Recommendation_System_Assignment.ipynb

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        "# **Problem statement.**\n",
        "\n",
        "# **Build a recommender system by using cosine simillarties score.**"
      ],
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      "source": [
        "**Load Data and Libraries**"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
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        "df = pd.read_csv('/content/book.csv')\n",
        "df.head()"
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              "   Unnamed: 0  User.ID                                         Book.Title  \\\n",
              "0           1   276726                                Classical Mythology   \n",
              "1           2   276729                                       Clara Callan   \n",
              "2           3   276729                               Decision in Normandy   \n",
              "3           4   276736  Flu: The Story of the Great Influenza Pandemic...   \n",
              "4           5   276737                             The Mummies of Urumchi   \n",
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              "      Unnamed: 0  User.ID                                    Book.Title  \\\n",
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 10000 entries, 0 to 9999\n",
            "Data columns (total 4 columns):\n",
            " #   Column       Non-Null Count  Dtype \n",
            "---  ------       --------------  ----- \n",
            " 0   Unnamed: 0   10000 non-null  int64 \n",
            " 1   User.ID      10000 non-null  int64 \n",
            " 2   Book.Title   10000 non-null  object\n",
            " 3   Book.Rating  10000 non-null  int64 \n",
            "dtypes: int64(3), object(1)\n",
            "memory usage: 312.6+ KB\n"
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        "#drop the column Unnamed as it is of no use\n",
        "book_df = df.drop('Unnamed: 0', axis=1)"
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              "      <th>4</th>\n",
              "      <td>276737</td>\n",
              "      <td>The Mummies of Urumchi</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cfa0d1b8-a57f-480a-a427-207dc46785a0')\"\n",
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              "    [theme=dark] .colab-df-convert:hover {\n",
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              "          const element = document.querySelector('#df-cfa0d1b8-a57f-480a-a427-207dc46785a0');\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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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(book_df['User.ID'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VUGhZ9CxxotI",
        "outputId": "584e0d04-db0d-4539-d146-5fd7d04a282d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2182"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "len(book_df['Book.Title'].unique())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4g06JEe0yiXt",
        "outputId": "f6f179b9-c442-46c7-9d19-c50ec0c1ce96"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9659"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#change names of columns to shorter names\n",
        "book_df = book_df.rename({'User.ID':'UserID', 'Book.Title':'Book', 'Book.Rating':'Rating'}, axis = 1)"
      ],
      "metadata": {
        "id": "zRbamOfHysXD"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "book_df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rBKgy8uO2sPm",
        "outputId": "8a9956e0-36ff-45e8-d64c-e8d4e1de7051"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['UserID', 'Book', 'Rating'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#check for missing values\n",
        "book_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xzF3s3vr2uoa",
        "outputId": "a99dacb6-c245-4f1e-b7f3-000be99c0f9d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserID    0\n",
              "Book      0\n",
              "Rating    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "No missing values in data."
      ],
      "metadata": {
        "id": "xM7hWlpf5qHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check for duplicate values\n",
        "book_df[book_df.duplicated()]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "jT0zILew3IEE",
        "outputId": "744a6a4b-bb33-412b-cb70-d38f4afbb99e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      UserID                       Book  Rating\n",
              "5051    2152  Le nouveau soleil de Teur       7\n",
              "7439    3757        The Magician's Tale       7"
            ],
            "text/html": [
              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>UserID</th>\n",
              "      <th>Book</th>\n",
              "      <th>Rating</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5051</th>\n",
              "      <td>2152</td>\n",
              "      <td>Le nouveau soleil de Teur</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7439</th>\n",
              "      <td>3757</td>\n",
              "      <td>The Magician's Tale</td>\n",
              "      <td>7</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              style=\"display:none;\">\n",
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              "      </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",
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              "\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e1369acd-ccc1-4199-a493-e8320a9af8a1 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-e1369acd-ccc1-4199-a493-e8320a9af8a1');\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",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will treat duplicate values after converting data to wide format."
      ],
      "metadata": {
        "id": "V79WDP5J5vov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#check data types of columns\n",
        "book_df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6QBK7CjV3PXX",
        "outputId": "2311d804-6af3-4168-975f-8b9910364fec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserID     int64\n",
              "Book      object\n",
              "Rating     int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "data types of the columns are correct."
      ],
      "metadata": {
        "id": "fH39L_iD6ESG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Data Visualisation**"
      ],
      "metadata": {
        "id": "bR2F2ST46qdJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Highest and lowest rating given to any movie\n",
        "book_df.Rating.unique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GS10rdvZ9rn-",
        "outputId": "a34d36b2-57af-4f2d-981e-bf0bf0f91c19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 5,  3,  6,  8,  7, 10,  9,  4,  1,  2])"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.countplot(book_df.Rating)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "82yqBPog7Sjs",
        "outputId": "7aac8b35-09a5-4b9f-fe46-82b799c05d07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f18f69e26d0>"
            ]
          },
          "metadata": {},
          "execution_count": 37
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Here we can see '8' is the rating given to the majority of times and '1' is the rating given very less times."
      ],
      "metadata": {
        "id": "Q8x-hRMV_Njr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Most & least rated books\n",
        "most_rated_a = book_df.Book.value_counts().head()\n",
        "most_rated_a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWDaK29ztxcR",
        "outputId": "f8b3129e-d5c5-4edb-bb37-ca6f06b7e039"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Fahrenheit 451                                   5\n",
              "Charlie and the Chocolate Factory                4\n",
              "The Subtle Knife (His Dark Materials, Book 2)    4\n",
              "Vanished                                         4\n",
              "Ender's Game (Ender Wiggins Saga (Paperback))    4\n",
              "Name: Book, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "least_rated_a = book_df.Book.value_counts().tail()\n",
        "least_rated_a"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I794lIk85wbx",
        "outputId": "183c95a1-57a1-43d2-e2f9-6ed5d9b8a1fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Murder on St. Mark's Place (Gaslight Mysteries)             1\n",
              "State of Grace                                              1\n",
              "Valsalva's Maneuver: Mots Justes and Indispensable Terms    1\n",
              "I love you, I hate you                                      1\n",
              "Kids Say the Darndest Things                                1\n",
              "Name: Book, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "array = ([['Fahrenheit 451',5], ['Charlie and the Chocolate Factory',4],['The Subtle Knife (His Dark Materials, Book 2)',4],\n",
        "         ['Vanished',4],['Enders Game (Ender Wiggins Saga (Paperback))',4],['Murder on St. Marks Place (Gaslight Mysteries)',1],\n",
        "          ['State of Grace',1],['Valsalvas Maneuver: Mots Justes and Indispensable Terms',1],\n",
        "          ['I love you, I hate you',1],['Kids Say the Darndest Things',1]])"
      ],
      "metadata": {
        "id": "thpg8vHFvOSw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "mnl_rated = pd.DataFrame(array,columns= ['Books', 'Number_of_Rating'])\n",
        "mnl_rated"
      ],
      "metadata": {
        "id": "xfQNXaC_M7Dh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "e214e096-ea41-46e5-96fd-056106e7b50e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                               Books  Number_of_Rating\n",
              "0                                     Fahrenheit 451                 5\n",
              "1                  Charlie and the Chocolate Factory                 4\n",
              "2      The Subtle Knife (His Dark Materials, Book 2)                 4\n",
              "3                                           Vanished                 4\n",
              "4       Enders Game (Ender Wiggins Saga (Paperback))                 4\n",
              "5     Murder on St. Marks Place (Gaslight Mysteries)                 1\n",
              "6                                     State of Grace                 1\n",
              "7  Valsalvas Maneuver: Mots Justes and Indispensa...                 1\n",
              "8                             I love you, I hate you                 1\n",
              "9                       Kids Say the Darndest Things                 1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b9fda5f-bc0f-4655-aa01-438f161d88a2\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Kids Say the Darndest Things</td>\n",
              "      <td>1</td>\n",
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              "\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-3b9fda5f-bc0f-4655-aa01-438f161d88a2 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-3b9fda5f-bc0f-4655-aa01-438f161d88a2');\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": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(70,20))\n",
        "plt.yticks(rotation = 0, fontsize = 40)\n",
        "plt.xticks(rotation = 90, fontsize = 40)\n",
        "sns.barplot(x = mnl_rated['Books'], y = mnl_rated['Number_of_Rating'])\n",
        "plt.xlabel('Book Name', size=40)\n",
        "plt.ylabel('Number of Rating', size=40)\n",
        "plt.title('Most & Least Rated Books', size=50)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 574
        },
        "id": "fUcMxrvvsYe6",
        "outputId": "a098a9be-6746-4d59-e502-abf885a0f394"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 5040x1440 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, from this plot we can say, Farhenheit 451 has been rated most number of times i.e 5."
      ],
      "metadata": {
        "id": "3bQg-0puICdT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#most rating given\n",
        "highest_rated = pd.DataFrame(book_df.groupby('Book')['Rating'].count())"
      ],
      "metadata": {
        "id": "-e15zBKL3B5C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "highest_rated.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "vyhV_lM3DmLU",
        "outputId": "b93d1c1a-fc2c-464e-bfaf-2b982282ce50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    Rating\n",
              "Book                                                      \n",
              " Jason, Madison &amp                                     1\n",
              " Other Stories;Merril;1985;McClelland &amp               1\n",
              " Repairing PC Drives &amp                                1\n",
              "'48                                                      1\n",
              "'O Au No Keia: Voices from Hawai'I's Mahu and T...       1"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2ffa9ed5-e650-422e-bf5d-5d6866eec9ca\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
              "    .dataframe thead th {\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Rating</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Book</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Jason, Madison &amp;amp</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other Stories;Merril;1985;McClelland &amp;amp</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Repairing PC Drives &amp;amp</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>'48</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>'O Au No Keia: Voices from Hawai'I's Mahu and Transgender Communities</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2ffa9ed5-e650-422e-bf5d-5d6866eec9ca')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
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              "      flex-wrap:wrap;\n",
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              "      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",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-2ffa9ed5-e650-422e-bf5d-5d6866eec9ca 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-2ffa9ed5-e650-422e-bf5d-5d6866eec9ca');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.histplot(highest_rated['Rating'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "PHC8fj3YF05N",
        "outputId": "92821f63-97b4-416d-db27-d8c08a20324a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f27b3757690>"
            ]
          },
          "metadata": {},
          "execution_count": 72
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, most books have been rated only once."
      ],
      "metadata": {
        "id": "B2oRiSb7H8J3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Pre-processing data**"
      ],
      "metadata": {
        "id": "cb5MoNJdW1Ml"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "hTTcgs_NIYSM",
        "outputId": "780e4147-41aa-443e-80f0-69f48fa8e745"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      UserID                                               Book  Rating\n",
              "0     276726                                Classical Mythology       5\n",
              "1     276729                                       Clara Callan       3\n",
              "2     276729                               Decision in Normandy       6\n",
              "3     276736  Flu: The Story of the Great Influenza Pandemic...       8\n",
              "4     276737                             The Mummies of Urumchi       6\n",
              "...      ...                                                ...     ...\n",
              "9995  162121       American Fried: Adventures of a Happy Eater.       7\n",
              "9996  162121                              Cannibal In Manhattan       9\n",
              "9997  162121                    How to Flirt: A Practical Guide       7\n",
              "9998  162121                                           Twilight       8\n",
              "9999  162129                       Kids Say the Darndest Things       6\n",
              "\n",
              "[10000 rows x 3 columns]"
            ],
            "text/html": [
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>0</th>\n",
              "      <td>276726</td>\n",
              "      <td>Classical Mythology</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>276729</td>\n",
              "      <td>Clara Callan</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>276729</td>\n",
              "      <td>Decision in Normandy</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>276736</td>\n",
              "      <td>Flu: The Story of the Great Influenza Pandemic...</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>276737</td>\n",
              "      <td>The Mummies of Urumchi</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>9995</th>\n",
              "      <td>162121</td>\n",
              "      <td>American Fried: Adventures of a Happy Eater.</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>162121</td>\n",
              "      <td>Cannibal In Manhattan</td>\n",
              "      <td>9</td>\n",
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              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>162121</td>\n",
              "      <td>How to Flirt: A Practical Guide</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>162121</td>\n",
              "      <td>Twilight</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>162129</td>\n",
              "      <td>Kids Say the Darndest Things</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8a12a5d8-20b7-408d-8389-abe7143c4f44')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8a12a5d8-20b7-408d-8389-abe7143c4f44 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-8a12a5d8-20b7-408d-8389-abe7143c4f44');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Popularity Recommeder System**"
      ],
      "metadata": {
        "id": "F2WU7RsQVU40"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It will recommed the books that have been rated atleast 3 times."
      ],
      "metadata": {
        "id": "rYUn1pyyVaii"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#number of rating given to each book\n",
        "num_rating = book_df.groupby('Book').count()['Rating'].reset_index()\n",
        "num_rating.rename(columns={'Rating':'num_ratings'}, inplace=True)\n",
        "num_rating"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "d_AWOXRkVJ_j",
        "outputId": "2ed16a32-2fd2-46f4-a004-41cf5a593550"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   Book  num_ratings\n",
              "0                                   Jason, Madison &amp            1\n",
              "1             Other Stories;Merril;1985;McClelland &amp            1\n",
              "2                              Repairing PC Drives &amp            1\n",
              "3                                                   '48            1\n",
              "4     'O Au No Keia: Voices from Hawai'I's Mahu and ...            1\n",
              "...                                                 ...          ...\n",
              "9654  \\Surely You're Joking, Mr. Feynman!\\: Adventur...            1\n",
              "9655             \\Well, there's your problem\\: Cartoons            1\n",
              "9656                            iI Paradiso Degli Orchi            1\n",
              "9657                                           stardust            1\n",
              "9658                                     Ã?Â?bermorgen.            1\n",
              "\n",
              "[9659 rows x 2 columns]"
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              "      <td>\\Well, there's your problem\\: Cartoons</td>\n",
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              "<p>9659 rows × 2 columns</p>\n",
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              "\n",
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              "      padding: 0 0 0 0;\n",
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              "    }\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",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
              "      <script>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-24a05f02-d2d1-4d87-9f0e-168a1a66e59e');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#average rating given to a book\n",
        "avg_rating = book_df.groupby('Book').mean()['Rating'].reset_index()\n",
        "avg_rating.rename(columns={'Rating':'avg_ratings'}, inplace=True)\n",
        "avg_rating"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "KD8SOqxrYmmV",
        "outputId": "0ea1ac79-41f3-4617-9ddf-05a8fe51f375"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                   Book  avg_ratings\n",
              "0                                   Jason, Madison &amp          8.0\n",
              "1             Other Stories;Merril;1985;McClelland &amp          6.0\n",
              "2                              Repairing PC Drives &amp          4.0\n",
              "3                                                   '48          7.0\n",
              "4     'O Au No Keia: Voices from Hawai'I's Mahu and ...          8.0\n",
              "...                                                 ...          ...\n",
              "9654  \\Surely You're Joking, Mr. Feynman!\\: Adventur...          8.0\n",
              "9655             \\Well, there's your problem\\: Cartoons          9.0\n",
              "9656                            iI Paradiso Degli Orchi          7.0\n",
              "9657                                           stardust          5.0\n",
              "9658                                     Ã?Â?bermorgen.          4.0\n",
              "\n",
              "[9659 rows x 2 columns]"
            ],
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              "      <td>Other Stories;Merril;1985;McClelland &amp;amp</td>\n",
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              "      <td>4.0</td>\n",
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        "popular_df = num_rating.merge(avg_rating, on = 'Book').sort_values\n",
        "popular_df"
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              "                                                   Book  num_ratings  \\\n",
              "0                                   Jason, Madison &amp            1   \n",
              "1             Other Stories;Merril;1985;McClelland &amp            1   \n",
              "2                              Repairing PC Drives &amp            1   \n",
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    {
      "cell_type": "code",
      "source": [
        "#Popular books according to our criteria\n",
        "popular_df = popular_df[popular_df['num_ratings'] >= 3]\n",
        "popular_df.sort_values('avg_ratings', ascending = False)"
      ],
      "metadata": {
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          "height": 927
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        "id": "zPCDrFBiZRzj",
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            "text/plain": [
              "                                                 Book  num_ratings  \\\n",
              "7398                                         The Face            3   \n",
              "8459    The Subtle Knife (His Dark Materials, Book 2)            4   \n",
              "6471                                         Stardust            4   \n",
              "8505                                    The Testament            3   \n",
              "3237                                    High Fidelity            3   \n",
              "7997                            The Neverending Story            3   \n",
              "7635             The Hitchhiker's Guide to the Galaxy            3   \n",
              "7540  The Golden Compass (His Dark Materials, Book 1)            3   \n",
              "6843  The Amber Spyglass (His Dark Materials, Book 3)            4   \n",
              "6637                     Suzanne's Diary for Nicholas            3   \n",
              "4873                          My Side of the Mountain            3   \n",
              "5383                                     Pet Sematary            3   \n",
              "5533                              Pride and Prejudice            3   \n",
              "6548                                        Strangers            3   \n",
              "9575                                Wuthering Heights            3   \n",
              "1481                                  Cold Sassy Tree            3   \n",
              "6205                                       Siddhartha            3   \n",
              "5409                                   Pigs in Heaven            3   \n",
              "7625                           The Hero and the Crown            3   \n",
              "4187                                Life Is Elsewhere            3   \n",
              "2518                                   Fahrenheit 451            5   \n",
              "8692                          The Wind in the Willows            3   \n",
              "9135                                         Vanished            4   \n",
              "1313                Charlie and the Chocolate Factory            4   \n",
              "4788                                        Mrs. Mike            3   \n",
              "2392    Ender's Game (Ender Wiggins Saga (Paperback))            4   \n",
              "3673                                               It            3   \n",
              "8456                                The Street Lawyer            3   \n",
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              "      avg_ratings  \n",
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              "      <th>5383</th>\n",
              "      <td>Pet Sematary</td>\n",
              "      <td>3</td>\n",
              "      <td>7.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5533</th>\n",
              "      <td>Pride and Prejudice</td>\n",
              "      <td>3</td>\n",
              "      <td>7.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6548</th>\n",
              "      <td>Strangers</td>\n",
              "      <td>3</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9575</th>\n",
              "      <td>Wuthering Heights</td>\n",
              "      <td>3</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1481</th>\n",
              "      <td>Cold Sassy Tree</td>\n",
              "      <td>3</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6205</th>\n",
              "      <td>Siddhartha</td>\n",
              "      <td>3</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5409</th>\n",
              "      <td>Pigs in Heaven</td>\n",
              "      <td>3</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7625</th>\n",
              "      <td>The Hero and the Crown</td>\n",
              "      <td>3</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4187</th>\n",
              "      <td>Life Is Elsewhere</td>\n",
              "      <td>3</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2518</th>\n",
              "      <td>Fahrenheit 451</td>\n",
              "      <td>5</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8692</th>\n",
              "      <td>The Wind in the Willows</td>\n",
              "      <td>3</td>\n",
              "      <td>6.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9135</th>\n",
              "      <td>Vanished</td>\n",
              "      <td>4</td>\n",
              "      <td>6.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1313</th>\n",
              "      <td>Charlie and the Chocolate Factory</td>\n",
              "      <td>4</td>\n",
              "      <td>6.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4788</th>\n",
              "      <td>Mrs. Mike</td>\n",
              "      <td>3</td>\n",
              "      <td>6.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2392</th>\n",
              "      <td>Ender's Game (Ender Wiggins Saga (Paperback))</td>\n",
              "      <td>4</td>\n",
              "      <td>6.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3673</th>\n",
              "      <td>It</td>\n",
              "      <td>3</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8456</th>\n",
              "      <td>The Street Lawyer</td>\n",
              "      <td>3</td>\n",
              "      <td>5.000000</td>\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "popular_df = popular_df.sort_values('avg_ratings', ascending = False).head(10)"
      ],
      "metadata": {
        "id": "8X-nS-yqZtHb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "popular_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "YuvoaId_mRJc",
        "outputId": "8297b7e3-7af6-4230-f6f9-0a3b8b5735d0"
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                 Book  num_ratings  \\\n",
              "7398                                         The Face            3   \n",
              "8459    The Subtle Knife (His Dark Materials, Book 2)            4   \n",
              "6471                                         Stardust            4   \n",
              "8505                                    The Testament            3   \n",
              "3237                                    High Fidelity            3   \n",
              "7997                            The Neverending Story            3   \n",
              "7635             The Hitchhiker's Guide to the Galaxy            3   \n",
              "7540  The Golden Compass (His Dark Materials, Book 1)            3   \n",
              "6843  The Amber Spyglass (His Dark Materials, Book 3)            4   \n",
              "6637                     Suzanne's Diary for Nicholas            3   \n",
              "\n",
              "      avg_ratings  \n",
              "7398     9.000000  \n",
              "8459     8.750000  \n",
              "6471     8.500000  \n",
              "8505     8.333333  \n",
              "3237     8.333333  \n",
              "7997     8.333333  \n",
              "7635     8.333333  \n",
              "7540     8.333333  \n",
              "6843     8.250000  \n",
              "6637     8.000000  "
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              "      <th>7997</th>\n",
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              "      <th>7635</th>\n",
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              "      <td>4</td>\n",
              "      <td>8.250000</td>\n",
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              "      <th>6637</th>\n",
              "      <td>Suzanne's Diary for Nicholas</td>\n",
              "      <td>3</td>\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, according to popularity, we can recommed the above 28 books with highest ot lowest ratings.\n",
        "\n",
        "- **popular_df is my Top-10 books.**"
      ],
      "metadata": {
        "id": "zMuaeELpcO7f"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Collaborative Filtering based Recommeder System**"
      ],
      "metadata": {
        "id": "Ac476YGicaJS"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "book_df.UserID.unique().shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0h614FC6hGz3",
        "outputId": "a9b0e81c-402e-4a9f-aa4f-88a6c6eeddd7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2182,)"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "book_df.Book.unique().shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QSdMNCGnkKsX",
        "outputId": "0c13e5d8-277a-4f35-99e2-0650fc440786"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(9659,)"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "book_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3a385VG0i4n5",
        "outputId": "7382766b-31c4-4592-8198-18190cd74674"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Let us try to build a recommeder system for books which have been rated more than or equal to 2 times and for users whohave rated more than or equal to 2 times.**"
      ],
      "metadata": {
        "id": "ldxokTAQam2p"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Users who have rated books more than or equal to 2 times.\n",
        "x = book_df.groupby('UserID').count()['Rating'] >= 2\n",
        "active_users = x[x].index\n",
        "active_users"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O1kGKw6shSu4",
        "outputId": "5a1fc260-803d-4526-9b8a-0786bb700fb0"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([     8,     14,     17,     26,     39,     53,     56,     69,\n",
              "                87,     92,\n",
              "            ...\n",
              "            278732, 278773, 278782, 278798, 278818, 278832, 278843, 278844,\n",
              "            278851, 278854],\n",
              "           dtype='int64', name='UserID', length=859)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "active_users_df = book_df[book_df['UserID'].isin(active_users)]"
      ],
      "metadata": {
        "id": "tHF91UbQhSke"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Out of 2182 users, 859 avtive users have given 8677 ratings.  "
      ],
      "metadata": {
        "id": "g4GaNc3xkr6v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#Books which have rated more than or equal to 2 times.\n",
        "y = active_users_df.groupby('Book').count()['Rating'] >= 2\n",
        "famous_book = y[y].index\n",
        "famous_book"
      ],
      "metadata": {
        "id": "Y5xeGwSAkrT6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "92515b87-31ca-49f9-8ed7-fcaaa2d87c20"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['1st to Die: A Novel', '2nd Chance', 'A Density of Souls',\n",
              "       'A Light in the Attic', 'A Little Princess', 'A Map of the World',\n",
              "       'A Reporter's Life', 'A Time to Kill', 'A Woman's Worth',\n",
              "       'Acceptable Risk',\n",
              "       ...\n",
              "       'White Oleander : A Novel', 'Wild Seed', 'Wildest Hearts',\n",
              "       'Winter's Tale', 'Wish You Well', 'Witch', 'Women on the Case',\n",
              "       'Wouldn't Take Nothing for My Journey Now', 'Wuthering Heights',\n",
              "       'Young Wives'],\n",
              "      dtype='object', name='Book', length=243)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "famous_book_df = active_users_df[active_users_df['Book'].isin(famous_book)]"
      ],
      "metadata": {
        "id": "wxTTytBqhSTz"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "famous_book_df.shape"
      ],
      "metadata": {
        "id": "ivEMpjCUhSPs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c233bebf-854e-4adc-acd7-dbc527a3787a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(511, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
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    {
      "cell_type": "code",
      "source": [
        "famous_book_df"
      ],
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          "height": 424
        },
        "id": "BIqyfFMGGYBM",
        "outputId": "b1a7a06f-defe-4398-a9bf-5294af7a9ccf"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      UserID                      Book  Rating\n",
              "18    276762             The Testament       8\n",
              "36    276798               Lying Awake       5\n",
              "37    276798     To Kill a Mockingbird       7\n",
              "44    276813                  Icebound       8\n",
              "47    276813              Isle of Dogs       8\n",
              "...      ...                       ...     ...\n",
              "9943  162052              Empire Falls       4\n",
              "9945  162052                      Rose       8\n",
              "9948  162052              Leaving Home       1\n",
              "9952  162052  Dave Barry in Cyberspace       8\n",
              "9961  162052            Tomcat in Love       6\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "- Out of 9659 books, 243 books have been rated more than or equal to 2 times by 859 active users."
      ],
      "metadata": {
        "id": "MG8fK85WC5GG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#drop duplicates\n",
        "final_df = famous_book_df.drop_duplicates()"
      ],
      "metadata": {
        "id": "R9i0aJlcGzbv"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nUSflMJKHNQh",
        "outputId": "c0850922-4abb-4267-c323-dac007d6ee88"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(509, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a pivot table for famous_book_df\n",
        "user_df = final_df.pivot_table(index = 'Book', columns = 'UserID', values = 'Rating')"
      ],
      "metadata": {
        "id": "FKkh6eI-C4xo"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_df.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "V5qFjPYEHWQ0"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "user_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 516
        },
        "id": "OxzWnuAeIbhd",
        "outputId": "71b3e700-6564-4091-d7b0-2da9b6fcaad8"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "UserID                                    53      160     183     242     \\\n",
              "Book                                                                       \n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A Density of Souls</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A Light in the Attic</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>A Little Princess</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "      <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>Witch</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Women on the Case</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wouldn't Take Nothing for My Journey Now</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Wuthering Heights</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Young Wives</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>243 rows × 276 columns</p>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Calculating cosine similarity distances between users\n",
        "from sklearn.metrics.pairwise import cosine_similarity"
      ],
      "metadata": {
        "id": "Q90LeaO9Ilez"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "similarity_scores = cosine_similarity(user_df)"
      ],
      "metadata": {
        "id": "wf8-5qRqJtqs"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "similarity_scores"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_9zGxuJVKic",
        "outputId": "3c0499ca-4471-408f-cccd-367fe08d21cc"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0., ..., 0., 0., 0.],\n",
              "       [0., 1., 0., ..., 0., 0., 0.],\n",
              "       [0., 0., 1., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a function to recommend similar books\n",
        "def recommend(book_name):\n",
        "    index = np.where(user_df.index == book_name)[0][0]\n",
        "    similar_items = sorted(list(enumerate(similarity_scores[index])), key=lambda x:x[1], reverse = True)[1:6]\n",
        "\n",
        "    for i in similar_items:\n",
        "      print(user_df.index[i[0]])"
      ],
      "metadata": {
        "id": "tpMjcsugKLV1"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Recommendations Results**"
      ],
      "metadata": {
        "id": "gWwhi9z5YjrB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "recommend('Young Wives')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GH8IR-b6VSE-",
        "outputId": "0c093737-ce53-414c-9084-2ab7167def04"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Icebound\n",
            "1st to Die: A Novel\n",
            "2nd Chance\n",
            "A Density of Souls\n",
            "A Light in the Attic\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recommend(\"Wouldn't Take Nothing for My Journey Now\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZbq6t1bYIU-",
        "outputId": "e7169883-0256-4b02-f5d9-1a6b75338c98"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A Time to Kill\n",
            "Life Before Man\n",
            "The Hobbit: Or There and Back Again\n",
            "The Adventures of Tom Sawyer\n",
            "How Stella Got Her Groove Back\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recommend('A Little Princess')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-4fL4gHbYcjm",
        "outputId": "2c8d3fcf-3fbd-4c90-bb7c-50e389cf6e31"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The Magician's Tale\n",
            "Juniper\n",
            "How to Make an American Quilt\n",
            "The Smoke Jumper\n",
            "Alice's Tulips\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Let us try to build a recommeder system for our original data**"
      ],
      "metadata": {
        "id": "QCOzMzhHa9Ds"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create a pivot table for famous_book_df\n",
        "recommend_df = book_df.pivot_table(index = 'Book', columns = 'UserID', values = 'Rating')"
      ],
      "metadata": {
        "id": "y7ItoRQnYzYq"
      },
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "recommend_df.fillna(0, inplace=True)"
      ],
      "metadata": {
        "id": "4ANBMCjabRtd"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "recommend_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 551
        },
        "id": "2natS7B4bcyS",
        "outputId": "db4c393a-3f54-45af-bf27-5222332f8b79"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "UserID                                              8       9       10      \\\n",
              "Book                                                                         \n",
              " Jason, Madison &amp                                   0.0     0.0     0.0   \n",
              " Other Stories;Merril;1985;McClelland &amp             0.0     0.0     0.0   \n",
              " Repairing PC Drives &amp                              0.0     0.0     0.0   \n",
              "'48                                                    0.0     0.0     0.0   \n",
              "'O Au No Keia: Voices from Hawai'I's Mahu and T...     0.0     0.0     0.0   \n",
              "...                                                    ...     ...     ...   \n",
              "\\Surely You're Joking, Mr. Feynman!\\: Adventure...     0.0     0.0     0.0   \n",
              "\\Well, there's your problem\\: Cartoons                 0.0     0.0     0.0   \n",
              "iI Paradiso Degli Orchi                                0.0     0.0     0.0   \n",
              "stardust                                               0.0     0.0     0.0   \n",
              "Ã?Â?bermorgen.                                         0.0     0.0     0.0   \n",
              "\n",
              "UserID                                              12      14      16      \\\n",
              "Book                                                                         \n",
              " Jason, Madison &amp                                   0.0     0.0     0.0   \n",
              " Other Stories;Merril;1985;McClelland &amp             0.0     0.0     0.0   \n",
              " Repairing PC Drives &amp                              0.0     0.0     0.0   \n",
              "'48                                                    0.0     0.0     0.0   \n",
              "'O Au No Keia: Voices from Hawai'I's Mahu and T...     0.0     0.0     0.0   \n",
              "...                                                    ...     ...     ...   \n",
              "\\Surely You're Joking, Mr. Feynman!\\: Adventure...     0.0     0.0     0.0   \n",
              "\\Well, there's your problem\\: Cartoons                 0.0     0.0     0.0   \n",
              "iI Paradiso Degli Orchi                                0.0     0.0     0.0   \n",
              "stardust                                               0.0     0.0     0.0   \n",
              "Ã?Â?bermorgen.                                         0.0     0.0     0.0   \n",
              "\n",
              "UserID                                              17      19      22      \\\n",
              "Book                                                                         \n",
              " Jason, Madison &amp                                   0.0     0.0     0.0   \n",
              " Other Stories;Merril;1985;McClelland &amp             0.0     0.0     0.0   \n",
              " Repairing PC Drives &amp                              0.0     0.0     0.0   \n",
              "'48                                                    0.0     0.0     0.0   \n",
              "'O Au No Keia: Voices from Hawai'I's Mahu and T...     0.0     0.0     0.0   \n",
              "...                                                    ...     ...     ...   \n",
              "\\Surely You're Joking, Mr. Feynman!\\: Adventure...     0.0     0.0     0.0   \n",
              "\\Well, there's your problem\\: Cartoons                 0.0     0.0     0.0   \n",
              "iI Paradiso Degli Orchi                                0.0     0.0     0.0   \n",
              "stardust                                               0.0     0.0     0.0   \n",
              "Ã?Â?bermorgen.                                         0.0     0.0     0.0   \n",
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          "metadata": {},
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      "cell_type": "code",
      "source": [
        "final_similarity_score = cosine_similarity(recommend_df)"
      ],
      "metadata": {
        "id": "YiT85MFqb1L1"
      },
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "final_similarity_score"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ULMflRhvdPib",
        "outputId": "d7979f8f-d90e-41e9-e4ea-a232c609c76e"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., ..., 0., 0., 0.],\n",
              "       [1., 1., 1., ..., 0., 0., 0.],\n",
              "       [1., 1., 1., ..., 0., 0., 0.],\n",
              "       ...,\n",
              "       [0., 0., 0., ..., 1., 0., 0.],\n",
              "       [0., 0., 0., ..., 0., 1., 0.],\n",
              "       [0., 0., 0., ..., 0., 0., 1.]])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#create a function to recommend similar books\n",
        "def recommend(book_name):\n",
        "    index = np.where(recommend_df.index == book_name)[0][0]\n",
        "    similar_items = sorted(list(enumerate(final_similarity_score[index])), key=lambda x:x[1], reverse = True)[1:6]\n",
        "\n",
        "    for i in similar_items:\n",
        "      print(recommend_df.index[i[0]])"
      ],
      "metadata": {
        "id": "kNuq9sVwdURt"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Recommendation Results for Original data**"
      ],
      "metadata": {
        "id": "4rtIKpDBd2ZA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "recommend(\"stardust\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oDu3rw_odtjI",
        "outputId": "b6e0e8e4-a378-4c26-a9bf-93a91cac52a7"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fires of Aggar\n",
            "Kamikaze Lust\n",
            "Rule of the Bone : Novel, A\n",
            "Stone Butch Blues\n",
            "The Adventures of Dougal\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recommend(\"'48\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Jdouv8vdzou",
        "outputId": "c64a5393-f4ec-4493-a0a0-714674a30e9e"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Angry Men, Passive Men: Understanding the Roots of Men's Anger and How to Move Beyond It\n",
            "Her Father's House\n",
            "The Day\n",
            "The Homeschooling Book of Answers : The 88 Most Important Questions Answered by Homeschooling's Most Respected Voices\n",
            "The Scold's Bridle\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "recommend(\"iI Paradiso Degli Orchi\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "te8XjsfBeBJB",
        "outputId": "3f81927f-d3b6-4618-b413-597e0e8df1b5"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Fantastic Imagination\n",
            "Hitched\n",
            "Huis Clos Suivi de Les Mouches (Folio Ser. No. 807)\n",
            "McNally's Chance\n",
            "Mystic River\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "LEhZaUU6eqJV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}