Preprocessing_Data.ipynb

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    "colab": {
      "name": "Preprocessing Data.ipynb",
      "provenance": [],
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      "name": "python3",
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    {
      "cell_type": "markdown",
      "source": [
        "# <img src=\"https://img.icons8.com/color/48/undefined/1-circle--v1.png\"/>**Folder**"
      ],
      "metadata": {
        "id": "vRVhhP3EZN5w"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menyambungkan files dataset di google drive storage**"
      ],
      "metadata": {
        "id": "PdXmoZh024uK"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qMnKwQtMd1bF",
        "outputId": "0cf80d4b-710f-442a-e8ad-5ed95cb8cedf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <img src=\"https://img.icons8.com/color/48/undefined/2-circle--v1.png\"/>**Exploratory Data Analysis (EDA) and Visualization**"
      ],
      "metadata": {
        "id": "99UqUg9yeQBk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menyiapkan Libraries Python**"
      ],
      "metadata": {
        "id": "6Q2WW_Es0pmm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "e4NVvOH5eDQm"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menampilkan dataset join order**"
      ],
      "metadata": {
        "id": "xz9icLUaZh2j"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_excel(\"/content/drive/MyDrive/Colab Notebooks/sales/join_order.xlsx\")\n",
        "df"
      ],
      "metadata": {
        "id": "P4gXPWD7eIJT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "1a96e9fd-5538-4c27-c291-f251b05bc524"
      },
      "execution_count": null,
      "outputs": [
        {
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              "             id  order_id  user_id  product_id  inventory_item_id    status  \\\n",
              "0      142678.0   98658.0  79128.0     28951.0           385231.0  Returned   \n",
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              "...         ...       ...      ...         ...                ...       ...   \n",
              "17747   36550.0   25342.0  20487.0       184.0            98689.0  Returned   \n",
              "17748   83752.0   58020.0  46700.0       417.0           226288.0  Returned   \n",
              "17749   84922.0   58842.0  47374.0       184.0           229443.0  Returned   \n",
              "17750   98934.0   68545.0  55023.0       417.0           267194.0  Returned   \n",
              "17751  167813.0  115892.0  93110.0      6992.0           453234.0  Returned   \n",
              "\n",
              "      created_at shipped_at delivered_at returned_at    sale_price gender  \\\n",
              "0     2021-11-02 2021-11-02   2021-11-07  2021-11-08  3.000000e+01      M   \n",
              "1     2021-10-12 2021-10-13   2021-10-14  2021-10-16  3.000000e+01      M   \n",
              "2     2021-09-29 2021-10-01   2021-10-03  2021-10-05  3.250000e+02      M   \n",
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              "4     2022-05-11 2022-05-13   2022-05-15  2022-05-15  3.500000e+01      F   \n",
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              "17747 2022-05-30 2022-05-30   2022-06-02  2022-06-03  8.820000e+15      F   \n",
              "17748 2021-07-30 2021-07-31   2021-08-01  2021-08-03  8.820000e+15      F   \n",
              "17749 2022-03-23 2022-03-23   2022-03-28  2022-03-28  8.820000e+15      F   \n",
              "17750 2020-12-29 2020-12-31   2021-01-02  2021-01-05  8.820000e+15      F   \n",
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              "      <th>id</th>\n",
              "      <th>order_id</th>\n",
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              "      <td>2021-11-02</td>\n",
              "      <td>2021-11-02</td>\n",
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              "      <td>2021-11-08</td>\n",
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              "      <td>2021-10-16</td>\n",
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              "      <td>2021-01-28</td>\n",
              "      <td>2021-01-29</td>\n",
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              "      <td>2022-05-15</td>\n",
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              "      <td>2021-05-12</td>\n",
              "      <td>2021-05-14</td>\n",
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          },
          "metadata": {},
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menampilkan 5 data pertama dari dataset join order**"
      ],
      "metadata": {
        "id": "7vTiYyfV0w19"
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    {
      "cell_type": "code",
      "source": [
        "df.head()"
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          "height": 206
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        "id": "j9w3hw0BeWtR",
        "outputId": "0423fbf1-4503-4522-9b43-982b8462cd9a"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "         id  order_id  user_id  product_id  inventory_item_id    status  \\\n",
              "0  142678.0   98658.0  79128.0     28951.0           385231.0  Returned   \n",
              "1  152447.0  105326.0  84559.0     28951.0           411732.0  Returned   \n",
              "2   39237.0   27222.0  21978.0     28557.0           105905.0  Returned   \n",
              "3   43331.0   29987.0  24222.0     28679.0           116984.0  Returned   \n",
              "4  101499.0   70266.0  56436.0     15419.0           274105.0  Returned   \n",
              "\n",
              "  created_at shipped_at delivered_at returned_at  sale_price gender  \\\n",
              "0 2021-11-02 2021-11-02   2021-11-07  2021-11-08        30.0      M   \n",
              "1 2021-10-12 2021-10-13   2021-10-14  2021-10-16        30.0      M   \n",
              "2 2021-09-29 2021-10-01   2021-10-03  2021-10-05       325.0      M   \n",
              "3 2021-01-24 2021-01-27   2021-01-28  2021-01-29        35.0      M   \n",
              "4 2022-05-11 2022-05-13   2022-05-15  2022-05-15        35.0      F   \n",
              "\n",
              "   num_of_item  \n",
              "0          2.0  \n",
              "1          3.0  \n",
              "2          1.0  \n",
              "3          1.0  \n",
              "4          1.0  "
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>142678.0</td>\n",
              "      <td>98658.0</td>\n",
              "      <td>79128.0</td>\n",
              "      <td>28951.0</td>\n",
              "      <td>385231.0</td>\n",
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              "      <td>2021-11-02</td>\n",
              "      <td>2021-11-02</td>\n",
              "      <td>2021-11-07</td>\n",
              "      <td>2021-11-08</td>\n",
              "      <td>30.0</td>\n",
              "      <td>M</td>\n",
              "      <td>2.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>152447.0</td>\n",
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              "      <td>84559.0</td>\n",
              "      <td>28951.0</td>\n",
              "      <td>411732.0</td>\n",
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              "      <td>2021-10-12</td>\n",
              "      <td>2021-10-13</td>\n",
              "      <td>2021-10-14</td>\n",
              "      <td>2021-10-16</td>\n",
              "      <td>30.0</td>\n",
              "      <td>M</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>39237.0</td>\n",
              "      <td>27222.0</td>\n",
              "      <td>21978.0</td>\n",
              "      <td>28557.0</td>\n",
              "      <td>105905.0</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2021-09-29</td>\n",
              "      <td>2021-10-01</td>\n",
              "      <td>2021-10-03</td>\n",
              "      <td>2021-10-05</td>\n",
              "      <td>325.0</td>\n",
              "      <td>M</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>43331.0</td>\n",
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              "      <td>24222.0</td>\n",
              "      <td>28679.0</td>\n",
              "      <td>116984.0</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2021-01-24</td>\n",
              "      <td>2021-01-27</td>\n",
              "      <td>2021-01-28</td>\n",
              "      <td>2021-01-29</td>\n",
              "      <td>35.0</td>\n",
              "      <td>M</td>\n",
              "      <td>1.0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>101499.0</td>\n",
              "      <td>70266.0</td>\n",
              "      <td>56436.0</td>\n",
              "      <td>15419.0</td>\n",
              "      <td>274105.0</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2022-05-11</td>\n",
              "      <td>2022-05-13</td>\n",
              "      <td>2022-05-15</td>\n",
              "      <td>2022-05-15</td>\n",
              "      <td>35.0</td>\n",
              "      <td>F</td>\n",
              "      <td>1.0</td>\n",
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              "\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",
              "          element.innerHTML = '';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menampilkan 5 data terakhir dari dataset join order**"
      ],
      "metadata": {
        "id": "iTNGrLu002wm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Q54JVwxEeZ_a",
        "outputId": "ae81b55a-d21c-4fae-ef23-1bac195cd652"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             id  order_id  user_id  product_id  inventory_item_id    status  \\\n",
              "17747   36550.0   25342.0  20487.0       184.0            98689.0  Returned   \n",
              "17748   83752.0   58020.0  46700.0       417.0           226288.0  Returned   \n",
              "17749   84922.0   58842.0  47374.0       184.0           229443.0  Returned   \n",
              "17750   98934.0   68545.0  55023.0       417.0           267194.0  Returned   \n",
              "17751  167813.0  115892.0  93110.0      6992.0           453234.0  Returned   \n",
              "\n",
              "      created_at shipped_at delivered_at returned_at    sale_price gender  \\\n",
              "17747 2022-05-30 2022-05-30   2022-06-02  2022-06-03  8.820000e+15      F   \n",
              "17748 2021-07-30 2021-07-31   2021-08-01  2021-08-03  8.820000e+15      F   \n",
              "17749 2022-03-23 2022-03-23   2022-03-28  2022-03-28  8.820000e+15      F   \n",
              "17750 2020-12-29 2020-12-31   2021-01-02  2021-01-05  8.820000e+15      F   \n",
              "17751 2021-05-12 2021-05-12   2021-05-14  2021-05-17  8.820000e+15      F   \n",
              "\n",
              "       num_of_item  \n",
              "17747          4.0  \n",
              "17748          4.0  \n",
              "17749          3.0  \n",
              "17750          3.0  \n",
              "17751          2.0  "
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              "      <th>num_of_item</th>\n",
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              "      <th>17747</th>\n",
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              "      <td>20487.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>98689.0</td>\n",
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              "      <td>2022-05-30</td>\n",
              "      <td>2022-05-30</td>\n",
              "      <td>2022-06-02</td>\n",
              "      <td>2022-06-03</td>\n",
              "      <td>8.820000e+15</td>\n",
              "      <td>F</td>\n",
              "      <td>4.0</td>\n",
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              "      <td>46700.0</td>\n",
              "      <td>417.0</td>\n",
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              "      <td>2021-07-30</td>\n",
              "      <td>2021-07-31</td>\n",
              "      <td>2021-08-01</td>\n",
              "      <td>2021-08-03</td>\n",
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              "      <td>47374.0</td>\n",
              "      <td>184.0</td>\n",
              "      <td>229443.0</td>\n",
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              "      <td>2022-03-23</td>\n",
              "      <td>2022-03-23</td>\n",
              "      <td>2022-03-28</td>\n",
              "      <td>2022-03-28</td>\n",
              "      <td>8.820000e+15</td>\n",
              "      <td>F</td>\n",
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              "    <tr>\n",
              "      <th>17750</th>\n",
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              "      <td>55023.0</td>\n",
              "      <td>417.0</td>\n",
              "      <td>267194.0</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>2021-01-02</td>\n",
              "      <td>2021-01-05</td>\n",
              "      <td>8.820000e+15</td>\n",
              "      <td>F</td>\n",
              "      <td>3.0</td>\n",
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              "    <tr>\n",
              "      <th>17751</th>\n",
              "      <td>167813.0</td>\n",
              "      <td>115892.0</td>\n",
              "      <td>93110.0</td>\n",
              "      <td>6992.0</td>\n",
              "      <td>453234.0</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2021-05-12</td>\n",
              "      <td>2021-05-12</td>\n",
              "      <td>2021-05-14</td>\n",
              "      <td>2021-05-17</td>\n",
              "      <td>8.820000e+15</td>\n",
              "      <td>F</td>\n",
              "      <td>2.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-56ca98c4-6487-4655-88f6-8c4c7445d4a6')\"\n",
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              "\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",
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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-56ca98c4-6487-4655-88f6-8c4c7445d4a6 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-56ca98c4-6487-4655-88f6-8c4c7445d4a6');\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": 106
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menghitung jumlah baris dan kolom dari dataset**"
      ],
      "metadata": {
        "id": "mTfgIi_Q0-K0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Gb5oKFjecq3",
        "outputId": "56905357-1f03-45c6-fe12-aa5fa3c3c952"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17752, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 107
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ***pada dataset ini mempunyai 17752 baris and 13 kolom.***"
      ],
      "metadata": {
        "id": "ijoCvJLM1FK7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menampilkan nama kolom dengan jumlah data masing-masing kolom dan tipe data yang digunakan, serta menghitung ukuran file dataset**"
      ],
      "metadata": {
        "id": "-_mUiHk01ri3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uYsvmZGLepT0",
        "outputId": "e6b6b8b3-71da-49dc-a75c-83c68ee4a11c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 17752 entries, 0 to 17751\n",
            "Data columns (total 13 columns):\n",
            " #   Column             Non-Null Count  Dtype         \n",
            "---  ------             --------------  -----         \n",
            " 0   id                 17752 non-null  float64       \n",
            " 1   order_id           17752 non-null  float64       \n",
            " 2   user_id            17752 non-null  float64       \n",
            " 3   product_id         17752 non-null  float64       \n",
            " 4   inventory_item_id  17752 non-null  float64       \n",
            " 5   status             17752 non-null  object        \n",
            " 6   created_at         17752 non-null  datetime64[ns]\n",
            " 7   shipped_at         17752 non-null  datetime64[ns]\n",
            " 8   delivered_at       17752 non-null  datetime64[ns]\n",
            " 9   returned_at        17752 non-null  datetime64[ns]\n",
            " 10  sale_price         17752 non-null  float64       \n",
            " 11  gender             17752 non-null  object        \n",
            " 12  num_of_item        17752 non-null  float64       \n",
            "dtypes: datetime64[ns](4), float64(7), object(2)\n",
            "memory usage: 1.8+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **menampilkan nama kolom dan tipe data yang digunakan**"
      ],
      "metadata": {
        "id": "W1Wys66Y1QgH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZcCrVnUkefSD",
        "outputId": "18b9095e-cf90-4a31-a20d-0dbd21fb4ba9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                          float64\n",
              "order_id                    float64\n",
              "user_id                     float64\n",
              "product_id                  float64\n",
              "inventory_item_id           float64\n",
              "status                       object\n",
              "created_at           datetime64[ns]\n",
              "shipped_at           datetime64[ns]\n",
              "delivered_at         datetime64[ns]\n",
              "returned_at          datetime64[ns]\n",
              "sale_price                  float64\n",
              "gender                       object\n",
              "num_of_item                 float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **Menghitung jumlah Non-Missing Values dari setiap variable**"
      ],
      "metadata": {
        "id": "xdHdQbAS1eR1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C87tjMIlehh5",
        "outputId": "f6cfefa6-3641-4767-f5ab-7816888e0a26"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   17752\n",
              "order_id             17752\n",
              "user_id              17752\n",
              "product_id           17752\n",
              "inventory_item_id    17752\n",
              "status               17752\n",
              "created_at           17752\n",
              "shipped_at           17752\n",
              "delivered_at         17752\n",
              "returned_at          17752\n",
              "sale_price           17752\n",
              "gender               17752\n",
              "num_of_item          17752\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **total 17752 data yang tidak memiliki missing value**"
      ],
      "metadata": {
        "id": "yRkK1-BZanOw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **mendeskripsikan dan menghitung nilai rata-rata setiap kolom dataset**"
      ],
      "metadata": {
        "id": "nhy5QETp1oI-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "-L6iOfC4el8V",
        "outputId": "c5a55939-fbbe-4059-b6cd-54635229fed1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  id       order_id       user_id    product_id  \\\n",
              "count   17752.000000   17752.000000  17752.000000  17752.000000   \n",
              "mean    90644.236537   62675.719074  50385.719468  15151.775687   \n",
              "std     52510.971853   36269.656689  29062.411279   8435.641404   \n",
              "min        17.000000       9.000000      6.000000      1.000000   \n",
              "25%     44260.000000   30647.500000  24749.750000   7755.750000   \n",
              "50%     91386.500000   63307.000000  50879.000000  15801.500000   \n",
              "75%    136279.750000   94271.750000  75665.750000  22428.500000   \n",
              "max    180504.000000  124508.000000  99997.000000  29120.000000   \n",
              "\n",
              "       inventory_item_id    sale_price   num_of_item  \n",
              "count       17752.000000  1.775200e+04  17752.000000  \n",
              "mean       244774.486480  8.137358e+15      1.902321  \n",
              "std        141798.943611  1.296444e+16      1.049199  \n",
              "min            45.000000  3.000000e+01      1.000000  \n",
              "25%        119485.750000  5.500000e+02      1.000000  \n",
              "50%        246820.500000  4.000000e+14      2.000000  \n",
              "75%        367954.500000  1.270000e+16      2.000000  \n",
              "max        487384.000000  7.160000e+16      4.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f00ae4f-e3d0-4838-b1e0-f5072bb4c423\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>order_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>inventory_item_id</th>\n",
              "      <th>sale_price</th>\n",
              "      <th>num_of_item</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17752.000000</td>\n",
              "      <td>17752.000000</td>\n",
              "      <td>17752.000000</td>\n",
              "      <td>17752.000000</td>\n",
              "      <td>17752.000000</td>\n",
              "      <td>1.775200e+04</td>\n",
              "      <td>17752.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>90644.236537</td>\n",
              "      <td>62675.719074</td>\n",
              "      <td>50385.719468</td>\n",
              "      <td>15151.775687</td>\n",
              "      <td>244774.486480</td>\n",
              "      <td>8.137358e+15</td>\n",
              "      <td>1.902321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>52510.971853</td>\n",
              "      <td>36269.656689</td>\n",
              "      <td>29062.411279</td>\n",
              "      <td>8435.641404</td>\n",
              "      <td>141798.943611</td>\n",
              "      <td>1.296444e+16</td>\n",
              "      <td>1.049199</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>17.000000</td>\n",
              "      <td>9.000000</td>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>45.000000</td>\n",
              "      <td>3.000000e+01</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>44260.000000</td>\n",
              "      <td>30647.500000</td>\n",
              "      <td>24749.750000</td>\n",
              "      <td>7755.750000</td>\n",
              "      <td>119485.750000</td>\n",
              "      <td>5.500000e+02</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>91386.500000</td>\n",
              "      <td>63307.000000</td>\n",
              "      <td>50879.000000</td>\n",
              "      <td>15801.500000</td>\n",
              "      <td>246820.500000</td>\n",
              "      <td>4.000000e+14</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>136279.750000</td>\n",
              "      <td>94271.750000</td>\n",
              "      <td>75665.750000</td>\n",
              "      <td>22428.500000</td>\n",
              "      <td>367954.500000</td>\n",
              "      <td>1.270000e+16</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>180504.000000</td>\n",
              "      <td>124508.000000</td>\n",
              "      <td>99997.000000</td>\n",
              "      <td>29120.000000</td>\n",
              "      <td>487384.000000</td>\n",
              "      <td>7.160000e+16</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f00ae4f-e3d0-4838-b1e0-f5072bb4c423')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f00ae4f-e3d0-4838-b1e0-f5072bb4c423 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-0f00ae4f-e3d0-4838-b1e0-f5072bb4c423');\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": 111
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **Data Cleansing**"
      ],
      "metadata": {
        "id": "-Y4bMuBjfVi6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **mengecek apakah masih ada missing value dari setiap variabel**"
      ],
      "metadata": {
        "id": "YfsPSaFt10Uo"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **hapus data missng value jika masih ada**"
      ],
      "metadata": {
        "id": "Ny-ofrtnrwVf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(how='any', inplace=True)"
      ],
      "metadata": {
        "id": "bermj7GZewmB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **mengecek data dan menampilkan data jika bernilai 0, maka tidak ada missing value, jika bernilai lebih dari 0, maka itu jumlah missing value**"
      ],
      "metadata": {
        "id": "MHGHKb-9r7PF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "75rvxIB-gLPo",
        "outputId": "b800a10b-4d01-40bf-c0c3-3346faa42158"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                   0\n",
              "order_id             0\n",
              "user_id              0\n",
              "product_id           0\n",
              "inventory_item_id    0\n",
              "status               0\n",
              "created_at           0\n",
              "shipped_at           0\n",
              "delivered_at         0\n",
              "returned_at          0\n",
              "sale_price           0\n",
              "gender               0\n",
              "num_of_item          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <img src=\"https://img.icons8.com/color/48/undefined/3-circle--v1.png\"/>**Data Visualizations**"
      ],
      "metadata": {
        "id": "A7oKlk70gVtn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "vhZv8b1bg_Gy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **visualisasi pada created_at**"
      ],
      "metadata": {
        "id": "V5o7NaFxbOlW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['created_at'] = pd.to_datetime(df['created_at'])\n",
        "\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.countplot(x = df['created_at'], palette=\"rocket\", linewidth = 1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "FduE1SjLHhcy",
        "outputId": "07b5a09e-f04f-4335-8b22-d2c55b4b9354"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **visualisasi pada shipped_at**"
      ],
      "metadata": {
        "id": "LCCKKnMsbXL6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['shipped_at'] = pd.to_datetime(df['shipped_at'])\n",
        "\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.countplot(x = df['shipped_at'], palette=\"mako\", linewidth = 1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "uIef3DHsDqFF",
        "outputId": "737162b1-9635-4b86-d323-d6c10ace249d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **visualisasi pada delivered_at**"
      ],
      "metadata": {
        "id": "u8RHFTKYbaPA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['delivered_at'] = pd.to_datetime(df['delivered_at'])\n",
        "\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.countplot(x = df['delivered_at'], palette=\"flare\", linewidth = 1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "2gJMRFK6Jeco",
        "outputId": "075eb608-b359-475f-9fe0-dff76c58cc1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **visualisasi pada returned_at**"
      ],
      "metadata": {
        "id": "UvSlZHLMbd4a"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df['returned_at'] = pd.to_datetime(df['returned_at'])\n",
        "\n",
        "plt.figure(figsize=(18,5))\n",
        "sns.countplot(x = df['returned_at'], palette=\"Blues\", linewidth = 1)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "DNbF2UMvJOTb",
        "outputId": "f33a8dd7-5cf9-4931-fa5b-a5567a8d0af0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **visualisasi pada gender dan number item**"
      ],
      "metadata": {
        "id": "Nplbvw5Dbhdc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x,y = 'gender', 'num_of_item'\n",
        "\n",
        "df1 = df.groupby(x)[y].value_counts(normalize=True)\n",
        "df1 = df1.mul(100)\n",
        "df1 = df1.rename('percent').reset_index()\n",
        "\n",
        "g = sns.catplot(x=x,y='percent',hue=y,kind='bar',data=df1)\n",
        "g.ax.set_ylim(0,100)\n",
        "\n",
        "for p in g.ax.patches:\n",
        "    txt = str(p.get_height().round(2)) + '%'\n",
        "    txt_x = p.get_x() \n",
        "    txt_y = p.get_height()\n",
        "    g.ax.text(txt_x,txt_y,txt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "id": "aMuqiMQHwp44",
        "outputId": "c6dec3fb-0968-4e2b-cbb0-c4b70a014114"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432.5x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **visualisasi pada gender dan status returned**"
      ],
      "metadata": {
        "id": "YRDJBLMAbmZf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "x,y = 'status', 'gender'\n",
        "\n",
        "df1 = df.groupby(x)[y].value_counts(normalize=True)\n",
        "df1 = df1.mul(100)\n",
        "df1 = df1.rename('percent').reset_index()\n",
        "\n",
        "g = sns.catplot(x=x,y='percent',hue=y,kind='bar',data=df1)\n",
        "g.ax.set_ylim(0,100)\n",
        "\n",
        "for p in g.ax.patches:\n",
        "    txt = str(p.get_height().round(2)) + '%'\n",
        "    txt_x = p.get_x() \n",
        "    txt_y = p.get_height()\n",
        "    g.ax.text(txt_x,txt_y,txt)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 373
        },
        "id": "nIh4K0V6gUvp",
        "outputId": "3ba344a9-4cec-4eb7-e413-fffd2ac3917b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 404.625x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **visualisasi presentase number item**"
      ],
      "metadata": {
        "id": "rnAPOlnYbrzZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.pie(df['num_of_item'].value_counts(), labels = df['num_of_item'].unique(),autopct='%1.2f%%')\n",
        "print(\"Number Of Items\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ORvBPHW9kgmO",
        "outputId": "8fd56a96-9dbb-4115-8f7c-bdb3a2056d7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number Of Items\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <img src=\"https://img.icons8.com/color/48/undefined/4-circle--v1.png\"/>**Data Spliting**"
      ],
      "metadata": {
        "id": "ab-KvnKHw6bo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "import json\n",
        "import pandas as pd\n",
        "from pprint import pprint\n",
        "\n",
        "df = pd.read_excel('/content/drive/MyDrive/Colab Notebooks/sales/join_order.xlsx')\n",
        "\n",
        "texts = []\n",
        "for id, order_id, user_id, product_id, inventory_item_id, status, created_at, shipped_at, delivered_at, returned_at, sale_price, gender, num_of_item in zip(\n",
        "    df['id'].tolist(), df['order_id'].tolist(), df['user_id'].tolist(), df['product_id'].tolist(), df['inventory_item_id'].tolist(), \n",
        "    df['status'].tolist(), df['created_at'].tolist(), df['shipped_at'].tolist(), df['delivered_at'].tolist(), df['returned_at'].tolist(), \n",
        "    df['sale_price'].tolist(), df['gender'].tolist(), df['num_of_item'].tolist()\n",
        "    ):\n",
        "    created_at = pd.to_datetime(created_at).date()\n",
        "    shipped_at = pd.to_datetime(shipped_at).date()\n",
        "    delivered_at = pd.to_datetime(delivered_at).date()\n",
        "    returned_at = pd.to_datetime(returned_at).date()\n",
        "    texts.append([id, order_id, user_id, product_id, inventory_item_id, status, created_at, shipped_at, delivered_at, returned_at, sale_price, gender, num_of_item])\n",
        "\n",
        "df = pd.DataFrame(texts, columns=['id', 'order_id', 'user_id', 'product_id', 'inventory_item_id', 'status', 'created_at', 'shipped_at', 'delivered_at', 'returned_at', 'sale_price', 'gender', 'num_of_item'])\n"
      ],
      "metadata": {
        "id": "cHKiSdbfw_Q2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **Train Data**"
      ],
      "metadata": {
        "id": "NKE610l2xBTA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install xlsxwriter\n",
        "\n",
        "import xlsxwriter"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7pGyaPyb3xzu",
        "outputId": "d6923077-3a75-4275-a4a3-6c89f7773452"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: xlsxwriter in /usr/local/lib/python3.7/dist-packages (3.0.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_df = df.sample(frac=0.8, random_state=1)\n",
        "\n",
        "writer = pd.ExcelWriter('/content/drive/MyDrive/Colab Notebooks/sales/train.xlsx', engine='xlsxwriter')\n",
        "train_df.to_excel(writer, sheet_name='Sheet1')\n",
        "writer.save()"
      ],
      "metadata": {
        "id": "gZ1oswNBxO2m"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_excel(\"/content/drive/MyDrive/Colab Notebooks/sales/train.xlsx\")\n",
        "train"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "Btr8LmqaAD7i",
        "outputId": "f45aac52-371c-4e2b-d8e7-8b46f29a8af1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Unnamed: 0      id  order_id  user_id  product_id  inventory_item_id  \\\n",
              "0           12492   31660     21949    17788       27121              85413   \n",
              "1           17335   41973     29068    23456       26589             113276   \n",
              "2            5358   65426     45219    36473       22173             176718   \n",
              "3           16007   31184     21609    17522        8501              84139   \n",
              "4           17319  157163    108588    87215       15906             424487   \n",
              "...           ...     ...       ...      ...         ...                ...   \n",
              "14197        9096   93544     64807    52054       28191             252675   \n",
              "14198       15486  148953    102917    82627        6298             402267   \n",
              "14199        1670  132080     91333    73259         536             356584   \n",
              "14200        9917   52956     36603    29562       20049             143006   \n",
              "14201       16382   46433     32125    25964       19880             125414   \n",
              "\n",
              "         status created_at shipped_at delivered_at returned_at  \\\n",
              "0      Returned 2020-09-28 2020-09-25   2020-09-30  2020-09-30   \n",
              "1      Returned 2021-10-09 2021-10-11   2021-10-13  2021-10-14   \n",
              "2      Returned 2021-12-09 2021-12-08   2021-12-08  2021-12-11   \n",
              "3      Returned 2021-12-06 2021-12-07   2021-12-12  2021-12-13   \n",
              "4      Returned 2022-05-07 2022-05-07   2022-05-08  2022-05-09   \n",
              "...         ...        ...        ...          ...         ...   \n",
              "14197  Returned 2020-07-07 2020-07-10   2020-07-10  2020-07-11   \n",
              "14198  Returned 2020-12-11 2020-12-12   2020-12-16  2020-12-18   \n",
              "14199  Returned 2022-03-30 2022-03-28   2022-03-29  2022-04-01   \n",
              "14200  Returned 2022-06-03 2022-06-02   2022-06-05  2022-06-08   \n",
              "14201  Returned 2021-09-13 2021-09-15   2021-09-17  2021-09-20   \n",
              "\n",
              "              sale_price gender  num_of_item  \n",
              "0        600000000000000      M            2  \n",
              "1       7500000000000000      M            3  \n",
              "2                   1395      M            4  \n",
              "3      18900000000000000      F            1  \n",
              "4       9800000000000000      F            4  \n",
              "...                  ...    ...          ...  \n",
              "14197  13000000000000000      M            1  \n",
              "14198  11900000000000000      F            1  \n",
              "14199                250      F            2  \n",
              "14200  59900000000000000      M            2  \n",
              "14201  10000000000000000      M            3  \n",
              "\n",
              "[14202 rows x 14 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th>delivered_at</th>\n",
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              "      <th>sale_price</th>\n",
              "      <th>gender</th>\n",
              "      <th>num_of_item</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12492</td>\n",
              "      <td>31660</td>\n",
              "      <td>21949</td>\n",
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              "      <td>2020-09-28</td>\n",
              "      <td>2020-09-25</td>\n",
              "      <td>2020-09-30</td>\n",
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              "      <td>29068</td>\n",
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              "      <td>2021-10-09</td>\n",
              "      <td>2021-10-11</td>\n",
              "      <td>2021-10-13</td>\n",
              "      <td>2021-10-14</td>\n",
              "      <td>7500000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
              "      <td>5358</td>\n",
              "      <td>65426</td>\n",
              "      <td>45219</td>\n",
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              "      <td>2021-12-08</td>\n",
              "      <td>2021-12-08</td>\n",
              "      <td>2021-12-11</td>\n",
              "      <td>1395</td>\n",
              "      <td>M</td>\n",
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              "    <tr>\n",
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              "      <td>21609</td>\n",
              "      <td>17522</td>\n",
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              "      <td>84139</td>\n",
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              "      <td>2021-12-07</td>\n",
              "      <td>2021-12-12</td>\n",
              "      <td>2021-12-13</td>\n",
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              "      <th>4</th>\n",
              "      <td>17319</td>\n",
              "      <td>157163</td>\n",
              "      <td>108588</td>\n",
              "      <td>87215</td>\n",
              "      <td>15906</td>\n",
              "      <td>424487</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2022-05-07</td>\n",
              "      <td>2022-05-07</td>\n",
              "      <td>2022-05-08</td>\n",
              "      <td>2022-05-09</td>\n",
              "      <td>9800000000000000</td>\n",
              "      <td>F</td>\n",
              "      <td>4</td>\n",
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              "    <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",
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              "    <tr>\n",
              "      <th>14197</th>\n",
              "      <td>9096</td>\n",
              "      <td>93544</td>\n",
              "      <td>64807</td>\n",
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              "      <td>28191</td>\n",
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              "      <td>2020-07-07</td>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>2020-07-10</td>\n",
              "      <td>2020-07-11</td>\n",
              "      <td>13000000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>14198</th>\n",
              "      <td>15486</td>\n",
              "      <td>148953</td>\n",
              "      <td>102917</td>\n",
              "      <td>82627</td>\n",
              "      <td>6298</td>\n",
              "      <td>402267</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2020-12-11</td>\n",
              "      <td>2020-12-12</td>\n",
              "      <td>2020-12-16</td>\n",
              "      <td>2020-12-18</td>\n",
              "      <td>11900000000000000</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>14199</th>\n",
              "      <td>1670</td>\n",
              "      <td>132080</td>\n",
              "      <td>91333</td>\n",
              "      <td>73259</td>\n",
              "      <td>536</td>\n",
              "      <td>356584</td>\n",
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              "      <td>2022-03-30</td>\n",
              "      <td>2022-03-28</td>\n",
              "      <td>2022-03-29</td>\n",
              "      <td>2022-04-01</td>\n",
              "      <td>250</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14200</th>\n",
              "      <td>9917</td>\n",
              "      <td>52956</td>\n",
              "      <td>36603</td>\n",
              "      <td>29562</td>\n",
              "      <td>20049</td>\n",
              "      <td>143006</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2022-06-03</td>\n",
              "      <td>2022-06-02</td>\n",
              "      <td>2022-06-05</td>\n",
              "      <td>2022-06-08</td>\n",
              "      <td>59900000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>14201</th>\n",
              "      <td>16382</td>\n",
              "      <td>46433</td>\n",
              "      <td>32125</td>\n",
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              "      <td>19880</td>\n",
              "      <td>125414</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2021-09-13</td>\n",
              "      <td>2021-09-15</td>\n",
              "      <td>2021-09-17</td>\n",
              "      <td>2021-09-20</td>\n",
              "      <td>10000000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>14202 rows × 14 columns</p>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-305a61cc-064a-4334-a405-b5cb3f9b2b18')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **Test Data**"
      ],
      "metadata": {
        "id": "7uZ25VtWxEX-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "test_df = df.drop(train_df.index)\n",
        "\n",
        "writer = pd.ExcelWriter('/content/drive/MyDrive/Colab Notebooks/sales/test.xlsx', engine='xlsxwriter')\n",
        "test_df.to_excel(writer, sheet_name='Sheet1')\n",
        "writer.save()"
      ],
      "metadata": {
        "id": "RielNLGtxPxM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "test = pd.read_excel(\"/content/drive/MyDrive/Colab Notebooks/sales/test.xlsx\")\n",
        "test"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "lyEomiSZAK7V",
        "outputId": "558a42cf-2f6e-4fb9-f8df-ff5e0121c19c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0      id  order_id  user_id  product_id  inventory_item_id  \\\n",
              "0              9    4805      3295     2662       28370              13038   \n",
              "1             14  177017    122150    98116        9002             477975   \n",
              "2             15      74        51       33        9380                198   \n",
              "3             18   67362     46557    37544       13840             181949   \n",
              "4             20   89666     62129    49973       15667             242242   \n",
              "...          ...     ...       ...      ...         ...                ...   \n",
              "3545       17723  145163    100321    80484       23505             391953   \n",
              "3546       17728   51234     35442    28673         201             138334   \n",
              "3547       17734   46432     32125    25964       28895             125412   \n",
              "3548       17739  169319    116946    93981        3429             457289   \n",
              "3549       17750   98934     68545    55023         417             267194   \n",
              "\n",
              "        status created_at shipped_at delivered_at returned_at  \\\n",
              "0     Returned 2022-05-08 2022-05-11   2022-05-13  2022-05-15   \n",
              "1     Returned 2022-04-28 2022-04-30   2022-05-02  2022-05-04   \n",
              "2     Returned 2019-09-02 2019-09-03   2019-09-03  2019-09-04   \n",
              "3     Returned 2022-01-09 2022-01-12   2022-01-16  2022-01-17   \n",
              "4     Returned 2022-02-05 2022-02-06   2022-02-07  2022-02-07   \n",
              "...        ...        ...        ...          ...         ...   \n",
              "3545  Returned 2022-04-28 2022-04-27   2022-04-28  2022-04-30   \n",
              "3546  Returned 2022-05-28 2022-05-24   2022-05-25  2022-05-28   \n",
              "3547  Returned 2021-09-15 2021-09-15   2021-09-17  2021-09-20   \n",
              "3548  Returned 2020-09-19 2020-09-20   2020-09-24  2020-09-26   \n",
              "3549  Returned 2020-12-29 2020-12-31   2021-01-02  2021-01-05   \n",
              "\n",
              "            sale_price gender  num_of_item  \n",
              "0                   45      M            1  \n",
              "1                   50      F            1  \n",
              "2                   55      F            1  \n",
              "3                   60      F            1  \n",
              "4                   60      F            1  \n",
              "...                ...    ...          ...  \n",
              "3545  2360000000000000      M            4  \n",
              "3546  1290000000000000      F            3  \n",
              "3547  2390000000000000      M            3  \n",
              "3548  2890000000000000      F            1  \n",
              "3549  8820000000000000      F            3  \n",
              "\n",
              "[3550 rows x 14 columns]"
            ],
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              "      <td>2022-05-11</td>\n",
              "      <td>2022-05-13</td>\n",
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              "      <td>2022-04-28</td>\n",
              "      <td>2022-04-30</td>\n",
              "      <td>2022-05-02</td>\n",
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              "      <td>15</td>\n",
              "      <td>74</td>\n",
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              "      <td>9380</td>\n",
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              "      <td>2019-09-02</td>\n",
              "      <td>2019-09-03</td>\n",
              "      <td>2019-09-03</td>\n",
              "      <td>2019-09-04</td>\n",
              "      <td>55</td>\n",
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              "      <th>3</th>\n",
              "      <td>18</td>\n",
              "      <td>67362</td>\n",
              "      <td>46557</td>\n",
              "      <td>37544</td>\n",
              "      <td>13840</td>\n",
              "      <td>181949</td>\n",
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              "      <td>2022-01-09</td>\n",
              "      <td>2022-01-12</td>\n",
              "      <td>2022-01-16</td>\n",
              "      <td>2022-01-17</td>\n",
              "      <td>60</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>20</td>\n",
              "      <td>89666</td>\n",
              "      <td>62129</td>\n",
              "      <td>49973</td>\n",
              "      <td>15667</td>\n",
              "      <td>242242</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2022-02-05</td>\n",
              "      <td>2022-02-06</td>\n",
              "      <td>2022-02-07</td>\n",
              "      <td>2022-02-07</td>\n",
              "      <td>60</td>\n",
              "      <td>F</td>\n",
              "      <td>1</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3545</th>\n",
              "      <td>17723</td>\n",
              "      <td>145163</td>\n",
              "      <td>100321</td>\n",
              "      <td>80484</td>\n",
              "      <td>23505</td>\n",
              "      <td>391953</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2022-04-28</td>\n",
              "      <td>2022-04-27</td>\n",
              "      <td>2022-04-28</td>\n",
              "      <td>2022-04-30</td>\n",
              "      <td>2360000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3546</th>\n",
              "      <td>17728</td>\n",
              "      <td>51234</td>\n",
              "      <td>35442</td>\n",
              "      <td>28673</td>\n",
              "      <td>201</td>\n",
              "      <td>138334</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2022-05-28</td>\n",
              "      <td>2022-05-24</td>\n",
              "      <td>2022-05-25</td>\n",
              "      <td>2022-05-28</td>\n",
              "      <td>1290000000000000</td>\n",
              "      <td>F</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3547</th>\n",
              "      <td>17734</td>\n",
              "      <td>46432</td>\n",
              "      <td>32125</td>\n",
              "      <td>25964</td>\n",
              "      <td>28895</td>\n",
              "      <td>125412</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2021-09-15</td>\n",
              "      <td>2021-09-15</td>\n",
              "      <td>2021-09-17</td>\n",
              "      <td>2021-09-20</td>\n",
              "      <td>2390000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3548</th>\n",
              "      <td>17739</td>\n",
              "      <td>169319</td>\n",
              "      <td>116946</td>\n",
              "      <td>93981</td>\n",
              "      <td>3429</td>\n",
              "      <td>457289</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2020-09-19</td>\n",
              "      <td>2020-09-20</td>\n",
              "      <td>2020-09-24</td>\n",
              "      <td>2020-09-26</td>\n",
              "      <td>2890000000000000</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3549</th>\n",
              "      <td>17750</td>\n",
              "      <td>98934</td>\n",
              "      <td>68545</td>\n",
              "      <td>55023</td>\n",
              "      <td>417</td>\n",
              "      <td>267194</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2020-12-29</td>\n",
              "      <td>2020-12-31</td>\n",
              "      <td>2021-01-02</td>\n",
              "      <td>2021-01-05</td>\n",
              "      <td>8820000000000000</td>\n",
              "      <td>F</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3550 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3232a6aa-4fea-4ff7-a6be-a048f2e89438')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## <img src=\"https://img.icons8.com/parakeet/24/undefined/experimental-arrow-parakeet.png\"/> **Validation Data**"
      ],
      "metadata": {
        "id": "b5GZbTijxJJC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "val_df = test_df.sample(frac=0.4, random_state=1024)\n",
        "\n",
        "writer = pd.ExcelWriter('/content/drive/MyDrive/Colab Notebooks/sales/val.xlsx', engine='xlsxwriter')\n",
        "val_df.to_excel(writer, sheet_name='Sheet1')\n",
        "writer.save()"
      ],
      "metadata": {
        "id": "HHfYNmslxQje"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val = pd.read_excel(\"/content/drive/MyDrive/Colab Notebooks/sales/val.xlsx\")\n",
        "val"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 467
        },
        "id": "hbla6iCBAPiP",
        "outputId": "855ae4b7-7e8b-481d-a6ce-91e0a2d9ab40"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Unnamed: 0      id  order_id  user_id  product_id  inventory_item_id  \\\n",
              "0          15383   16693     11506     9402       19675              45108   \n",
              "1          11970    2115      1464     1158        2020               5747   \n",
              "2          14384    6963      4782     3892        3028              18872   \n",
              "3          10856   95499     66151    53127       13821             257950   \n",
              "4           7695  137445     95072    76289       27161             371083   \n",
              "...          ...     ...       ...      ...         ...                ...   \n",
              "1415        9887   95238     65964    52980       16175             257249   \n",
              "1416        5909   96098     66561    53446       23188             259545   \n",
              "1417       10777   41410     28685    23155       11038             111763   \n",
              "1418       11401  170597    117821    94709       16771             460706   \n",
              "1419       14359   59798     41355    33398        3847             161499   \n",
              "\n",
              "        status created_at shipped_at delivered_at returned_at  \\\n",
              "0     Returned 2021-02-15 2021-02-16   2021-02-19  2021-02-22   \n",
              "1     Returned 2021-10-08 2021-10-10   2021-10-12  2021-10-14   \n",
              "2     Returned 2022-02-27 2022-02-26   2022-02-26  2022-03-01   \n",
              "3     Returned 2021-03-24 2021-03-25   2021-03-28  2021-03-30   \n",
              "4     Returned 2020-12-23 2020-12-26   2020-12-28  2020-12-28   \n",
              "...        ...        ...        ...          ...         ...   \n",
              "1415  Returned 2021-11-22 2021-11-22   2021-11-22  2021-11-22   \n",
              "1416  Returned 2021-01-12 2021-01-11   2021-01-15  2021-01-17   \n",
              "1417  Returned 2019-11-01 2019-11-04   2019-11-05  2019-11-07   \n",
              "1418  Returned 2019-10-26 2019-10-29   2019-10-30  2019-11-02   \n",
              "1419  Returned 2022-03-23 2022-03-23   2022-03-27  2022-03-27   \n",
              "\n",
              "             sale_price gender  num_of_item  \n",
              "0      2940000000000000      M            2  \n",
              "1       400000000000000      F            1  \n",
              "2     30000000000000000      F            2  \n",
              "3       831000000000000      F            3  \n",
              "4                   950      M            1  \n",
              "...                 ...    ...          ...  \n",
              "1415  32000000000000000      M            2  \n",
              "1416                450      M            2  \n",
              "1417  10700000000000000      F            2  \n",
              "1418    350000000000000      M            1  \n",
              "1419  29000000000000000      F            2  \n",
              "\n",
              "[1420 rows x 14 columns]"
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              "      <th></th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>id</th>\n",
              "      <th>order_id</th>\n",
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              "      <td>2021-10-10</td>\n",
              "      <td>2021-10-12</td>\n",
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              "      <td>6963</td>\n",
              "      <td>4782</td>\n",
              "      <td>3892</td>\n",
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              "      <td>2022-02-27</td>\n",
              "      <td>2022-02-26</td>\n",
              "      <td>2022-02-26</td>\n",
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              "      <th>3</th>\n",
              "      <td>10856</td>\n",
              "      <td>95499</td>\n",
              "      <td>66151</td>\n",
              "      <td>53127</td>\n",
              "      <td>13821</td>\n",
              "      <td>257950</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2021-03-24</td>\n",
              "      <td>2021-03-25</td>\n",
              "      <td>2021-03-28</td>\n",
              "      <td>2021-03-30</td>\n",
              "      <td>831000000000000</td>\n",
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              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>7695</td>\n",
              "      <td>137445</td>\n",
              "      <td>95072</td>\n",
              "      <td>76289</td>\n",
              "      <td>27161</td>\n",
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              "      <td>2020-12-23</td>\n",
              "      <td>2020-12-26</td>\n",
              "      <td>2020-12-28</td>\n",
              "      <td>2020-12-28</td>\n",
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              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>1415</th>\n",
              "      <td>9887</td>\n",
              "      <td>95238</td>\n",
              "      <td>65964</td>\n",
              "      <td>52980</td>\n",
              "      <td>16175</td>\n",
              "      <td>257249</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2021-11-22</td>\n",
              "      <td>2021-11-22</td>\n",
              "      <td>2021-11-22</td>\n",
              "      <td>2021-11-22</td>\n",
              "      <td>32000000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1416</th>\n",
              "      <td>5909</td>\n",
              "      <td>96098</td>\n",
              "      <td>66561</td>\n",
              "      <td>53446</td>\n",
              "      <td>23188</td>\n",
              "      <td>259545</td>\n",
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              "      <td>2021-01-12</td>\n",
              "      <td>2021-01-11</td>\n",
              "      <td>2021-01-15</td>\n",
              "      <td>2021-01-17</td>\n",
              "      <td>450</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1417</th>\n",
              "      <td>10777</td>\n",
              "      <td>41410</td>\n",
              "      <td>28685</td>\n",
              "      <td>23155</td>\n",
              "      <td>11038</td>\n",
              "      <td>111763</td>\n",
              "      <td>Returned</td>\n",
              "      <td>2019-11-01</td>\n",
              "      <td>2019-11-04</td>\n",
              "      <td>2019-11-05</td>\n",
              "      <td>2019-11-07</td>\n",
              "      <td>10700000000000000</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>1418</th>\n",
              "      <td>11401</td>\n",
              "      <td>170597</td>\n",
              "      <td>117821</td>\n",
              "      <td>94709</td>\n",
              "      <td>16771</td>\n",
              "      <td>460706</td>\n",
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              "      <td>2019-10-26</td>\n",
              "      <td>2019-10-29</td>\n",
              "      <td>2019-10-30</td>\n",
              "      <td>2019-11-02</td>\n",
              "      <td>350000000000000</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
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              "      <th>1419</th>\n",
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              "      <td>2022-03-23</td>\n",
              "      <td>2022-03-23</td>\n",
              "      <td>2022-03-27</td>\n",
              "      <td>2022-03-27</td>\n",
              "      <td>29000000000000000</td>\n",
              "      <td>F</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1420 rows × 14 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bdd5721-ff8c-4e87-913f-a3de92cbf163')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3bdd5721-ff8c-4e87-913f-a3de92cbf163 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-3bdd5721-ff8c-4e87-913f-a3de92cbf163');\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": 129
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# <img src=\"https://img.icons8.com/color/48/undefined/5-circle--v1.png\"/>**summary**"
      ],
      "metadata": {
        "id": "kIZ8aryab0hM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Train Data `14202 rows × 14 columns`\n",
        "# Test Data `3550 rows × 14 columns`\n",
        "# Validation Data `1420 rows × 14 columns`"
      ],
      "metadata": {
        "id": "i0uE_CWhSU4c"
      }
    }
  ]
}