notebook.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H9wADwK78DCz"
      },
      "source": [
        "# Proyek Analisis Data: E-Commerce Public Dataset\n",
        "- **Nama:** Muhammad Fariz Eda Andhika\n",
        "- **Email:** farizedaa@gmail.com\n",
        "- **ID Dicoding:** farizeda"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eE0raob58DC0"
      },
      "source": [
        "## Menentukan Pertanyaan Bisnis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GmQeQ5YF8DC0"
      },
      "source": [
        "- What are the top-selling product categories?\n",
        "\n",
        "- Which sellers have the highest sales volume?\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H-z4QGlO8DC1"
      },
      "source": [
        "## Import Semua Packages/Library yang Digunakan"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "FVYwaObI8DC1"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h_Sh51Xy8DC1"
      },
      "source": [
        "## Data Wrangling"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sXU2GBYu8DC1"
      },
      "source": [
        "### Gathering Data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memuat tabel sellers_df, order_items_df, products_df, product_category_translation_df"
      ],
      "metadata": {
        "id": "yzBw0QjWAXBi"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "zjCBk1BI8DC1"
      },
      "outputs": [],
      "source": [
        "# For Business Question 1\n",
        "order_items_df = pd.read_csv('order_items_dataset.csv')\n",
        "products_df = pd.read_csv('products_dataset.csv')\n",
        "product_category_translation_df= pd.read_csv('product_category_name_translation.csv')\n",
        "\n",
        "# For Business Question 2\n",
        "sellers_df = pd.read_csv('sellers_dataset.csv')"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Head() untuk masing-masing df"
      ],
      "metadata": {
        "id": "Jyylu_VXT6QY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_items_df.head()"
      ],
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          "height": 226
        },
        "id": "o8h66KBNVlfc",
        "outputId": "88703b67-bb1f-4305-d507-ce5b4a6ff975"
      },
      "execution_count": 42,
      "outputs": [
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              "                           order_id  order_item_id  \\\n",
              "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
              "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
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              "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
              "\n",
              "                         product_id                         seller_id  \\\n",
              "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
              "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
              "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
              "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
              "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
              "\n",
              "   shipping_limit_date   price  freight_value  \n",
              "0  2017-09-19 09:45:35   58.90          13.29  \n",
              "1  2017-05-03 11:05:13  239.90          19.93  \n",
              "2  2018-01-18 14:48:30  199.00          17.87  \n",
              "3  2018-08-15 10:10:18   12.99          12.79  \n",
              "4  2017-02-13 13:57:51  199.90          18.14  "
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              "                         product_id  product_category_name  \\\n",
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              "      <td>1.0</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>20.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>96bd76ec8810374ed1b65e291975717f</td>\n",
              "      <td>esporte_lazer</td>\n",
              "      <td>46.0</td>\n",
              "      <td>250.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>18.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>cef67bcfe19066a932b7673e239eb23d</td>\n",
              "      <td>bebes</td>\n",
              "      <td>27.0</td>\n",
              "      <td>261.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>371.0</td>\n",
              "      <td>26.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>26.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9dc1a7de274444849c219cff195d0b71</td>\n",
              "      <td>utilidades_domesticas</td>\n",
              "      <td>37.0</td>\n",
              "      <td>402.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>625.0</td>\n",
              "      <td>20.0</td>\n",
              "      <td>17.0</td>\n",
              "      <td>13.0</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
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              "\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "\n",
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              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "</div>\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "products_df",
              "summary": "{\n  \"name\": \"products_df\",\n  \"rows\": 32951,\n  \"fields\": [\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 32951,\n        \"samples\": [\n          \"f819f0c84a64f02d3a5606ca95edd272\",\n          \"b9de40e43fccb6ba53b7eadbd5c49077\",\n          \"26afe4ed5890d941fcba14205863eec7\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_category_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 73,\n        \"samples\": [\n          \"utilidades_domesticas\",\n          \"pc_gamer\",\n          \"eletronicos\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name_lenght\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.245740725237239,\n        \"min\": 5.0,\n        \"max\": 76.0,\n        \"num_unique_values\": 66,\n        \"samples\": [\n          13.0,\n          72.0,\n          40.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_description_lenght\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 635.1152246349538,\n        \"min\": 4.0,\n        \"max\": 3992.0,\n        \"num_unique_values\": 2960,\n        \"samples\": [\n          1314.0,\n          40.0,\n          123.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_photos_qty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.7367656379315444,\n        \"min\": 1.0,\n        \"max\": 20.0,\n        \"num_unique_values\": 19,\n        \"samples\": [\n          1.0,\n          9.0,\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_weight_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4282.038730977007,\n        \"min\": 0.0,\n        \"max\": 40425.0,\n        \"num_unique_values\": 2204,\n        \"samples\": [\n          567.0,\n          241.0,\n          2646.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_length_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16.914458054065967,\n        \"min\": 7.0,\n        \"max\": 105.0,\n        \"num_unique_values\": 99,\n        \"samples\": [\n          88.0,\n          75.0,\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_height_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13.637554061749494,\n        \"min\": 2.0,\n        \"max\": 105.0,\n        \"num_unique_values\": 102,\n        \"samples\": [\n          6.0,\n          38.0,\n          54.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_width_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 12.079047453227835,\n        \"min\": 6.0,\n        \"max\": 118.0,\n        \"num_unique_values\": 95,\n        \"samples\": [\n          80.0,\n          38.0,\n          9.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_category_translation_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "nJ6ku4K6VsEQ",
        "outputId": "1af640d9-32f4-4b5f-a6b4-da23372ceff5"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    product_category_name product_category_name_english\n",
              "0            beleza_saude                 health_beauty\n",
              "1  informatica_acessorios         computers_accessories\n",
              "2              automotivo                          auto\n",
              "3         cama_mesa_banho                bed_bath_table\n",
              "4        moveis_decoracao               furniture_decor"
            ],
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              "      <td>beleza_saude</td>\n",
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              "      <th>1</th>\n",
              "      <td>informatica_acessorios</td>\n",
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              "      <td>auto</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
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              "      <td>bed_bath_table</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
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              "                                                    [key], {});\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-81e941fe-c8e1-4959-9580-10a77561af9b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "product_category_translation_df",
              "summary": "{\n  \"name\": \"product_category_translation_df\",\n  \"rows\": 71,\n  \"fields\": [\n    {\n      \"column\": \"product_category_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"cool_stuff\",\n          \"beleza_saude\",\n          \"construcao_ferramentas_seguranca\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_category_name_english\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"cool_stuff\",\n          \"health_beauty\",\n          \"construction_tools_safety\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sellers_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 243
        },
        "id": "27iB0WYxVvLP",
        "outputId": "3c61ef9f-bc83-48d6-ffe8-59efb3b22185"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                          seller_id  seller_zip_code_prefix  \\\n",
              "0  3442f8959a84dea7ee197c632cb2df15                   13023   \n",
              "1  d1b65fc7debc3361ea86b5f14c68d2e2                   13844   \n",
              "2  ce3ad9de960102d0677a81f5d0bb7b2d                   20031   \n",
              "3  c0f3eea2e14555b6faeea3dd58c1b1c3                    4195   \n",
              "4  51a04a8a6bdcb23deccc82b0b80742cf                   12914   \n",
              "\n",
              "         seller_city seller_state  \n",
              "0           campinas           SP  \n",
              "1         mogi guacu           SP  \n",
              "2     rio de janeiro           RJ  \n",
              "3          sao paulo           SP  \n",
              "4  braganca paulista           SP  "
            ],
            "text/html": [
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              "      <th>seller_id</th>\n",
              "      <th>seller_zip_code_prefix</th>\n",
              "      <th>seller_city</th>\n",
              "      <th>seller_state</th>\n",
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              "      <td>13844</td>\n",
              "      <td>mogi guacu</td>\n",
              "      <td>SP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ce3ad9de960102d0677a81f5d0bb7b2d</td>\n",
              "      <td>20031</td>\n",
              "      <td>rio de janeiro</td>\n",
              "      <td>RJ</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>c0f3eea2e14555b6faeea3dd58c1b1c3</td>\n",
              "      <td>4195</td>\n",
              "      <td>sao paulo</td>\n",
              "      <td>SP</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>51a04a8a6bdcb23deccc82b0b80742cf</td>\n",
              "      <td>12914</td>\n",
              "      <td>braganca paulista</td>\n",
              "      <td>SP</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ff7796e1-cf6f-4b80-bb81-60a8e88ca871')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
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              "\n",
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              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-ff7796e1-cf6f-4b80-bb81-60a8e88ca871 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-ff7796e1-cf6f-4b80-bb81-60a8e88ca871');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6eacfd9d-ca0b-4496-ac3a-f6e4afb125aa')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-6eacfd9d-ca0b-4496-ac3a-f6e4afb125aa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "sellers_df",
              "summary": "{\n  \"name\": \"sellers_df\",\n  \"rows\": 3095,\n  \"fields\": [\n    {\n      \"column\": \"seller_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3095,\n        \"samples\": [\n          \"1f7fd2a6fcd5a6fa5d8a4dabc72aaae0\",\n          \"6e1862e15f33d9994bc25922a85e1efc\",\n          \"e628d4a53c109f09ca88098338b3a3f5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seller_zip_code_prefix\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 32713,\n        \"min\": 1001,\n        \"max\": 99730,\n        \"num_unique_values\": 2246,\n        \"samples\": [\n          87160,\n          6787,\n          36325\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seller_city\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 611,\n        \"samples\": [\n          \"salvador\",\n          \"guaira\",\n          \"echapora\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seller_state\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 23,\n        \"samples\": [\n          \"ES\",\n          \"MG\",\n          \"SP\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FHSiqaZp8DC1"
      },
      "source": [
        "### Assessing Data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Menilai order_items_df\n"
      ],
      "metadata": {
        "id": "PQqWWg14WTvI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan assessment terhadap masing-masing df menggunakan method info()"
      ],
      "metadata": {
        "id": "glLH1IcZV558"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_items_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gaew6tCC_uTw",
        "outputId": "aed3bc42-dbcd-4b75-d947-a22220d6baad"
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 112650 entries, 0 to 112649\n",
            "Data columns (total 7 columns):\n",
            " #   Column               Non-Null Count   Dtype  \n",
            "---  ------               --------------   -----  \n",
            " 0   order_id             112650 non-null  object \n",
            " 1   order_item_id        112650 non-null  int64  \n",
            " 2   product_id           112650 non-null  object \n",
            " 3   seller_id            112650 non-null  object \n",
            " 4   shipping_limit_date  112650 non-null  object \n",
            " 5   price                112650 non-null  float64\n",
            " 6   freight_value        112650 non-null  float64\n",
            "dtypes: float64(2), int64(1), object(4)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Konsisten pada jumlah non-null count, namun terdapat kesalahan tipe object pada 'shipping_limit_data' yang seharusnya berupa date_time, bukan object."
      ],
      "metadata": {
        "id": "H_orL0JiXTsd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pengechekkan missing values"
      ],
      "metadata": {
        "id": "9r2K2BFjWfQp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_items_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "45Pskw6KWcCH",
        "outputId": "f799a789-2e02-43db-eeb2-fd7196636806"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id               0\n",
              "order_item_id          0\n",
              "product_id             0\n",
              "seller_id              0\n",
              "shipping_limit_date    0\n",
              "price                  0\n",
              "freight_value          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tampak tidak ada missing values, lanjut ke pengecekkan nilai duplikat:"
      ],
      "metadata": {
        "id": "fN-q9KdIV1s8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Jumlah duplikasi: \", order_items_df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B8bEbFtKWlB6",
        "outputId": "f56c6885-f23e-4e54-aeb7-bbf3c814c1ff"
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada duplikasi"
      ],
      "metadata": {
        "id": "vU3ZR8_0Zl__"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_items_df.describe(include=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "OBvZ8aQdZwIM",
        "outputId": "cb81703c-f542-4586-acb5-c75bf84b8046"
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                order_id  order_item_id  \\\n",
              "count                             112650  112650.000000   \n",
              "unique                             98666            NaN   \n",
              "top     8272b63d03f5f79c56e9e4120aec44ef            NaN   \n",
              "freq                                  21            NaN   \n",
              "mean                                 NaN       1.197834   \n",
              "std                                  NaN       0.705124   \n",
              "min                                  NaN       1.000000   \n",
              "25%                                  NaN       1.000000   \n",
              "50%                                  NaN       1.000000   \n",
              "75%                                  NaN       1.000000   \n",
              "max                                  NaN      21.000000   \n",
              "\n",
              "                              product_id                         seller_id  \\\n",
              "count                             112650                            112650   \n",
              "unique                             32951                              3095   \n",
              "top     aca2eb7d00ea1a7b8ebd4e68314663af  6560211a19b47992c3666cc44a7e94c0   \n",
              "freq                                 527                              2033   \n",
              "mean                                 NaN                               NaN   \n",
              "std                                  NaN                               NaN   \n",
              "min                                  NaN                               NaN   \n",
              "25%                                  NaN                               NaN   \n",
              "50%                                  NaN                               NaN   \n",
              "75%                                  NaN                               NaN   \n",
              "max                                  NaN                               NaN   \n",
              "\n",
              "        shipping_limit_date          price  freight_value  \n",
              "count                112650  112650.000000  112650.000000  \n",
              "unique                93318            NaN            NaN  \n",
              "top     2017-07-21 18:25:23            NaN            NaN  \n",
              "freq                     21            NaN            NaN  \n",
              "mean                    NaN     120.653739      19.990320  \n",
              "std                     NaN     183.633928      15.806405  \n",
              "min                     NaN       0.850000       0.000000  \n",
              "25%                     NaN      39.900000      13.080000  \n",
              "50%                     NaN      74.990000      16.260000  \n",
              "75%                     NaN     134.900000      21.150000  \n",
              "max                     NaN    6735.000000     409.680000  "
            ],
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              "  <div id=\"df-73957e5a-f755-49ec-af8e-177f623e2482\" 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>order_id</th>\n",
              "      <th>order_item_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>seller_id</th>\n",
              "      <th>shipping_limit_date</th>\n",
              "      <th>price</th>\n",
              "      <th>freight_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>112650</td>\n",
              "      <td>112650.000000</td>\n",
              "      <td>112650</td>\n",
              "      <td>112650</td>\n",
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              "      <td>112650.000000</td>\n",
              "      <td>112650.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>98666</td>\n",
              "      <td>NaN</td>\n",
              "      <td>32951</td>\n",
              "      <td>3095</td>\n",
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              "      <td>6560211a19b47992c3666cc44a7e94c0</td>\n",
              "      <td>2017-07-21 18:25:23</td>\n",
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              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>527</td>\n",
              "      <td>2033</td>\n",
              "      <td>21</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.197834</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>120.653739</td>\n",
              "      <td>19.990320</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>0.705124</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>183.633928</td>\n",
              "      <td>15.806405</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
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              "      <td>0.850000</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>39.900000</td>\n",
              "      <td>13.080000</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>NaN</td>\n",
              "      <td>74.990000</td>\n",
              "      <td>16.260000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>134.900000</td>\n",
              "      <td>21.150000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>6735.000000</td>\n",
              "      <td>409.680000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-73957e5a-f755-49ec-af8e-177f623e2482')\"\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 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-73957e5a-f755-49ec-af8e-177f623e2482 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-73957e5a-f755-49ec-af8e-177f623e2482');\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",
              "\n",
              "\n",
              "<div id=\"df-ce9dca9a-aac2-40f3-a535-574b5e135bdd\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-ce9dca9a-aac2-40f3-a535-574b5e135bdd')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-ce9dca9a-aac2-40f3-a535-574b5e135bdd button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"order_items_df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"order_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          98666,\n          \"21\",\n          \"112650\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"order_item_id\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39826.43126188818,\n        \"min\": 0.7051240313951733,\n        \"max\": 112650.0,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          1.1978339991122948,\n          21.0,\n          0.7051240313951733\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          32951,\n          \"527\",\n          \"112650\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"seller_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          3095,\n          \"2033\",\n          \"112650\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"shipping_limit_date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"1970-01-01 00:00:00.000000021\",\n        \"max\": \"2017-07-21 18:25:23\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          93318,\n          \"21\",\n          \"112650\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39528.033699224645,\n        \"min\": 0.85,\n        \"max\": 112650.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          120.65373901464716,\n          74.99,\n          112650.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"freight_value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 39802.98023062691,\n        \"min\": 0.0,\n        \"max\": 112650.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          19.990319928983578,\n          16.26,\n          112650.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada kejanggalan values"
      ],
      "metadata": {
        "id": "cTdh1jl6aDAT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Menilai products_df"
      ],
      "metadata": {
        "id": "4bObOaqXX9OX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fgg6L1nDYDdT",
        "outputId": "cd84ce2e-836f-4bda-a3a8-4a9124cdfd25"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 32951 entries, 0 to 32950\n",
            "Data columns (total 9 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   product_id                  32951 non-null  object \n",
            " 1   product_category_name       32341 non-null  object \n",
            " 2   product_name_lenght         32341 non-null  float64\n",
            " 3   product_description_lenght  32341 non-null  float64\n",
            " 4   product_photos_qty          32341 non-null  float64\n",
            " 5   product_weight_g            32949 non-null  float64\n",
            " 6   product_length_cm           32949 non-null  float64\n",
            " 7   product_height_cm           32949 non-null  float64\n",
            " 8   product_width_cm            32949 non-null  float64\n",
            "dtypes: float64(7), object(2)\n",
            "memory usage: 2.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Terlihat terdapat perbedaan jumlah non-null untuk beberapa kolom."
      ],
      "metadata": {
        "id": "JZUNM-PpYIyd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xHh66CqVYSfk",
        "outputId": "d22ff463-d738-4b89-dd33-9f2084c543e6"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_id                      0\n",
              "product_category_name         610\n",
              "product_name_lenght           610\n",
              "product_description_lenght    610\n",
              "product_photos_qty            610\n",
              "product_weight_g                2\n",
              "product_length_cm               2\n",
              "product_height_cm               2\n",
              "product_width_cm                2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ternyata terdapat beberapa kolom yang memiliki banyak missing values."
      ],
      "metadata": {
        "id": "dnOH4z69YmWs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Jumlah duplikasi: \", products_df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_3a1g-lxY5Bh",
        "outputId": "3053d4e5-aeed-4160-c9a4-1edab4a3f25b"
      },
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada duplikasi pada df ini"
      ],
      "metadata": {
        "id": "5tYTXIXBY8Iv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products_df.describe(include=\"all\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 414
        },
        "id": "2VEbhpKjaINY",
        "outputId": "5e9339b4-f73f-44b4-be7b-7a81c0f63533"
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              product_id product_category_name  \\\n",
              "count                              32951                 32341   \n",
              "unique                             32951                    73   \n",
              "top     1e9e8ef04dbcff4541ed26657ea517e5       cama_mesa_banho   \n",
              "freq                                   1                  3029   \n",
              "mean                                 NaN                   NaN   \n",
              "std                                  NaN                   NaN   \n",
              "min                                  NaN                   NaN   \n",
              "25%                                  NaN                   NaN   \n",
              "50%                                  NaN                   NaN   \n",
              "75%                                  NaN                   NaN   \n",
              "max                                  NaN                   NaN   \n",
              "\n",
              "        product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
              "count          32341.000000                32341.000000        32341.000000   \n",
              "unique                  NaN                         NaN                 NaN   \n",
              "top                     NaN                         NaN                 NaN   \n",
              "freq                    NaN                         NaN                 NaN   \n",
              "mean              48.476949                  771.495285            2.188986   \n",
              "std               10.245741                  635.115225            1.736766   \n",
              "min                5.000000                    4.000000            1.000000   \n",
              "25%               42.000000                  339.000000            1.000000   \n",
              "50%               51.000000                  595.000000            1.000000   \n",
              "75%               57.000000                  972.000000            3.000000   \n",
              "max               76.000000                 3992.000000           20.000000   \n",
              "\n",
              "        product_weight_g  product_length_cm  product_height_cm  \\\n",
              "count       32949.000000       32949.000000       32949.000000   \n",
              "unique               NaN                NaN                NaN   \n",
              "top                  NaN                NaN                NaN   \n",
              "freq                 NaN                NaN                NaN   \n",
              "mean         2276.472488          30.815078          16.937661   \n",
              "std          4282.038731          16.914458          13.637554   \n",
              "min             0.000000           7.000000           2.000000   \n",
              "25%           300.000000          18.000000           8.000000   \n",
              "50%           700.000000          25.000000          13.000000   \n",
              "75%          1900.000000          38.000000          21.000000   \n",
              "max         40425.000000         105.000000         105.000000   \n",
              "\n",
              "        product_width_cm  \n",
              "count       32949.000000  \n",
              "unique               NaN  \n",
              "top                  NaN  \n",
              "freq                 NaN  \n",
              "mean           23.196728  \n",
              "std            12.079047  \n",
              "min             6.000000  \n",
              "25%            15.000000  \n",
              "50%            20.000000  \n",
              "75%            30.000000  \n",
              "max           118.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-650633fd-ab79-4884-9401-32f3cae6617e\" 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>product_id</th>\n",
              "      <th>product_category_name</th>\n",
              "      <th>product_name_lenght</th>\n",
              "      <th>product_description_lenght</th>\n",
              "      <th>product_photos_qty</th>\n",
              "      <th>product_weight_g</th>\n",
              "      <th>product_length_cm</th>\n",
              "      <th>product_height_cm</th>\n",
              "      <th>product_width_cm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>32951</td>\n",
              "      <td>32341</td>\n",
              "      <td>32341.000000</td>\n",
              "      <td>32341.000000</td>\n",
              "      <td>32341.000000</td>\n",
              "      <td>32949.000000</td>\n",
              "      <td>32949.000000</td>\n",
              "      <td>32949.000000</td>\n",
              "      <td>32949.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>32951</td>\n",
              "      <td>73</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>1e9e8ef04dbcff4541ed26657ea517e5</td>\n",
              "      <td>cama_mesa_banho</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>1</td>\n",
              "      <td>3029</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>48.476949</td>\n",
              "      <td>771.495285</td>\n",
              "      <td>2.188986</td>\n",
              "      <td>2276.472488</td>\n",
              "      <td>30.815078</td>\n",
              "      <td>16.937661</td>\n",
              "      <td>23.196728</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.245741</td>\n",
              "      <td>635.115225</td>\n",
              "      <td>1.736766</td>\n",
              "      <td>4282.038731</td>\n",
              "      <td>16.914458</td>\n",
              "      <td>13.637554</td>\n",
              "      <td>12.079047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>2.000000</td>\n",
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              "      <td>339.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>300.000000</td>\n",
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              "      <td>8.000000</td>\n",
              "      <td>15.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>1.000000</td>\n",
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              "      <td>25.000000</td>\n",
              "      <td>13.000000</td>\n",
              "      <td>20.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>57.000000</td>\n",
              "      <td>972.000000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>1900.000000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>21.000000</td>\n",
              "      <td>30.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>76.000000</td>\n",
              "      <td>3992.000000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>40425.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>105.000000</td>\n",
              "      <td>118.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-650633fd-ab79-4884-9401-32f3cae6617e')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-650633fd-ab79-4884-9401-32f3cae6617e 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-650633fd-ab79-4884-9401-32f3cae6617e');\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",
              "\n",
              "\n",
              "<div id=\"df-fb35dd58-251c-4eda-bfd2-3cd209bf508f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-fb35dd58-251c-4eda-bfd2-3cd209bf508f')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-fb35dd58-251c-4eda-bfd2-3cd209bf508f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"products_df\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"product_id\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"32951\",\n          \"1e9e8ef04dbcff4541ed26657ea517e5\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_category_name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          73,\n          \"3029\",\n          \"32341\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_name_lenght\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11419.661260206109,\n        \"min\": 5.0,\n        \"max\": 32341.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          48.47694876472589,\n          51.0,\n          32341.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_description_lenght\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11134.099589169266,\n        \"min\": 4.0,\n        \"max\": 32341.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          771.4952846232337,\n          595.0,\n          32341.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_photos_qty\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11432.760549996,\n        \"min\": 1.0,\n        \"max\": 32341.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          32341.0,\n          2.1889861166939797,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_weight_g\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 16431.121578666553,\n        \"min\": 0.0,\n        \"max\": 40425.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2276.4724877841513,\n          700.0,\n          32949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_length_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11637.111419534527,\n        \"min\": 7.0,\n        \"max\": 32949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          30.81507784758263,\n          25.0,\n          32949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_height_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11640.20719558358,\n        \"min\": 2.0,\n        \"max\": 32949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          16.937661234028347,\n          13.0,\n          32949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_width_cm\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11637.958165171713,\n        \"min\": 6.0,\n        \"max\": 32949.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          23.196728277034204,\n          20.0,\n          32949.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada kejanggalan values"
      ],
      "metadata": {
        "id": "ElMDf9Q5aLIX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Menilai product_category_translation_df"
      ],
      "metadata": {
        "id": "WBBiA3TUZGRt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "product_category_translation_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jeuO38UlZT41",
        "outputId": "414021de-b4d3-4ecd-d47d-f7f9b5a7c528"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 71 entries, 0 to 70\n",
            "Data columns (total 2 columns):\n",
            " #   Column                         Non-Null Count  Dtype \n",
            "---  ------                         --------------  ----- \n",
            " 0   product_category_name          71 non-null     object\n",
            " 1   product_category_name_english  71 non-null     object\n",
            "dtypes: object(2)\n",
            "memory usage: 1.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Konsisten pada jumlah non-null dan tidak ada keanehan pada Dtype"
      ],
      "metadata": {
        "id": "m2T_uERAZYAp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "product_category_translation_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Bhq70h-CZWmz",
        "outputId": "615a3df2-2bcf-4178-f879-1ebd19d79481"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_category_name            0\n",
              "product_category_name_english    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada missing values"
      ],
      "metadata": {
        "id": "1-j1m7Wka2dn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Jumlah duplikasi: \", product_category_translation_df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8Cc-UeT5a4wv",
        "outputId": "5d759ebc-1dcb-4354-caf2-0b1d3494a81f"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada duplikat"
      ],
      "metadata": {
        "id": "wl8cAPZ3bAI6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "product_category_translation_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "oLAiJvf0bChg",
        "outputId": "a409ea93-d1aa-4951-8804-72a58aaa91ff"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       product_category_name product_category_name_english\n",
              "count                     71                            71\n",
              "unique                    71                            71\n",
              "top             beleza_saude                 health_beauty\n",
              "freq                       1                             1"
            ],
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              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "  <thead>\n",
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              "      <th>product_category_name_english</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>71</td>\n",
              "      <td>71</td>\n",
              "    </tr>\n",
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              "      <th>unique</th>\n",
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              "      <th>top</th>\n",
              "      <td>beleza_saude</td>\n",
              "      <td>health_beauty</td>\n",
              "    </tr>\n",
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              "      <th>freq</th>\n",
              "      <td>1</td>\n",
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              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "    .colab-df-buttons div {\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-22ac4ab3-dfb5-4c66-aa55-2bbc93e07724 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-22ac4ab3-dfb5-4c66-aa55-2bbc93e07724');\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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              "\n",
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              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
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              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-18b6ff95-987a-48c5-a3ad-210ebc94e88c button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"product_category_translation_df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"product_category_name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"71\",\n          \"beleza_saude\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"product_category_name_english\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"71\",\n          \"health_beauty\",\n          \"1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Karena semua atribute kategorikal dapat dilanjutkan"
      ],
      "metadata": {
        "id": "P2v9jlH1bKkF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Menilai sellers_df"
      ],
      "metadata": {
        "id": "U8lbDQpObar-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sellers_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-WrBbWhbdO_",
        "outputId": "e636e7f3-7fa5-4eec-b5fc-5d373aa3d567"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3095 entries, 0 to 3094\n",
            "Data columns (total 4 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   seller_id               3095 non-null   object\n",
            " 1   seller_zip_code_prefix  3095 non-null   int64 \n",
            " 2   seller_city             3095 non-null   object\n",
            " 3   seller_state            3095 non-null   object\n",
            "dtypes: int64(1), object(3)\n",
            "memory usage: 96.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Konsisten pada jumlah nilai null, dan Dtype tampak sesuai"
      ],
      "metadata": {
        "id": "lkzg6ZNhbiHe"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sellers_df.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P6RR3M_RbmGK",
        "outputId": "31a53b75-d797-4ad4-b901-9fed6d45301a"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "seller_id                 0\n",
              "seller_zip_code_prefix    0\n",
              "seller_city               0\n",
              "seller_state              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada missing values"
      ],
      "metadata": {
        "id": "Q0t4xVdLbpQg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Jumlah duplikasi: \", sellers_df.duplicated().sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wh6SnsMUbuad",
        "outputId": "b3e408e5-7b4c-4341-c646-cb308271bf0e"
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jumlah duplikasi:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada duplikat"
      ],
      "metadata": {
        "id": "HoMcADkgb05k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sellers_df.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "EvgpFwppb-vA",
        "outputId": "fb1d3650-dd91-4c06-b3ab-3e9f95149312"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       seller_zip_code_prefix\n",
              "count             3095.000000\n",
              "mean             32291.059451\n",
              "std              32713.453830\n",
              "min               1001.000000\n",
              "25%               7093.500000\n",
              "50%              14940.000000\n",
              "75%              64552.500000\n",
              "max              99730.000000"
            ],
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              "      <td>32291.059451</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"sellers_df\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"seller_zip_code_prefix\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 34548.896404488776,\n        \"min\": 1001.0,\n        \"max\": 99730.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          32291.059450726978,\n          14940.0,\n          3095.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengetahui domain dari data tersebut, dapat diasumsikan tidak ada kejanggalan dari zip code tersebut"
      ],
      "metadata": {
        "id": "_AL8sg8_cF7s"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EhN5R4hr8DC1"
      },
      "source": [
        "### Cleaning Data"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Membersihkan order_items_df\n"
      ],
      "metadata": {
        "id": "zrja1yk0bVbb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengubah object menjadi dateTime"
      ],
      "metadata": {
        "id": "H0LYFAzCcu0q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "column = \"shipping_limit_date\"\n",
        "order_items_df[column] = pd.to_datetime(order_items_df[column])"
      ],
      "metadata": {
        "id": "jVnYpprE9Evz"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pengechekkan"
      ],
      "metadata": {
        "id": "pi_iFWr3dBOA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_items_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tYH6HWL4dDgo",
        "outputId": "88bf4ed0-5248-43df-c37f-0943f93acf15"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 112650 entries, 0 to 112649\n",
            "Data columns (total 7 columns):\n",
            " #   Column               Non-Null Count   Dtype         \n",
            "---  ------               --------------   -----         \n",
            " 0   order_id             112650 non-null  object        \n",
            " 1   order_item_id        112650 non-null  int64         \n",
            " 2   product_id           112650 non-null  object        \n",
            " 3   seller_id            112650 non-null  object        \n",
            " 4   shipping_limit_date  112650 non-null  datetime64[ns]\n",
            " 5   price                112650 non-null  float64       \n",
            " 6   freight_value        112650 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), object(3)\n",
            "memory usage: 6.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dapat terlihat bahwa tipe data sudah berupa menjadi datetime64[ns]"
      ],
      "metadata": {
        "id": "Nz2dRe0adMuP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_items_df.to_csv('cleaned_order_items.csv', index=False)"
      ],
      "metadata": {
        "id": "J0IFC6g-vxWy"
      },
      "execution_count": 64,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Membersihkan products_df"
      ],
      "metadata": {
        "id": "9iu8i2X7dQr4"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Drop missing rows"
      ],
      "metadata": {
        "id": "r9Xuzn_9gOBx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping rows with missing 'product_category_name', 'product_name_lenght', 'product_description_lenght', 'product_photos_qty'\n",
        "products_df = products_df.dropna(subset=['product_category_name', 'product_name_lenght', 'product_description_lenght', 'product_photos_qty'])\n"
      ],
      "metadata": {
        "id": "xkMalilAdYwj"
      },
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Impute by using median"
      ],
      "metadata": {
        "id": "fJNm7XycgPLt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "for column in ['product_weight_g', 'product_length_cm', 'product_height_cm', 'product_width_cm']:\n",
        "    products_df[column].fillna(products_df[column].median(), inplace=True)"
      ],
      "metadata": {
        "id": "RpFOFyq9gLt1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9770189f-4d81-4964-f4e8-b6d26d3dab94"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-66-bf62c5073050>:2: 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",
            "  products_df[column].fillna(products_df[column].median(), inplace=True)\n",
            "<ipython-input-66-bf62c5073050>:2: 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",
            "  products_df[column].fillna(products_df[column].median(), inplace=True)\n",
            "<ipython-input-66-bf62c5073050>:2: 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",
            "  products_df[column].fillna(products_df[column].median(), inplace=True)\n",
            "<ipython-input-66-bf62c5073050>:2: 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",
            "  products_df[column].fillna(products_df[column].median(), inplace=True)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pengechekkan"
      ],
      "metadata": {
        "id": "QQxA7k-IgS5N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LeftW5vqgUXf",
        "outputId": "0c7ed165-4376-46d6-b130-66f1c5c08392"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 32341 entries, 0 to 32950\n",
            "Data columns (total 9 columns):\n",
            " #   Column                      Non-Null Count  Dtype  \n",
            "---  ------                      --------------  -----  \n",
            " 0   product_id                  32341 non-null  object \n",
            " 1   product_category_name       32341 non-null  object \n",
            " 2   product_name_lenght         32341 non-null  float64\n",
            " 3   product_description_lenght  32341 non-null  float64\n",
            " 4   product_photos_qty          32341 non-null  float64\n",
            " 5   product_weight_g            32341 non-null  float64\n",
            " 6   product_length_cm           32341 non-null  float64\n",
            " 7   product_height_cm           32341 non-null  float64\n",
            " 8   product_width_cm            32341 non-null  float64\n",
            "dtypes: float64(7), object(2)\n",
            "memory usage: 2.5+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset sudah terlihat bersih :D"
      ],
      "metadata": {
        "id": "i_eWrNX6gW2G"
      }
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gp-Y6wU38DC1"
      },
      "source": [
        "## Exploratory Data Analysis (EDA)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MW7WF2kr8DC1"
      },
      "source": [
        "Pertama, kita akan menggabungkan order_items_dataset dengan product_dataset untuk mendapatkan informasi kategori produk untuk setiap item pesanan."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "id": "e9CQCZjk8DC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "outputId": "5a3aa45e-1308-4b0b-f1bd-9e3b4fdad6de"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           order_id  order_item_id  \\\n",
              "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
              "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
              "2  000229ec398224ef6ca0657da4fc703e              1   \n",
              "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
              "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
              "\n",
              "                         product_id                         seller_id  \\\n",
              "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
              "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
              "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
              "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
              "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
              "\n",
              "  shipping_limit_date   price  freight_value product_category_name  \\\n",
              "0 2017-09-19 09:45:35   58.90          13.29            cool_stuff   \n",
              "1 2017-05-03 11:05:13  239.90          19.93              pet_shop   \n",
              "2 2018-01-18 14:48:30  199.00          17.87      moveis_decoracao   \n",
              "3 2018-08-15 10:10:18   12.99          12.79            perfumaria   \n",
              "4 2017-02-13 13:57:51  199.90          18.14    ferramentas_jardim   \n",
              "\n",
              "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
              "0                 58.0                       598.0                 4.0   \n",
              "1                 56.0                       239.0                 2.0   \n",
              "2                 59.0                       695.0                 2.0   \n",
              "3                 42.0                       480.0                 1.0   \n",
              "4                 59.0                       409.0                 1.0   \n",
              "\n",
              "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \n",
              "0             650.0               28.0                9.0              14.0  \n",
              "1           30000.0               50.0               30.0              40.0  \n",
              "2            3050.0               33.0               13.0              33.0  \n",
              "3             200.0               16.0               10.0              15.0  \n",
              "4            3750.0               35.0               40.0              30.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-b3f6700f-1821-4235-a357-339b1b92db36\" class=\"colab-df-container\">\n",
              "    <div>\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>order_id</th>\n",
              "      <th>order_item_id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>seller_id</th>\n",
              "      <th>shipping_limit_date</th>\n",
              "      <th>price</th>\n",
              "      <th>freight_value</th>\n",
              "      <th>product_category_name</th>\n",
              "      <th>product_name_lenght</th>\n",
              "      <th>product_description_lenght</th>\n",
              "      <th>product_photos_qty</th>\n",
              "      <th>product_weight_g</th>\n",
              "      <th>product_length_cm</th>\n",
              "      <th>product_height_cm</th>\n",
              "      <th>product_width_cm</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
              "      <td>1</td>\n",
              "      <td>4244733e06e7ecb4970a6e2683c13e61</td>\n",
              "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
              "      <td>2017-09-19 09:45:35</td>\n",
              "      <td>58.90</td>\n",
              "      <td>13.29</td>\n",
              "      <td>cool_stuff</td>\n",
              "      <td>58.0</td>\n",
              "      <td>598.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>650.0</td>\n",
              "      <td>28.0</td>\n",
              "      <td>9.0</td>\n",
              "      <td>14.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>00018f77f2f0320c557190d7a144bdd3</td>\n",
              "      <td>1</td>\n",
              "      <td>e5f2d52b802189ee658865ca93d83a8f</td>\n",
              "      <td>dd7ddc04e1b6c2c614352b383efe2d36</td>\n",
              "      <td>2017-05-03 11:05:13</td>\n",
              "      <td>239.90</td>\n",
              "      <td>19.93</td>\n",
              "      <td>pet_shop</td>\n",
              "      <td>56.0</td>\n",
              "      <td>239.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>30000.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>30.0</td>\n",
              "      <td>40.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>000229ec398224ef6ca0657da4fc703e</td>\n",
              "      <td>1</td>\n",
              "      <td>c777355d18b72b67abbeef9df44fd0fd</td>\n",
              "      <td>5b51032eddd242adc84c38acab88f23d</td>\n",
              "      <td>2018-01-18 14:48:30</td>\n",
              "      <td>199.00</td>\n",
              "      <td>17.87</td>\n",
              "      <td>moveis_decoracao</td>\n",
              "      <td>59.0</td>\n",
              "      <td>695.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>3050.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>13.0</td>\n",
              "      <td>33.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>00024acbcdf0a6daa1e931b038114c75</td>\n",
              "      <td>1</td>\n",
              "      <td>7634da152a4610f1595efa32f14722fc</td>\n",
              "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
              "      <td>2018-08-15 10:10:18</td>\n",
              "      <td>12.99</td>\n",
              "      <td>12.79</td>\n",
              "      <td>perfumaria</td>\n",
              "      <td>42.0</td>\n",
              "      <td>480.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>200.0</td>\n",
              "      <td>16.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>00042b26cf59d7ce69dfabb4e55b4fd9</td>\n",
              "      <td>1</td>\n",
              "      <td>ac6c3623068f30de03045865e4e10089</td>\n",
              "      <td>df560393f3a51e74553ab94004ba5c87</td>\n",
              "      <td>2017-02-13 13:57:51</td>\n",
              "      <td>199.90</td>\n",
              "      <td>18.14</td>\n",
              "      <td>ferramentas_jardim</td>\n",
              "      <td>59.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3750.0</td>\n",
              "      <td>35.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3f6700f-1821-4235-a357-339b1b92db36')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      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",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
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              "      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-b3f6700f-1821-4235-a357-339b1b92db36 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-b3f6700f-1821-4235-a357-339b1b92db36');\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",
              "\n",
              "\n",
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              "            title=\"Suggest charts\"\n",
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              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-f0b3003f-5894-4049-ac6f-6da2381d366b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "order_items_products_df"
            }
          },
          "metadata": {},
          "execution_count": 68
        }
      ],
      "source": [
        "order_items_products_df = pd.merge(order_items_df, products_df, on='product_id', how='left')\n",
        "\n",
        "order_items_products_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "karena masih dalam berbentuk kode (tidak terlalu insightful), maka sebaiknya ditambahkan translasi terlebih dahulu"
      ],
      "metadata": {
        "id": "u_IlpANCheiG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "order_items_products_translated_df = pd.merge(\n",
        "    order_items_products_df,\n",
        "    product_category_translation_df,\n",
        "    left_on='product_category_name',\n",
        "    right_on='product_category_name',\n",
        "    how='left'\n",
        ")\n",
        "\n",
        "order_items_products_translated_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "6gz3OAryhWnF",
        "outputId": "d1dfeed3-d7ae-42e3-d83b-acedc65a47a8"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           order_id  order_item_id  \\\n",
              "0  00010242fe8c5a6d1ba2dd792cb16214              1   \n",
              "1  00018f77f2f0320c557190d7a144bdd3              1   \n",
              "2  000229ec398224ef6ca0657da4fc703e              1   \n",
              "3  00024acbcdf0a6daa1e931b038114c75              1   \n",
              "4  00042b26cf59d7ce69dfabb4e55b4fd9              1   \n",
              "\n",
              "                         product_id                         seller_id  \\\n",
              "0  4244733e06e7ecb4970a6e2683c13e61  48436dade18ac8b2bce089ec2a041202   \n",
              "1  e5f2d52b802189ee658865ca93d83a8f  dd7ddc04e1b6c2c614352b383efe2d36   \n",
              "2  c777355d18b72b67abbeef9df44fd0fd  5b51032eddd242adc84c38acab88f23d   \n",
              "3  7634da152a4610f1595efa32f14722fc  9d7a1d34a5052409006425275ba1c2b4   \n",
              "4  ac6c3623068f30de03045865e4e10089  df560393f3a51e74553ab94004ba5c87   \n",
              "\n",
              "  shipping_limit_date   price  freight_value product_category_name  \\\n",
              "0 2017-09-19 09:45:35   58.90          13.29            cool_stuff   \n",
              "1 2017-05-03 11:05:13  239.90          19.93              pet_shop   \n",
              "2 2018-01-18 14:48:30  199.00          17.87      moveis_decoracao   \n",
              "3 2018-08-15 10:10:18   12.99          12.79            perfumaria   \n",
              "4 2017-02-13 13:57:51  199.90          18.14    ferramentas_jardim   \n",
              "\n",
              "   product_name_lenght  product_description_lenght  product_photos_qty  \\\n",
              "0                 58.0                       598.0                 4.0   \n",
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              "2                 59.0                       695.0                 2.0   \n",
              "3                 42.0                       480.0                 1.0   \n",
              "4                 59.0                       409.0                 1.0   \n",
              "\n",
              "   product_weight_g  product_length_cm  product_height_cm  product_width_cm  \\\n",
              "0             650.0               28.0                9.0              14.0   \n",
              "1           30000.0               50.0               30.0              40.0   \n",
              "2            3050.0               33.0               13.0              33.0   \n",
              "3             200.0               16.0               10.0              15.0   \n",
              "4            3750.0               35.0               40.0              30.0   \n",
              "\n",
              "  product_category_name_english  \n",
              "0                    cool_stuff  \n",
              "1                      pet_shop  \n",
              "2               furniture_decor  \n",
              "3                     perfumery  \n",
              "4                  garden_tools  "
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              "      <th>0</th>\n",
              "      <td>00010242fe8c5a6d1ba2dd792cb16214</td>\n",
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              "      <td>48436dade18ac8b2bce089ec2a041202</td>\n",
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              "      <td>2017-05-03 11:05:13</td>\n",
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              "      <td>695.0</td>\n",
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              "      <td>9d7a1d34a5052409006425275ba1c2b4</td>\n",
              "      <td>2018-08-15 10:10:18</td>\n",
              "      <td>12.99</td>\n",
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              "type": "dataframe",
              "variable_name": "order_items_products_translated_df"
            }
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Setelah ini, akan melakukan agregat sales volume terhadap kategori"
      ],
      "metadata": {
        "id": "9Hp8JZBEhy9Z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales_volume_by_category = order_items_products_translated_df.groupby('product_category_name_english')['price'].sum().reset_index()"
      ],
      "metadata": {
        "id": "aPkN0PzChlRI"
      },
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Sekarang melakukan sorting untuk melihat top nya"
      ],
      "metadata": {
        "id": "upraZh3wiXeB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_selling_categories = sales_volume_by_category.sort_values(by='price', ascending=False)\n",
        "\n",
        "top_selling_categories.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QRirCUNriWTg",
        "outputId": "9603d00f-d707-4aad-cf47-8f4f3666810b"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   product_category_name_english       price\n",
              "43                 health_beauty  1258681.34\n",
              "70                 watches_gifts  1205005.68\n",
              "7                 bed_bath_table  1036988.68\n",
              "65                sports_leisure   988048.97\n",
              "15         computers_accessories   911954.32"
            ],
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              "      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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-fa815350-5005-4dd6-af03-7a4bf385983c 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-fa815350-5005-4dd6-af03-7a4bf385983c');\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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-dbc59e6b-3fc0-4419-b09e-db7d0614377b')\"\n",
              "            title=\"Suggest charts\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-dbc59e6b-3fc0-4419-b09e-db7d0614377b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_selling_categories",
              "summary": "{\n  \"name\": \"top_selling_categories\",\n  \"rows\": 71,\n  \"fields\": [\n    {\n      \"column\": \"product_category_name_english\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 71,\n        \"samples\": [\n          \"fashion_bags_accessories\",\n          \"health_beauty\",\n          \"costruction_tools_tools\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 303705.2028956078,\n        \"min\": 283.28999999999996,\n        \"max\": 1258681.34,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          152823.54,\n          1258681.34,\n          15903.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selanjutnya, akan mengeksplorasikan Which sellers have the highest sales volume?"
      ],
      "metadata": {
        "id": "0XETTx6DkZIu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Menghitung sales volume dari setip seller"
      ],
      "metadata": {
        "id": "jEqu2MZJmKmX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales_volume = order_items_df.groupby('seller_id')['price'].sum().reset_index()"
      ],
      "metadata": {
        "id": "61eqW10Ekf1o"
      },
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Memperoleh top sellers dengan sorting"
      ],
      "metadata": {
        "id": "ZMJCldPyma7c"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "top_sellers = sales_volume.sort_values('price', ascending=False)"
      ],
      "metadata": {
        "id": "Cacpa4zUmRX_"
      },
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "top_sellers.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "RjIPypxPmXOM",
        "outputId": "1f2d0378-a1a9-49aa-ca34-ddaabfe8f783"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             seller_id      price\n",
              "857   4869f7a5dfa277a7dca6462dcf3b52b2  229472.63\n",
              "1013  53243585a1d6dc2643021fd1853d8905  222776.05\n",
              "881   4a3ca9315b744ce9f8e9374361493884  200472.92\n",
              "3024  fa1c13f2614d7b5c4749cbc52fecda94  194042.03\n",
              "1535  7c67e1448b00f6e969d365cea6b010ab  187923.89"
            ],
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              "      <td>200472.92</td>\n",
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              "      <th>3024</th>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      background-color: #E8F0FE;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
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              "\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-4e211dc2-53a1-4193-a2f3-6bb7541662cf 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-4e211dc2-53a1-4193-a2f3-6bb7541662cf');\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",
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              "  .colab-df-quickchart {\n",
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              "      --disabled-fill-color: #666;\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "    height: 32px;\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
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              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-259a3132-0071-4a33-8574-e65e7f4e1d9a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "top_sellers",
              "summary": "{\n  \"name\": \"top_sellers\",\n  \"rows\": 3095,\n  \"fields\": [\n    {\n      \"column\": \"seller_id\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3095,\n        \"samples\": [\n          \"ff1e15b778c700abdd4d239b81ac466d\",\n          \"6426d21aca402a131fc0a5d0960a3c90\",\n          \"562f8cfc51f265411f487d07e9c40e7f\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"price\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13921.997191689172,\n        \"min\": 3.5,\n        \"max\": 229472.63,\n        \"num_unique_values\": 2778,\n        \"samples\": [\n          2654.8,\n          354.92,\n          2597.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsyZjqak8DC2"
      },
      "source": [
        "## Visualization & Explanatory Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZxOiQ6n8DC2"
      },
      "source": [
        "### Pertanyaan 1:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "id": "1swJUdAD8DC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "outputId": "2a98baa2-bc60-42fd-87ec-b2b020fdbb20"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-75-90fb468c7bc1>:6: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=\"price\", y=\"product_category_name_english\", data=top_5_categories, palette=colors)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3500x1500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "\n",
        "top_5_categories = top_selling_categories.head()\n",
        "fig, ax = plt.subplots(figsize=(35, 15))\n",
        "\n",
        "colors = [\"#90CAF9\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
        "\n",
        "sns.barplot(x=\"price\", y=\"product_category_name_english\", data=top_5_categories, palette=colors)\n",
        "ax.set_ylabel(None)\n",
        "ax.set_xlabel(\"Number of Sales\", fontsize=30)\n",
        "ax.set_title(\"Best Performing Product\", loc=\"center\", fontsize=50)\n",
        "ax.tick_params(axis='y', labelsize=35)\n",
        "ax.tick_params(axis='x', labelsize=30)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DgHI7CiU8DC2"
      },
      "source": [
        "### Pertanyaan 2:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 76,
      "metadata": {
        "id": "Go0lCsvO8DC2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 369
        },
        "outputId": "e96f83ab-ca27-4778-da4c-d041a4adb858"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-76-fed2230306d3>:7: FutureWarning: \n",
            "\n",
            "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `y` variable to `hue` and set `legend=False` for the same effect.\n",
            "\n",
            "  sns.barplot(x=\"price\", y=\"seller_id\", data=top_5_sellers, palette=colors)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 3500x1500 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# Extract top 5 sellers for visualization\n",
        "top_5_sellers = top_sellers.head()\n",
        "fig, ax = plt.subplots(figsize=(35, 15))\n",
        "\n",
        "colors = [\"#90CAF9\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\", \"#D3D3D3\"]\n",
        "\n",
        "sns.barplot(x=\"price\", y=\"seller_id\", data=top_5_sellers, palette=colors)\n",
        "ax.set_ylabel(None)\n",
        "ax.set_xlabel(\"Number of Sales\", fontsize=30)\n",
        "ax.set_title(\"Seller ID\", loc=\"center\", fontsize=50)\n",
        "ax.tick_params(axis='y', labelsize=35)\n",
        "ax.tick_params(axis='x', labelsize=30)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WeHlCeX8DC2"
      },
      "source": [
        "## Conclusion"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTcyR48Y8DC2"
      },
      "source": [
        "- Conclution pertanyaan 1\n",
        "Kesimpulan dari pertanyaan satu adalah Top 5 Kategori yang memiliki volume penjualan tertinggi adalah health_beauty, watches_gifts, bed_bath_table, sport_leisure, dan computer_accessories\n",
        "\n",
        "Jika dilihat sudut pandang bisnis, hasil top 5 kategori terbaik di atas menunjukan tingginya permintaan konsumen. Hal ini dapat menyarankan pengusaha untuk menginvestasikan tenaga atau uang mereka untuk menargetkan kepada kategori-kategori ini karena demand yang tinggi.\n",
        "\n",
        "\n",
        "- Conclution pertanyaan 2\n",
        "Kesimpulan dari pertanyaan kedua adalah Top 5 Seles yang menghasilkan volume penjualan tertinggi adalah\n",
        "\n",
        "  1. 4869f7a5dfa277a7dca6462dcf3b52b2\n",
        "  2. 53243585a1d6dc2643021fd1853d8905\n",
        "  3. 4a3ca9315b744ce9f8e9374361493884\n",
        "  4. fa1c13f2614d7b5c4749cbc52fecda94\n",
        "  5. 7c67e1448b00f6e969d365cea6b010ab\n",
        "\n",
        "Jika dilihat dari sudut pandang bisnis, mengetahui top sellers dapat memberikan kita wawasan atas bagaimana kita dapat menganalisis strategi mereka. Baik dalam segi matketing, manajemen penjualan produk, dan lain-lain. Hal ini dapat memberikan wawasan berharga untuk diterapkan ke kita untuk meningkatkan performa penjualan atau membantu mengidentifikasi SWOT dari perusahaan kita maupun mereka."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "4KO34LqvV_MC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Exporting\n",
        "\n",
        "top_sellers.to_csv('top_sellers.csv', index=False)\n",
        "top_selling_categories.to_csv('top_selling_categories.csv', index=False)"
      ],
      "metadata": {
        "id": "_dIqcTb80D1r"
      },
      "execution_count": 77,
      "outputs": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.8.0 (tags/v3.8.0:fa919fd, Oct 14 2019, 19:21:23) [MSC v.1916 32 bit (Intel)]"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "972b3bf27e332e87b5379f2791f6ef9dfc79c71018c370b0d7423235e20fe4d7"
      }
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}