</td>\n",
              "      <td>276680</td>\n",
              "      <td>0865055041</td>\n",
              "      <td>0</td>\n",
              "      <td>Kitchen (Historic Communities (Paperback))</td>\n",
              "      <td>Bobbie Kalman</td>\n",
              "      <td>1990</td>\n",
              "      <td>Crabtree Publishing Company</td>\n",
              "      <td>http://images.amazon.com/images/P/0865055041.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0865055041.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0865055041.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746442</th>\n",
              "      <td>1149619</td>\n",
              "      <td>276680</td>\n",
              "      <td>086505505X</td>\n",
              "      <td>0</td>\n",
              "      <td>Home Crafts (Historic Communities (Paperback))</td>\n",
              "      <td>Bobbie Kalman</td>\n",
              "      <td>1990</td>\n",
              "      <td>Crabtree Publishing Company</td>\n",
              "      <td>http://images.amazon.com/images/P/086505505X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/086505505X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/086505505X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746443</th>\n",
              "      <td>1149625</td>\n",
              "      <td>276680</td>\n",
              "      <td>1564407284</td>\n",
              "      <td>0</td>\n",
              "      <td>The Old Sturbridge Village Cookbook, 2nd : Aut...</td>\n",
              "      <td>Caroline Sloat</td>\n",
              "      <td>1995</td>\n",
              "      <td>Globe Pequot</td>\n",
              "      <td>http://images.amazon.com/images/P/1564407284.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/1564407284.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/1564407284.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746444</th>\n",
              "      <td>1149632</td>\n",
              "      <td>276680</td>\n",
              "      <td>1582343594</td>\n",
              "      <td>0</td>\n",
              "      <td>City of Masks : A Cree Black Novel</td>\n",
              "      <td>Daniel Hecht</td>\n",
              "      <td>2004</td>\n",
              "      <td>Bloomsbury USA</td>\n",
              "      <td>http://images.amazon.com/images/P/1582343594.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/1582343594.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/1582343594.0...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>459514 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-23707abb-cc43-4a86-afef-5eb0935a8c22')\"\n",
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              "    [theme=dark] .colab-df-convert {\n",
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            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Filtering Based on Recipes"
      ],
      "metadata": {
        "id": "0fqJvcyFdfB0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Filtering Based on recipes...\n",
        "y = filtered_ratings.groupby('Book-Title').count()['Book-Rating']>=50\n",
        "famous_books = y[y].index\n",
        "famous_books"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QKyh2rc0dUCj",
        "outputId": "bf9931b2-ef24-4537-8a8f-fad8a0becdc5"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['1984', '1st to Die: A Novel', '2nd Chance', '4 Blondes',\n",
              "       'A Beautiful Mind: The Life of Mathematical Genius and Nobel Laureate John Nash',\n",
              "       'A Bend in the Road', 'A Case of Need',\n",
              "       'A Child Called \\It\\\": One Child's Courage to Survive\"',\n",
              "       'A Civil Action', 'A Cry In The Night',\n",
              "       ...\n",
              "       'Winter Solstice', 'Wish You Well', 'Without Remorse',\n",
              "       'Wizard and Glass (The Dark Tower, Book 4)', 'Wuthering Heights',\n",
              "       'Year of Wonders', 'You Belong To Me',\n",
              "       'Zen and the Art of Motorcycle Maintenance: An Inquiry into Values',\n",
              "       'Zoya', '\\O\\\" Is for Outlaw\"'],\n",
              "      dtype='object', name='Book-Title', length=764)"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Final ratings dataframe...\n",
        "final_ratings = filtered_ratings[filtered_ratings['Book-Title'].isin(famous_books)]"
      ],
      "metadata": {
        "id": "lutsvP8cgVfl"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping Duplicates if any...\n",
        "final_ratings.drop_duplicates()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 730
        },
        "id": "Xv8hGY4neodA",
        "outputId": "bc9f8d20-7810-4041-c0bf-02b5ab02df51"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0  User-ID        ISBN  Book-Rating              Book-Title  \\\n",
              "58            5966   278418  038550120X            0         A Painted House   \n",
              "59           25981     6251  038550120X            0         A Painted House   \n",
              "63           64980    13273  038550120X            0         A Painted House   \n",
              "64           65999    13552  038550120X            0         A Painted House   \n",
              "65           74604    16106  038550120X            0         A Painted House   \n",
              "...            ...      ...         ...          ...                     ...   \n",
              "744640     1130643   271284  0440910927            0           The Rainmaker   \n",
              "744963     1133070   271705  B0001PIOX4            0          Fahrenheit 451   \n",
              "745724     1142740   274549  0451518012            0             Animal Farm   \n",
              "745786     1143468   274808  0449701913            0              Homecoming   \n",
              "746301     1147556   275970  1586210661            9  Me Talk Pretty One Day   \n",
              "\n",
              "          Book-Author Year-Of-Publication                Publisher  \\\n",
              "58       JOHN GRISHAM                2001                Doubleday   \n",
              "59       JOHN GRISHAM                2001                Doubleday   \n",
              "63       JOHN GRISHAM                2001                Doubleday   \n",
              "64       JOHN GRISHAM                2001                Doubleday   \n",
              "65       JOHN GRISHAM                2001                Doubleday   \n",
              "...               ...                 ...                      ...   \n",
              "744640   John Grisham                1995                   Island   \n",
              "744963   Ray Bradbury                1993     Simon &amp; Schuster   \n",
              "745724  George Orwell                1956              Signet Book   \n",
              "745786  Cynthia Voigt                1983            Fawcett Books   \n",
              "746301  David Sedaris                2001  Time Warner Audio Major   \n",
              "\n",
              "                                              Image-URL-S  \\\n",
              "58      http://images.amazon.com/images/P/038550120X.0...   \n",
              "59      http://images.amazon.com/images/P/038550120X.0...   \n",
              "63      http://images.amazon.com/images/P/038550120X.0...   \n",
              "64      http://images.amazon.com/images/P/038550120X.0...   \n",
              "65      http://images.amazon.com/images/P/038550120X.0...   \n",
              "...                                                   ...   \n",
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              "746301  http://images.amazon.com/images/P/1586210661.0...   \n",
              "\n",
              "                                              Image-URL-M  \\\n",
              "58      http://images.amazon.com/images/P/038550120X.0...   \n",
              "59      http://images.amazon.com/images/P/038550120X.0...   \n",
              "63      http://images.amazon.com/images/P/038550120X.0...   \n",
              "64      http://images.amazon.com/images/P/038550120X.0...   \n",
              "65      http://images.amazon.com/images/P/038550120X.0...   \n",
              "...                                                   ...   \n",
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              "...                                                   ...  \n",
              "744640  http://images.amazon.com/images/P/0440910927.0...  \n",
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              "      <td>0</td>\n",
              "      <td>A Painted House</td>\n",
              "      <td>JOHN GRISHAM</td>\n",
              "      <td>2001</td>\n",
              "      <td>Doubleday</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>64980</td>\n",
              "      <td>13273</td>\n",
              "      <td>038550120X</td>\n",
              "      <td>0</td>\n",
              "      <td>A Painted House</td>\n",
              "      <td>JOHN GRISHAM</td>\n",
              "      <td>2001</td>\n",
              "      <td>Doubleday</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>65999</td>\n",
              "      <td>13552</td>\n",
              "      <td>038550120X</td>\n",
              "      <td>0</td>\n",
              "      <td>A Painted House</td>\n",
              "      <td>JOHN GRISHAM</td>\n",
              "      <td>2001</td>\n",
              "      <td>Doubleday</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>74604</td>\n",
              "      <td>16106</td>\n",
              "      <td>038550120X</td>\n",
              "      <td>0</td>\n",
              "      <td>A Painted House</td>\n",
              "      <td>JOHN GRISHAM</td>\n",
              "      <td>2001</td>\n",
              "      <td>Doubleday</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/038550120X.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>744640</th>\n",
              "      <td>1130643</td>\n",
              "      <td>271284</td>\n",
              "      <td>0440910927</td>\n",
              "      <td>0</td>\n",
              "      <td>The Rainmaker</td>\n",
              "      <td>John Grisham</td>\n",
              "      <td>1995</td>\n",
              "      <td>Island</td>\n",
              "      <td>http://images.amazon.com/images/P/0440910927.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0440910927.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0440910927.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>744963</th>\n",
              "      <td>1133070</td>\n",
              "      <td>271705</td>\n",
              "      <td>B0001PIOX4</td>\n",
              "      <td>0</td>\n",
              "      <td>Fahrenheit 451</td>\n",
              "      <td>Ray Bradbury</td>\n",
              "      <td>1993</td>\n",
              "      <td>Simon &amp;amp; Schuster</td>\n",
              "      <td>http://images.amazon.com/images/P/B0001PIOX4.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/B0001PIOX4.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/B0001PIOX4.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>745724</th>\n",
              "      <td>1142740</td>\n",
              "      <td>274549</td>\n",
              "      <td>0451518012</td>\n",
              "      <td>0</td>\n",
              "      <td>Animal Farm</td>\n",
              "      <td>George Orwell</td>\n",
              "      <td>1956</td>\n",
              "      <td>Signet Book</td>\n",
              "      <td>http://images.amazon.com/images/P/0451518012.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0451518012.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0451518012.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>745786</th>\n",
              "      <td>1143468</td>\n",
              "      <td>274808</td>\n",
              "      <td>0449701913</td>\n",
              "      <td>0</td>\n",
              "      <td>Homecoming</td>\n",
              "      <td>Cynthia Voigt</td>\n",
              "      <td>1983</td>\n",
              "      <td>Fawcett Books</td>\n",
              "      <td>http://images.amazon.com/images/P/0449701913.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0449701913.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/0449701913.0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>746301</th>\n",
              "      <td>1147556</td>\n",
              "      <td>275970</td>\n",
              "      <td>1586210661</td>\n",
              "      <td>9</td>\n",
              "      <td>Me Talk Pretty One Day</td>\n",
              "      <td>David Sedaris</td>\n",
              "      <td>2001</td>\n",
              "      <td>Time Warner Audio Major</td>\n",
              "      <td>http://images.amazon.com/images/P/1586210661.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/1586210661.0...</td>\n",
              "      <td>http://images.amazon.com/images/P/1586210661.0...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>65080 rows × 11 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f558a9b2-efd6-4013-ba04-0c74a2c71f5d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "\n",
              "      <script>\n",
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              "          document.querySelector('#df-f558a9b2-efd6-4013-ba04-0c74a2c71f5d button.colab-df-convert');\n",
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              "\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Test Split"
      ],
      "metadata": {
        "id": "pU_l9Xnfg8j6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "# Group the data frame by the 'UserID' column\n",
        "grouped = final_ratings.groupby('User-ID')\n",
        "\n",
        "# Calculate the number of reviews for each user\n",
        "review_counts = grouped['ISBN'].count()\n",
        "\n",
        "# Create an empty list to store the test data frames\n",
        "test_data = []\n",
        "\n",
        "# Iterate through each group of reviews for a user\n",
        "for user, group in grouped:\n",
        "    # Determining the number of reviews to include in the test data frame\n",
        "    test_size = int(np.ceil(0.2 * len(group)))\n",
        "    # Split the group of reviews into train and test data frames\n",
        "    train = group.iloc[:-test_size]\n",
        "    test = group.iloc[-test_size:]\n",
        "    # Append the test data frame to the list of test data frames\n",
        "    test_data.append(test)\n",
        "\n",
        "# Concatenate all of the test data frames into a single test data frame\n",
        "test_data = pd.concat(test_data)\n",
        "\n",
        "# Drop the groups of reviews from the original data frame that were split into the test data frame\n",
        "train_data = final_ratings[~final_ratings.index.isin(test_data.index)]"
      ],
      "metadata": {
        "id": "v4WxVWDmguU5"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "DdSDq0uwiqND",
        "outputId": "6e70d3cf-a7c0-4f81-fbdf-1e01f7e8dd81"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Unnamed: 0  User-ID        ISBN  Book-Rating         Book-Title  \\\n",
              "143726       10230      254  0446356832            0  The Sands of Time   \n",
              "143888       10241      254  0451137965            0            Thinner   \n",
              "143961       10242      254  0451150244            0       Pet Sematary   \n",
              "143971       10243      254  0451167317            8      The Dark Half   \n",
              "144211       10253      254  0451526341            8        Animal Farm   \n",
              "\n",
              "           Book-Author Year-Of-Publication         Publisher  \\\n",
              "143726  Sidney Sheldon                1989      Warner Books   \n",
              "143888    Stephen King                1985  New Amer Library   \n",
              "143961    Stephen King                1984       Signet Book   \n",
              "143971    Stephen King                1994       Signet Book   \n",
              "144211   George Orwell                2004            Signet   \n",
              "\n",
              "                                              Image-URL-S  \\\n",
              "143726  http://images.amazon.com/images/P/0446356832.0...   \n",
              "143888  http://images.amazon.com/images/P/0451137965.0...   \n",
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              "\n",
              "                                              Image-URL-M  \\\n",
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              "143971  http://images.amazon.com/images/P/0451167317.0...   \n",
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              "\n",
              "                                              Image-URL-L  \n",
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              "143971  http://images.amazon.com/images/P/0451167317.0...  \n",
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              "\n",
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              "          const element = document.querySelector('#df-fafa633e-e496-4b2e-93bf-99680e50bb12');\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": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 461
        },
        "id": "-s9YAcdPitcs",
        "outputId": "4089c63e-80ad-42e9-f96e-ba8a7829af12"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Unnamed: 0  User-ID        ISBN  Book-Rating       Book-Title  \\\n",
              "58        5966   278418  038550120X            0  A Painted House   \n",
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              "\n",
              "     Book-Author Year-Of-Publication  Publisher  \\\n",
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              "\n",
              "                                          Image-URL-S  \\\n",
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              "                                                     [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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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Shape of Train Data:- \",train_data.shape)\n",
        "print(\"Shape of Test Data:- \",test_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wut0vQjcizqU",
        "outputId": "d40f056c-ec48-447f-fd28-15fd2f6af7f9"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape of Train Data:-  (51595, 11)\n",
            "Shape of Test Data:-  (13485, 11)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_data.to_csv('/content/drive/MyDrive/Semester-2/Recommendation System/train.csv')\n",
        "test_data.to_csv('test.csv')"
      ],
      "metadata": {
        "id": "vaaWpVP9HwNv"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_ratings.to_csv('/content/drive/MyDrive/Semester-2/Recommendation System/ratings_final.csv')"
      ],
      "metadata": {
        "id": "pkb3h4erPbCW"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Creating Pivot Table"
      ],
      "metadata": {
        "id": "c5o6c1s1j3rt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# create pivot table for train data\n",
        "train_pt = train_data.pivot_table(index='ISBN', columns='User-ID', values='Book-Rating')\n",
        "# calculate similarity scores\n",
        "similarity_scores = cosine_similarity(train_pt.fillna(0))\n",
        "# create pivot table for test data\n",
        "test_pt = test_data.pivot_table(index='ISBN', columns='User-ID', values='Book-Rating')\n",
        "# test_pt.fillna(0,inplace=True)\n",
        "# train_pt.fillna(0,inplace=True)"
      ],
      "metadata": {
        "id": "nsdwCrihjaSC"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_pt"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 519
        },
        "id": "gkzgfA4SkUv6",
        "outputId": "10384cac-1e9a-448a-bb53-8a59ce4b1bdc"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "User-ID     254     507     882     1435    1733    2033    2110    2276    \\\n",
              "ISBN                                                                         \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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              "  "
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          "metadata": {},
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    {
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      "execution_count": 28,
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>B00007CWQC</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B00009EF82</th>\n",
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              "      <td>...</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B0000T6KHI</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>B0001PIOX4</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>...</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1621 rows × 1159 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5f4b8d02-addc-4683-9c77-3e3269b58695')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "      \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",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "    [theme=dark] .colab-df-convert {\n",
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              "          const element = document.querySelector('#df-5f4b8d02-addc-4683-9c77-3e3269b58695');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\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",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Recommendation Engine"
      ],
      "metadata": {
        "id": "GuPLy1f_lKvo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def get_top_k_recommendations(user_id, k, rated_items=[]):\n",
        "    \"\"\"\n",
        "    Get top k recommendations for a given user ID.\n",
        "    Returns:\n",
        "    List of top k recommended recipe names for the user.\n",
        "    \"\"\"\n",
        "    if user_id not in train_pt.columns:\n",
        "        return []\n",
        "\n",
        "    # update user's ratings with rated items from previous rounds\n",
        "    for book, rating in rated_items:\n",
        "        if book in train_pt.index:\n",
        "            train_pt[user_id][book] = rating\n",
        "\n",
        "    # Fetching the rated books from train pivot table for given user_id \n",
        "    rated_books = train_pt[user_id]\n",
        "    rated_books = rated_books[rated_books.notna()]\n",
        "    # Finding the unrated books for given user id\n",
        "    unrated_books = train_pt.index.difference(rated_books.index)\n",
        "    predicted_ratings = []\n",
        "\n",
        "    '''\n",
        "    Predicting Rating for each book in unrated books.\n",
        "    '''\n",
        "    for book in unrated_books:\n",
        "        similarity_scores_for_book = similarity_scores[train_pt.index.get_loc(book)]\n",
        "        user_ratings = train_pt[user_id].fillna(0)\n",
        "        numerator = (user_ratings * similarity_scores_for_book).sum()\n",
        "        denominator = similarity_scores_for_book.sum()\n",
        "\n",
        "        if denominator == 0:\n",
        "            predicted_rating = 0\n",
        "        else:\n",
        "            predicted_rating = numerator / denominator\n",
        "\n",
        "        predicted_ratings.append((book, predicted_rating))\n",
        "        \n",
        "    top_book_names = [book for book, predicted_rating in sorted(predicted_ratings, key=lambda x: x[1], reverse=True)[:k]]\n",
        "    return top_book_names"
      ],
      "metadata": {
        "id": "aYxxyV3ZkoeX"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def main():\n",
        "  \"\"\"\n",
        "    Runs the main recommendation loop for the recipe recommender system.\n",
        "\n",
        "    The loop prompts the user to enter their ID and displays the top k recommended\n",
        "    recipes for the user based on their ratings and similarity to other users. The user\n",
        "    can then select a recipe and provide a rating for it, and the loop will continue\n",
        "    until the user is satisfied or there are no more recommendations left.\n",
        "\n",
        "    \"\"\"\n",
        "   \n",
        "  # initialize list of rated items\n",
        "  rated_items = []\n",
        "\n",
        "  # Taking user_id from the user\n",
        "  user_id = int(input(\"Enter user id:\"))\n",
        "\n",
        "  # loop until user is satisfied or there are no more recommendations\n",
        "  while True:\n",
        "      # get top k recommendations for user\n",
        "      recommendations = get_top_k_recommendations(user_id, 5, rated_items)\n",
        "\n",
        "      # check if there are any recommendations left\n",
        "      if len(recommendations) == 0:\n",
        "          print(\"No more recommendations\")\n",
        "          break\n",
        "\n",
        "      # display recommendations to user\n",
        "      print(\"Here are your recommendations:\")\n",
        "      for i, recommendation in enumerate(recommendations):\n",
        "          print(f\"{i+1}\"+\". \" f\"{final_ratings[final_ratings['ISBN'] == recommendation]['Book-Title'].values[0]}\")\n",
        "      \n",
        "      # ask user to select item they like or if they want more recommendations\n",
        "      selection = input(\"Enter number of item you like or 'm' for more recommendations: \")\n",
        "\n",
        "      # if user wants more recommendations, continue to next round\n",
        "      if selection == 'm':\n",
        "          continue\n",
        "\n",
        "      # get the index of the selected item\n",
        "      try:\n",
        "          selection_idx = int(selection) - 1\n",
        "          selected_item = recommendations[selection_idx]\n",
        "      except:\n",
        "          print(\"Invalid selection\")\n",
        "          continue\n",
        "\n",
        "      # add selected item and rating to list of rated items\n",
        "      rating = float(input(f\"What rating would you give {selected_item} (0-5)? \"))\n",
        "      rated_items.append((selected_item, rating))\n",
        "\n",
        "      # if user is satisfied, show ingredients of selected item\n",
        "      if input(\"Are you satisfied? (y/n) \") == 'y':\n",
        "          print(f\"Book Name for {selected_item}:\")\n",
        "          print(final_ratings[final_ratings['ISBN'] == selected_item]['Book-Title'].values[0])\n",
        "          break\n",
        "\n",
        "if __name__ == '__main__':\n",
        "    main()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aYaVi1u0oI0E",
        "outputId": "fa79a64e-a570-4f4b-a257-78900435f5d5"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Enter user id:2033\n",
            "Here are your recommendations:\n",
            "1. Dragonfly in Amber\n",
            "2. Neverwhere\n",
            "3. Catering to Nobody\n",
            "4. Angels\n",
            "5. The Golden Compass (His Dark Materials, Book 1)\n",
            "Enter number of item you like or 'm' for more recommendations: 3\n",
            "What rating would you give 0312042779 (0-5)? 4\n",
            "Are you satisfied? (y/n) y\n",
            "Book Name for 0312042779:\n",
            "Catering to Nobody\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "7DVtgHWQpRzK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}