EDA&Preprocessing.ipynb

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      "authorship_tag": "ABX9TyPb4Y/Zkne9GmKzw74UYsFf",
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      "name": "python3",
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Sarvesh1814/US-Book-Recommendation-System-/blob/main/EDA%26Preprocessing.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Importing Libraries and Loading Data"
      ],
      "metadata": {
        "id": "eCMiFp0aLZaG"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MkwKRvnmJGUr"
      },
      "outputs": [],
      "source": [
        "import pandas as pd \n",
        "import numpy as np \n",
        "import matplotlib.pyplot as plt "
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3Z7AALE5KjUu",
        "outputId": "01131217-3c94-4786-af58-292c19da5e4c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "main = \"/content/drive/MyDrive/RS Data/Assignment 3/\""
      ],
      "metadata": {
        "id": "Xc59lC9eKkLk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "book = pd.read_csv(main+\"Books.csv\")\n",
        "rating = pd.read_csv(main+\"Ratings.csv\")\n",
        "users = pd.read_csv(main+\"Users.csv\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tw5DXbAYLEN0",
        "outputId": "8375dad9-f41c-4e40-979e-94b296d2a61f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-4-d51d1b914709>:1: DtypeWarning: Columns (3) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  book = pd.read_csv(main+\"Books.csv\")\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Exploratory Data Analysis (Specifically focused on users)"
      ],
      "metadata": {
        "id": "Of0Jd95iMLt-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "users.head()"
      ],
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        "outputId": "39172e6e-4ad7-4e86-9c60-122f627748e9"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   User-ID                            Location   Age\n",
              "0        1                  nyc, new york, usa   NaN\n",
              "1        2           stockton, california, usa  18.0\n",
              "2        3     moscow, yukon territory, russia   NaN\n",
              "3        4           porto, v.n.gaia, portugal  17.0\n",
              "4        5  farnborough, hants, united kingdom   NaN"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>User-ID</th>\n",
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              "      <td>NaN</td>\n",
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              "      <td>stockton, california, usa</td>\n",
              "      <td>18.0</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>moscow, yukon territory, russia</td>\n",
              "      <td>NaN</td>\n",
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              "      <th>3</th>\n",
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              "      <td>porto, v.n.gaia, portugal</td>\n",
              "      <td>17.0</td>\n",
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              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>farnborough, hants, united kingdom</td>\n",
              "      <td>NaN</td>\n",
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          "metadata": {},
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    {
      "cell_type": "code",
      "source": [
        "plt.hist(users['Age'], bins=20)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Age Distribution of Users')\n",
        "plt.show()"
      ],
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        "colab": {
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Most of the users in the data lie between age 20-40.**"
      ],
      "metadata": {
        "id": "Lhw_1VTfS_oU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exctracting Nationality information \n",
        "users['country']=''\n",
        "for i in range(len(users)):\n",
        "  s = users['Location'].iloc[i].split(\",\")\n",
        "  users['country'].iloc[i] = s[-1]"
      ],
      "metadata": {
        "id": "qJIPBs43L2Ys",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f756463f-421f-4463-d634-72482f253b4d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-7-20a2cd689495>:5: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  users['country'].iloc[i] = s[-1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "country_counts = users['country'].value_counts().nlargest(5)\n",
        "\n",
        "plt.bar(country_counts.index, country_counts.values)\n",
        "plt.xlabel('Country')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Users of Top 5 countries')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "07mbAKZLOZFb",
        "outputId": "f0617340-89c6-4d45-fedb-28ab23f54eee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Majority of users belong to USA**"
      ],
      "metadata": {
        "id": "hDKgyYX1TO6G"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "usa_users = users[users['country'] == ' usa']\n",
        "plt.hist(usa_users['Age'],bins=20)\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Age Distribution of USA Users')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "fvo4LvEePW7b",
        "outputId": "bb745663-a725-4937-e6c7-aff942845a86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Same trend of age is being followed in the user of American Nationality**"
      ],
      "metadata": {
        "id": "LLxdcE29TUly"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "usa_users.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xfD2AjsjRevy",
        "outputId": "01e37d28-88ea-4171-eff6-3aeb6071057b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(139711, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Based on above statisics and findings for this recommendation system we are going to include users who belong to USA.**\n",
        "\n",
        "There are total 139711 users that satisfies the above criteria."
      ],
      "metadata": {
        "id": "eu-mRTpRTeMN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "publisher_counts = book['Publisher'].value_counts()\n",
        "top_publishers = publisher_counts.head(5)\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "ax.bar(top_publishers.index, top_publishers.values)\n",
        "ax.set_xlabel('Publisher')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_title('Top 5 Publishers by Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "CV4CHVhJT8DI",
        "outputId": "dbc9b67a-2dbe-4bba-9846-6998c45628aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "year_counts = book['Year-Of-Publication'].value_counts()\n",
        "\n",
        "# get the top 5 publication years by count\n",
        "top_years = year_counts.head(5)\n",
        "\n",
        "# create a bar chart of the top 5 publication years\n",
        "fig, ax = plt.subplots(figsize=(8, 6))\n",
        "ax.bar(top_years.index.astype(str), top_years.values)\n",
        "ax.set_xlabel('Publication Year')\n",
        "ax.set_ylabel('Count')\n",
        "ax.set_title('Top 5 Publication Years by Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "HmGULkcQT-TZ",
        "outputId": "e4bdfe5e-b434-4763-bd62-28bb2816fddf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Most of the books in the catalouge are published in the year 2002.**\n",
        "\n",
        "**Majority of books are being published by Harlequin**"
      ],
      "metadata": {
        "id": "EX-7Pa58cWI6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Further processing with Ratings Database"
      ],
      "metadata": {
        "id": "meWyoCNYcoEj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "usa_users.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "cs2VIpkTcsEz",
        "outputId": "84bfbfd4-d3e5-481c-a0ca-e35c971582f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   User-ID                       Location   Age country\n",
              "0        1             nyc, new york, usa   NaN     usa\n",
              "1        2      stockton, california, usa  18.0     usa\n",
              "5        6  santa monica, california, usa  61.0     usa\n",
              "6        7            washington, dc, usa   NaN     usa\n",
              "8        9     germantown, tennessee, usa   NaN     usa"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c33665f7-460b-4822-bcfb-b68f8eff5fb9\">\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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              "\n",
              "    .dataframe thead th {\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>User-ID</th>\n",
              "      <th>Location</th>\n",
              "      <th>Age</th>\n",
              "      <th>country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>nyc, new york, usa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>stockton, california, usa</td>\n",
              "      <td>18.0</td>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>santa monica, california, usa</td>\n",
              "      <td>61.0</td>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>washington, dc, usa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>germantown, tennessee, usa</td>\n",
              "      <td>NaN</td>\n",
              "      <td>usa</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c33665f7-460b-4822-bcfb-b68f8eff5fb9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-c33665f7-460b-4822-bcfb-b68f8eff5fb9 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-c33665f7-460b-4822-bcfb-b68f8eff5fb9');\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",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
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    },
    {
      "cell_type": "code",
      "source": [
        "rating.head()"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   User-ID        ISBN  Book-Rating\n",
              "0   276725  034545104X            0\n",
              "1   276726  0155061224            5\n",
              "2   276727  0446520802            0\n",
              "3   276729  052165615X            3\n",
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    {
      "cell_type": "code",
      "source": [
        "filtered_ratings = rating[rating['User-ID'].isin(usa_users['User-ID'])]"
      ],
      "metadata": {
        "id": "m4OSNswIc9Jr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "filtered_ratings.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZqtQ45cEdegM",
        "outputId": "b9317fe5-477b-4a02-b70c-97721109db0c"
      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    User-ID        ISBN  Book-Rating\n",
              "0    276725  034545104X            0\n",
              "1    276726  0155061224            5\n",
              "8    276744  038550120X            7\n",
              "16   276747  0060517794            9\n",
              "17   276747  0451192001            0"
            ],
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              "\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
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              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-963cbfbb-0308-472b-aac1-b2782386ad3b 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-963cbfbb-0308-472b-aac1-b2782386ad3b');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Original Rating Dataframe Shape: {rating.shape}\")\n",
        "print(f\"Usa_Users' Rating Dataframe Shape: {filtered_ratings.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OBnYGoKMdmkb",
        "outputId": "65a6d5c0-ea9d-4d1d-c010-3e4b5811a207"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Rating Dataframe Shape: (1149780, 3)\n",
            "Usa_Users' Rating Dataframe Shape: (768068, 3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_path = \"/content/drive/MyDrive/RS Data/Assignment 3/Processed_data/\"\n",
        "usa_users.to_csv(data_path+\"users.csv\")\n",
        "filtered_ratings.to_csv(data_path+\"ratings.csv\")"
      ],
      "metadata": {
        "id": "U_r3qWOdeD4r"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**But on further experimentation with above database the 0 ratings items were creating problems for the recommendation system**"
      ],
      "metadata": {
        "id": "jHtUeYNItaH6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rating['Book-Rating'].hist(bins=5)\n",
        "plt.xlabel('Rating')\n",
        "plt.ylabel('Frequency')\n",
        "plt.title('Distribution of Ratings')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "rcLFQ86-tBwK",
        "outputId": "8d71c048-bb10-4758-897d-f5d5e2ebb33c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}