Final_Project_DBA_Kelompok_255.ipynb

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    "colab": {
      "name": "Final Project DBA - Kelompok 255.ipynb",
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        "TQj7LkRxtgSe",
        "xkpbftGFtw-a"
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      "name": "python3",
      "display_name": "Python 3"
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      "name": "python"
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/NabilahSharfina/Ruangguru-Bootcamp/blob/main/Final_Project_DBA_Kelompok_255.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***PRODUCT***"
      ],
      "metadata": {
        "id": "7sLJ97wBn9yy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "Qfrot9Bzp2cB"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# load data from google\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BudT44M2ZRT-",
        "outputId": "8e932eba-2da4-4a18-d1be-b70b8315e681"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Exploratory Data Analysis (EDA)**"
      ],
      "metadata": {
        "id": "Mb-RDGTUNaqS"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Load Data"
      ],
      "metadata": {
        "id": "bj3B938faS6h"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Table inventory_items"
      ],
      "metadata": {
        "id": "krHvagoUyf_W"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "height": 1000
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        "outputId": "7e6283fc-d83a-4671-cefb-adf17035b57e"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            id  product_id                 created_at  \\\n",
              "0        90967       16898  2021-07-19 02:20:10+00:00   \n",
              "1        90968       16898  2020-04-13 07:46:00+00:00   \n",
              "2        90969       16898  2020-09-02 17:43:00+00:00   \n",
              "3        90970       16898  2021-08-19 15:17:00+00:00   \n",
              "4        96715       16898  2020-05-09 11:16:10+00:00   \n",
              "...        ...         ...                        ...   \n",
              "487389  234443       14392  2020-04-09 17:38:00+00:00   \n",
              "487390  290890       14392  2021-03-18 14:42:49+00:00   \n",
              "487391  290891       14392  2022-04-25 03:04:00+00:00   \n",
              "487392  301361       14392  2021-05-15 11:44:11+00:00   \n",
              "487393  301362       14392  2020-06-22 15:50:00+00:00   \n",
              "\n",
              "                          sold_at       cost product_category  \\\n",
              "0       2021-07-20 14:34:10+00:00  13.925000      Tops & Tees   \n",
              "1                             NaN  13.925000      Tops & Tees   \n",
              "2                             NaN  13.925000      Tops & Tees   \n",
              "3                             NaN  13.925000      Tops & Tees   \n",
              "4       2020-06-23 23:59:10+00:00  13.925000      Tops & Tees   \n",
              "...                           ...        ...              ...   \n",
              "487389                        NaN  15.752521        Maternity   \n",
              "487390  2021-05-15 04:49:49+00:00  15.752521        Maternity   \n",
              "487391                        NaN  15.752521        Maternity   \n",
              "487392  2021-06-27 04:00:11+00:00  15.752521        Maternity   \n",
              "487393                        NaN  15.752521        Maternity   \n",
              "\n",
              "                                             product_name  \\\n",
              "0                   Quiksilver Waterman Men's On The Rise   \n",
              "1                   Quiksilver Waterman Men's On The Rise   \n",
              "2                   Quiksilver Waterman Men's On The Rise   \n",
              "3                   Quiksilver Waterman Men's On The Rise   \n",
              "4                   Quiksilver Waterman Men's On The Rise   \n",
              "...                                                   ...   \n",
              "487389  La Leche League International Pullover Nursing...   \n",
              "487390  La Leche League International Pullover Nursing...   \n",
              "487391  La Leche League International Pullover Nursing...   \n",
              "487392  La Leche League International Pullover Nursing...   \n",
              "487393  La Leche League International Pullover Nursing...   \n",
              "\n",
              "                                    product_brand  product_retail_price  \\\n",
              "0                                             NaN             25.000000   \n",
              "1                                             NaN             25.000000   \n",
              "2                                             NaN             25.000000   \n",
              "3                                             NaN             25.000000   \n",
              "4                                             NaN             25.000000   \n",
              "...                                           ...                   ...   \n",
              "487389  La Leche League Sports Nurising Bra White             35.720001   \n",
              "487390  La Leche League Sports Nurising Bra White             35.720001   \n",
              "487391  La Leche League Sports Nurising Bra White             35.720001   \n",
              "487392  La Leche League Sports Nurising Bra White             35.720001   \n",
              "487393  La Leche League Sports Nurising Bra White             35.720001   \n",
              "\n",
              "       product_department                       product_sku  \\\n",
              "0                     Men  22811EE19846217512507785E74D12CC   \n",
              "1                     Men  22811EE19846217512507785E74D12CC   \n",
              "2                     Men  22811EE19846217512507785E74D12CC   \n",
              "3                     Men  22811EE19846217512507785E74D12CC   \n",
              "4                     Men  22811EE19846217512507785E74D12CC   \n",
              "...                   ...                               ...   \n",
              "487389              Women  625D2D3101C23C47A46984741C232FED   \n",
              "487390              Women  625D2D3101C23C47A46984741C232FED   \n",
              "487391              Women  625D2D3101C23C47A46984741C232FED   \n",
              "487392              Women  625D2D3101C23C47A46984741C232FED   \n",
              "487393              Women  625D2D3101C23C47A46984741C232FED   \n",
              "\n",
              "        product_distribution_center_id  \n",
              "0                                    3  \n",
              "1                                    3  \n",
              "2                                    3  \n",
              "3                                    3  \n",
              "4                                    3  \n",
              "...                                ...  \n",
              "487389                               9  \n",
              "487390                               9  \n",
              "487391                               9  \n",
              "487392                               9  \n",
              "487393                               9  \n",
              "\n",
              "[487394 rows x 12 columns]"
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              "      <th>sold_at</th>\n",
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              "      <th>0</th>\n",
              "      <td>90967</td>\n",
              "      <td>16898</td>\n",
              "      <td>2021-07-19 02:20:10+00:00</td>\n",
              "      <td>2021-07-20 14:34:10+00:00</td>\n",
              "      <td>13.925000</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.000000</td>\n",
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              "      <td>3</td>\n",
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              "      <td>2020-04-13 07:46:00+00:00</td>\n",
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              "      <td>13.925000</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>Men</td>\n",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "      <td>3</td>\n",
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              "      <th>2</th>\n",
              "      <td>90969</td>\n",
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              "      <td>2020-09-02 17:43:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>13.925000</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>Men</td>\n",
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              "      <td>3</td>\n",
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              "      <th>3</th>\n",
              "      <td>90970</td>\n",
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              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.000000</td>\n",
              "      <td>Men</td>\n",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "      <td>3</td>\n",
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              "      <th>4</th>\n",
              "      <td>96715</td>\n",
              "      <td>16898</td>\n",
              "      <td>2020-05-09 11:16:10+00:00</td>\n",
              "      <td>2020-06-23 23:59:10+00:00</td>\n",
              "      <td>13.925000</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>25.000000</td>\n",
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              "      <td>22811EE19846217512507785E74D12CC</td>\n",
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              "      <td>14392</td>\n",
              "      <td>2020-04-09 17:38:00+00:00</td>\n",
              "      <td>NaN</td>\n",
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              "      <td>La Leche League International Pullover Nursing...</td>\n",
              "      <td>La Leche League Sports Nurising Bra White</td>\n",
              "      <td>35.720001</td>\n",
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              "      <td>La Leche League International Pullover Nursing...</td>\n",
              "      <td>La Leche League Sports Nurising Bra White</td>\n",
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              "      <td>Maternity</td>\n",
              "      <td>La Leche League International Pullover Nursing...</td>\n",
              "      <td>La Leche League Sports Nurising Bra White</td>\n",
              "      <td>35.720001</td>\n",
              "      <td>Women</td>\n",
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              "      <td>15.752521</td>\n",
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              "      <td>La Leche League Sports Nurising Bra White</td>\n",
              "      <td>35.720001</td>\n",
              "      <td>Women</td>\n",
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              "      <td>14392</td>\n",
              "      <td>2020-06-22 15:50:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>15.752521</td>\n",
              "      <td>Maternity</td>\n",
              "      <td>La Leche League International Pullover Nursing...</td>\n",
              "      <td>La Leche League Sports Nurising Bra White</td>\n",
              "      <td>35.720001</td>\n",
              "      <td>Women</td>\n",
              "      <td>625D2D3101C23C47A46984741C232FED</td>\n",
              "      <td>9</td>\n",
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              "<p>487394 rows × 12 columns</p>\n",
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          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "inv_items = pd.read_csv('/content/drive/MyDrive/Project Ruang Guru Kel 255/Dataset Final Project DBA/inventory_items.csv')\n",
        "inv_items"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4Tw3sINprend",
        "outputId": "cb2f3c39-095f-409b-d59b-fcbc7a870975"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "(487394, 12)"
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          "metadata": {},
          "execution_count": 10
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      "cell_type": "code",
      "source": [
        "inv_items.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-T77Aq5dCfy",
        "outputId": "1af6ea95-8477-48e7-f9c3-dd1492346c5f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 487394 entries, 0 to 487393\n",
            "Data columns (total 12 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   id                              487394 non-null  int64  \n",
            " 1   product_id                      487394 non-null  int64  \n",
            " 2   created_at                      487394 non-null  object \n",
            " 3   sold_at                         180508 non-null  object \n",
            " 4   cost                            487394 non-null  float64\n",
            " 5   product_category                487394 non-null  object \n",
            " 6   product_name                    487359 non-null  object \n",
            " 7   product_brand                   487020 non-null  object \n",
            " 8   product_retail_price            487394 non-null  float64\n",
            " 9   product_department              487394 non-null  object \n",
            " 10  product_sku                     487394 non-null  object \n",
            " 11  product_distribution_center_id  487394 non-null  int64  \n",
            "dtypes: float64(2), int64(3), object(7)\n",
            "memory usage: 44.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Keterangan:**\n",
        "1. Dataframe memiliki total 487.394 baris dan 12 kolom.\n",
        "2. Dataframe masih memiliki *null values* di kolom `sold_at`.\n",
        "3. Target klasifikasi adalah kolom `product_distribution_center_id` dengan tipe data `int64`\n",
        "4. Berarti sisanya adalah *feature* (predictor)\n",
        "\n",
        "Dari informasi di atas, bisa dikelompokkan kolom kategorical dan numerical sebagai berikut:"
      ],
      "metadata": {
        "id": "ctg_2r7GVnEX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Kelompok categorical & numerical\n",
        "inv_cats = ['created_at', 'sold_at', 'product_category', 'product_name', 'product_brand', 'product_department', 'product_sku']\n",
        "inv_nums = ['id', 'product_id', 'cost', 'product_retail_price', 'product_distribution_center_id']"
      ],
      "metadata": {
        "id": "jhvN-J_NWj8E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2WGo1dGB3g9h",
        "outputId": "ddb721ed-daaf-4194-c15a-76703782e23a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['id', 'product_id', 'created_at', 'sold_at', 'cost',\n",
              "       'product_category', 'product_name', 'product_brand',\n",
              "       'product_retail_price', 'product_department', 'product_sku',\n",
              "       'product_distribution_center_id'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KOyahIgdJG-_",
        "outputId": "2a64de49-0725-48d8-aa6c-8657da37e700"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                  int64\n",
              "product_id                          int64\n",
              "created_at                         object\n",
              "sold_at                            object\n",
              "cost                              float64\n",
              "product_category                   object\n",
              "product_name                       object\n",
              "product_brand                      object\n",
              "product_retail_price              float64\n",
              "product_department                 object\n",
              "product_sku                        object\n",
              "product_distribution_center_id      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vsRk9ICPJBKu",
        "outputId": "f4d09448-4886-4d6e-e3eb-ae73926abeea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                487394\n",
              "product_id                        487394\n",
              "created_at                        487394\n",
              "sold_at                           180508\n",
              "cost                              487394\n",
              "product_category                  487394\n",
              "product_name                      487359\n",
              "product_brand                     487020\n",
              "product_retail_price              487394\n",
              "product_department                487394\n",
              "product_sku                       487394\n",
              "product_distribution_center_id    487394\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Table products"
      ],
      "metadata": {
        "id": "s5KN5e4syzF2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products = pd.read_csv('/content/drive/MyDrive/Project Ruang Guru Kel 255/Dataset Final Project DBA/products.csv')\n",
        "products"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 974
        },
        "id": "juhNtfG3fuGn",
        "outputId": "267f0966-c032-4968-b7df-c79b87e45794"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          id       cost                       category  \\\n",
              "0      27569  92.652563                           Swim   \n",
              "1      27445  24.719661                           Swim   \n",
              "2      27457  15.897600                           Swim   \n",
              "3      27466  17.850000                           Swim   \n",
              "4      27481  29.408001                           Swim   \n",
              "...      ...        ...                            ...   \n",
              "29115   1993  22.150999  Fashion Hoodies & Sweatshirts   \n",
              "29116   2062  18.973360  Fashion Hoodies & Sweatshirts   \n",
              "29117   2308   9.028020  Fashion Hoodies & Sweatshirts   \n",
              "29118   1852  17.674240  Fashion Hoodies & Sweatshirts   \n",
              "29119   2296  17.533261  Fashion Hoodies & Sweatshirts   \n",
              "\n",
              "                                                    name  \\\n",
              "0         2XU Men's Swimmers Compression Long Sleeve Top   \n",
              "1             TYR Sport Men's Square Leg Short Swim Suit   \n",
              "2        TYR Sport Men's Solid Durafast Jammer Swim Suit   \n",
              "3      TYR Sport Men's Swim Short/Resistance Short Sw...   \n",
              "4                        TYR Alliance Team Splice Jammer   \n",
              "...                                                  ...   \n",
              "29115  Comfort Colors C1598 Womens Full-Zip Hooded Fl...   \n",
              "29116  Comfort Colors Pigment-Dyed Ladies Full-Zip Ho...   \n",
              "29117  Comfort Colors by Chouinard Women's Drawstring...   \n",
              "29118  DV by Dolce Vita Women's Larisa Hooded Sweatshirt   \n",
              "29119    Independent Trading Co Deluxe Fleece Zip Hoodie   \n",
              "\n",
              "                        brand  retail_price department  \\\n",
              "0                         2XU    150.410004        Men   \n",
              "1                         TYR     38.990002        Men   \n",
              "2                         TYR     27.600000        Men   \n",
              "3                         TYR     30.000000        Men   \n",
              "4                         TYR     45.950001        Men   \n",
              "...                       ...           ...        ...   \n",
              "29115          Comfort Colors     52.119999      Women   \n",
              "29116          Comfort Colors     39.860001      Women   \n",
              "29117          Comfort Colors     18.020000      Women   \n",
              "29118        DV by Dolce Vita     34.520000      Women   \n",
              "29119  Independent Trading Co     36.990002      Women   \n",
              "\n",
              "                                    sku  distribution_center_id  \n",
              "0      B23C5765E165D83AA924FA8F13C05F25                       1  \n",
              "1      2AB7D3B23574C3DEA2BD278AFD0939AB                       1  \n",
              "2      8F831227B0EB6C6D09A0555531365933                       1  \n",
              "3      67317D6DCC4CB778AEB9219565F5456B                       1  \n",
              "4      213C888198806EF1A0E2BBF2F4855C6C                       1  \n",
              "...                                 ...                     ...  \n",
              "29115  C5A4E7E6882845EA7BB4D9462868219B                      10  \n",
              "29116  2D405B367158E3F12D7C1E31A96B3AF3                      10  \n",
              "29117  E58AEA67B01FA747687F038DFDE066F6                      10  \n",
              "29118  EB1E78328C46506B46A4AC4A1E378B91                      10  \n",
              "29119  130F1A8E9E102707F3F91B010F151B0B                      10  \n",
              "\n",
              "[29120 rows x 9 columns]"
            ],
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          },
          "metadata": {},
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        "products.shape"
      ],
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        "id": "ow5nLuFrfuLA",
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(29120, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "products.columns.values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FtexRNC5JigG",
        "outputId": "147df913-c832-4422-a035-ca5308ce0385"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['id', 'cost', 'category', 'name', 'brand', 'retail_price',\n",
              "       'department', 'sku', 'distribution_center_id'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "products.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HPzHJAg4JiiL",
        "outputId": "3fca8e1a-c1f5-41a6-e193-27f2a67eadf5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 29120 entries, 0 to 29119\n",
            "Data columns (total 9 columns):\n",
            " #   Column                  Non-Null Count  Dtype  \n",
            "---  ------                  --------------  -----  \n",
            " 0   id                      29120 non-null  int64  \n",
            " 1   cost                    29120 non-null  float64\n",
            " 2   category                29120 non-null  object \n",
            " 3   name                    29118 non-null  object \n",
            " 4   brand                   29096 non-null  object \n",
            " 5   retail_price            29120 non-null  float64\n",
            " 6   department              29120 non-null  object \n",
            " 7   sku                     29120 non-null  object \n",
            " 8   distribution_center_id  29120 non-null  int64  \n",
            "dtypes: float64(2), int64(2), object(5)\n",
            "memory usage: 2.0+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "products.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8JuDF6A7Jila",
        "outputId": "e3af85e5-4530-4fb5-834f-f028427d2f8c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                          int64\n",
              "cost                      float64\n",
              "category                   object\n",
              "name                       object\n",
              "brand                      object\n",
              "retail_price              float64\n",
              "department                 object\n",
              "sku                        object\n",
              "distribution_center_id      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "products.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dF3kDKavJr3O",
        "outputId": "1f45ef9b-20bb-4c1a-e31e-ed80d5aac909"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                        29120\n",
              "cost                      29120\n",
              "category                  29120\n",
              "name                      29118\n",
              "brand                     29096\n",
              "retail_price              29120\n",
              "department                29120\n",
              "sku                       29120\n",
              "distribution_center_id    29120\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Numerical Data\n",
        "numerics = ['int8','int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "\n",
        "display(products.select_dtypes(include = numerics).columns)\n",
        "print(products.select_dtypes(include = numerics).shape)\n",
        "\n",
        "df_num_products = products.select_dtypes(include = numerics)\n",
        "df_num_products.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rXDT49aZvHob",
        "outputId": "a4558d61-358a-4fe6-f36f-3a7bdc08a241"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['id', 'cost', 'retail_price', 'distribution_center_id'], dtype='object')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(29120, 4)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id       cost  retail_price  distribution_center_id\n",
              "0  27569  92.652563    150.410004                       1\n",
              "1  27445  24.719661     38.990002                       1\n",
              "2  27457  15.897600     27.600000                       1\n",
              "3  27466  17.850000     30.000000                       1\n",
              "4  27481  29.408001     45.950001                       1"
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              "                                                     [key], {});\n",
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              "\n",
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      "cell_type": "code",
      "source": [
        "df_num_products.describe().transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3P9PoGvWvH7G",
        "outputId": "29952da8-1385-488a-bbcd-44f1976b0a77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 id          cost  retail_price  distribution_center_id\n",
              "count  29120.000000  29120.000000  29120.000000            29120.000000\n",
              "mean   14560.500000     28.481774     59.220164                4.982898\n",
              "std     8406.364256     30.624681     65.888927                2.901153\n",
              "min        1.000000      0.008300      0.020000                1.000000\n",
              "25%     7280.750000     11.275613     24.000000                2.000000\n",
              "50%    14560.500000     19.675081     39.990002                5.000000\n",
              "75%    21840.250000     34.440000     69.949997                8.000000\n",
              "max    29120.000000    557.151002    999.000000               10.000000"
            ],
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              "      <th>distribution_center_id</th>\n",
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              "      <th>count</th>\n",
              "      <td>29120.000000</td>\n",
              "      <td>29120.000000</td>\n",
              "      <td>29120.000000</td>\n",
              "      <td>29120.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>14560.500000</td>\n",
              "      <td>28.481774</td>\n",
              "      <td>59.220164</td>\n",
              "      <td>4.982898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8406.364256</td>\n",
              "      <td>30.624681</td>\n",
              "      <td>65.888927</td>\n",
              "      <td>2.901153</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.008300</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7280.750000</td>\n",
              "      <td>11.275613</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>14560.500000</td>\n",
              "      <td>19.675081</td>\n",
              "      <td>39.990002</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>21840.250000</td>\n",
              "      <td>34.440000</td>\n",
              "      <td>69.949997</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>29120.000000</td>\n",
              "      <td>557.151002</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>10.000000</td>\n",
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              "\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Non Numerical Data\n",
        "display(products.select_dtypes(include = ['object']).columns)\n",
        "print(products.select_dtypes(include = object).shape)\n",
        "\n",
        "df_cat_products = products.select_dtypes(include = ['object'])\n",
        "df_cat_products.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_awyjrQdtf1F",
        "outputId": "284b1ec8-ff9c-4fb8-aad2-4f373e1e89dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['category', 'name', 'brand', 'department', 'sku'], dtype='object')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(29120, 5)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  category                                                   name brand  \\\n",
              "0     Swim         2XU Men's Swimmers Compression Long Sleeve Top   2XU   \n",
              "1     Swim             TYR Sport Men's Square Leg Short Swim Suit   TYR   \n",
              "2     Swim        TYR Sport Men's Solid Durafast Jammer Swim Suit   TYR   \n",
              "3     Swim  TYR Sport Men's Swim Short/Resistance Short Swim Suit   TYR   \n",
              "4     Swim                        TYR Alliance Team Splice Jammer   TYR   \n",
              "\n",
              "  department                               sku  \n",
              "0        Men  B23C5765E165D83AA924FA8F13C05F25  \n",
              "1        Men  2AB7D3B23574C3DEA2BD278AFD0939AB  \n",
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        "df_cat_products.describe()"
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            "text/plain": [
              "         category                                                name  \\\n",
              "count       29120                                               29118   \n",
              "unique         26                                               27309   \n",
              "top     Intimates  Wrangler Men's Premium Performance Cowboy Cut Jean   \n",
              "freq         2363                                                   8   \n",
              "\n",
              "            brand department                               sku  \n",
              "count       29096      29120                             29120  \n",
              "unique       2756          2                             29120  \n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek missing value\n",
        "products.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nJ2lkb50u5xi",
        "outputId": "a6e42f79-6861-4aa2-bac4-8e7e8db102f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                         0\n",
              "cost                       0\n",
              "category                   0\n",
              "name                       2\n",
              "brand                     24\n",
              "retail_price               0\n",
              "department                 0\n",
              "sku                        0\n",
              "distribution_center_id     0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rows = []\n",
        "for col in df_cat_products :\n",
        "  rows.append(\n",
        "    {\n",
        "      'Nama Kolom': col,\n",
        "      'Total Unique Values': len(products[col].unique()),\n",
        "      'Unique Values':  products[col].unique()\n",
        "    }\n",
        "  )\n",
        "\n",
        "# pd.options.display.max_colwidth = 100 #maksimal tampil 100 karakter\n",
        "# maksimal tampil 70 karakter\n",
        "pd.options.display.max_colwidth = 70 \n",
        "\n",
        "# pd.options.display.max_colwidth \n",
        "# tanpa batas \n",
        "unik_products = pd.DataFrame(rows)\n",
        "unik_products.sort_values(by='Total Unique Values', ascending=False).reset_index().drop('index', 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WkkTe66ztFZL",
        "outputId": "553b2e33-ed0d-4d0d-eb40-ee0843df7465"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n"
          ]
        },
        {
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          "data": {
            "text/plain": [
              "   Nama Kolom  Total Unique Values  \\\n",
              "0         sku                29120   \n",
              "1        name                27310   \n",
              "2       brand                 2757   \n",
              "3    category                   26   \n",
              "4  department                    2   \n",
              "\n",
              "                                                           Unique Values  \n",
              "0  [B23C5765E165D83AA924FA8F13C05F25, 2AB7D3B23574C3DEA2BD278AFD0939A...  \n",
              "1  [2XU Men's Swimmers Compression Long Sleeve Top, TYR Sport Men's S...  \n",
              "2  [2XU, TYR, Onia, 2EROS, Matix, Alki'i, Hurley, TRUNKS, Ezekiel, Ru...  \n",
              "3  [Swim, Jeans, Pants, Socks, Active, Shorts, Sweaters, Underwear, A...  \n",
              "4                                                           [Men, Women]  "
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              "\n",
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              "          const element = document.querySelector('#df-8676bec7-b895-4e4e-9785-97389a910a26');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fungsi count():**\n",
        "This gives the number of non-missing values for each variable and is extremely useful while handling missing values in a data frame."
      ],
      "metadata": {
        "id": "oNnMBDzyLEbk"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Table orders"
      ],
      "metadata": {
        "id": "GExzCUECy20J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "orders = pd.read_csv('/content/drive/MyDrive/Project Ruang Guru Kel 255/Dataset Final Project DBA/orders.csv')\n",
        "orders.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 337
        },
        "id": "osV9Qp1HgS7N",
        "outputId": "d8e2361c-7f42-4f48-8b04-02f739d73f9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   order_id  user_id   status gender                 created_at returned_at  \\\n",
              "0        11        9  Shipped      F  2021-12-11 05:34:00+00:00         NaN   \n",
              "1        12        9  Shipped      F  2022-01-18 05:34:00+00:00         NaN   \n",
              "2        22       14  Shipped      F  2020-08-18 11:51:00+00:00         NaN   \n",
              "3        25       16  Shipped      F  2020-12-16 08:35:00+00:00         NaN   \n",
              "4        26       16  Shipped      F  2020-09-07 08:35:00+00:00         NaN   \n",
              "\n",
              "                  shipped_at delivered_at  num_of_item  \n",
              "0  2021-12-12 10:01:00+00:00          NaN            3  \n",
              "1  2022-01-19 20:20:00+00:00          NaN            1  \n",
              "2  2020-08-20 16:45:00+00:00          NaN            1  \n",
              "3  2020-12-17 21:43:00+00:00          NaN            2  \n",
              "4  2020-09-10 00:15:00+00:00          NaN            1  "
            ],
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              "\n",
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>order_id</th>\n",
              "      <th>user_id</th>\n",
              "      <th>status</th>\n",
              "      <th>gender</th>\n",
              "      <th>created_at</th>\n",
              "      <th>returned_at</th>\n",
              "      <th>shipped_at</th>\n",
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              "      <td>3</td>\n",
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              "      <th>1</th>\n",
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              "      <td>9</td>\n",
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              "      <td>14</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>F</td>\n",
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              "      <th>3</th>\n",
              "      <td>25</td>\n",
              "      <td>16</td>\n",
              "      <td>Shipped</td>\n",
              "      <td>F</td>\n",
              "      <td>2020-12-16 08:35:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-12-17 21:43:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
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              "      <td>26</td>\n",
              "      <td>16</td>\n",
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              "      <td>2020-09-07 08:35:00+00:00</td>\n",
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              "      <td>NaN</td>\n",
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              "          const element = document.querySelector('#df-a40bba89-3330-405e-b4ef-6c094d9725cf');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "orders.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h8j3vf0zgS-n",
        "outputId": "cce102bf-0003-4207-bc0b-d53ee4c99221"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(124512, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Numerical Data\n",
        "numerics = ['int8','int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "\n",
        "display(orders.select_dtypes(include = numerics).columns)\n",
        "print(orders.select_dtypes(include = numerics).shape)\n",
        "\n",
        "df_num_orders = orders.select_dtypes(include = numerics)\n",
        "df_num_orders.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "id": "v9SC_yoIvXBj",
        "outputId": "db25859e-63ec-40d7-bd8c-b4957efa4bd8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['order_id', 'user_id', 'num_of_item'], dtype='object')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(124512, 3)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   order_id  user_id  num_of_item\n",
              "0        11        9            3\n",
              "1        12        9            1\n",
              "2        22       14            1\n",
              "3        25       16            2\n",
              "4        26       16            1"
            ],
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              "                                                     [key], {});\n",
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              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_num_orders.describe().transpose()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "R8zySdQrvXFo",
        "outputId": "3ece7825-d74f-4f05-ed28-f7896f0f525b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            order_id        user_id    num_of_item\n",
              "count  124512.000000  124512.000000  124512.000000\n",
              "mean    62256.500000   50050.013613       1.449724\n",
              "std     35943.662696   28800.914580       0.802567\n",
              "min         1.000000       1.000000       1.000000\n",
              "25%     31128.750000   25151.000000       1.000000\n",
              "50%     62256.500000   50071.500000       1.000000\n",
              "75%     93384.250000   74949.250000       2.000000\n",
              "max    124512.000000  100000.000000       4.000000"
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              "\n",
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              "      <th>mean</th>\n",
              "      <td>62256.500000</td>\n",
              "      <td>50050.013613</td>\n",
              "      <td>1.449724</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>35943.662696</td>\n",
              "      <td>28800.914580</td>\n",
              "      <td>0.802567</td>\n",
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              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>31128.750000</td>\n",
              "      <td>25151.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>50071.500000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>93384.250000</td>\n",
              "      <td>74949.250000</td>\n",
              "      <td>2.000000</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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            ]
          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Non Numerical Data\n",
        "display(orders.select_dtypes(include = ['object']).columns)\n",
        "print(orders.select_dtypes(include = object).shape)\n",
        "\n",
        "df_cat_orders = orders.select_dtypes(include = ['object'])\n",
        "df_cat_orders.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "n2Ko_9pyuRjI",
        "outputId": "0af2874a-a246-4e88-db52-77ff71bb2ff4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['status', 'gender', 'created_at', 'returned_at', 'shipped_at',\n",
              "       'delivered_at'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(124512, 6)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    status gender                 created_at returned_at  \\\n",
              "0  Shipped      F  2021-12-11 05:34:00+00:00         NaN   \n",
              "1  Shipped      F  2022-01-18 05:34:00+00:00         NaN   \n",
              "2  Shipped      F  2020-08-18 11:51:00+00:00         NaN   \n",
              "3  Shipped      F  2020-12-16 08:35:00+00:00         NaN   \n",
              "4  Shipped      F  2020-09-07 08:35:00+00:00         NaN   \n",
              "\n",
              "                  shipped_at delivered_at  \n",
              "0  2021-12-12 10:01:00+00:00          NaN  \n",
              "1  2022-01-19 20:20:00+00:00          NaN  \n",
              "2  2020-08-20 16:45:00+00:00          NaN  \n",
              "3  2020-12-17 21:43:00+00:00          NaN  \n",
              "4  2020-09-10 00:15:00+00:00          NaN  "
            ],
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              "      <td>2020-09-07 08:35:00+00:00</td>\n",
              "      <td>NaN</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            + ' to learn more about interactive tables.';\n",
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    {
      "cell_type": "code",
      "source": [
        "df_cat_orders.describe()"
      ],
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      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "         status  gender                 created_at                returned_at  \\\n",
              "count    124512  124512                     124512                      12149   \n",
              "unique        5       2                     113885                      12065   \n",
              "top     Shipped       F  2022-06-01 12:08:00+00:00  2021-09-07 00:37:00+00:00   \n",
              "freq      37466   62288                          5                          2   \n",
              "\n",
              "                       shipped_at               delivered_at  \n",
              "count                       80884                      43418  \n",
              "unique                      77625                      42494  \n",
              "top     2022-05-20 07:35:00+00:00  2022-06-01 05:15:00+00:00  \n",
              "freq                            5                          3  "
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              "      <td>2021-09-07 00:37:00+00:00</td>\n",
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              "      <td>2022-06-01 05:15:00+00:00</td>\n",
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              "      <td>3</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ca64af6a-95e8-44b2-8bb8-8dab43c15072')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ca64af6a-95e8-44b2-8bb8-8dab43c15072 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-ca64af6a-95e8-44b2-8bb8-8dab43c15072');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek missing value\n",
        "orders.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4t1SSq-ZujrJ",
        "outputId": "188e8529-372d-4598-ce5e-aa96c742fab1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "order_id             0\n",
              "user_id              0\n",
              "status               0\n",
              "gender               0\n",
              "created_at           0\n",
              "returned_at     112363\n",
              "shipped_at       43628\n",
              "delivered_at     81094\n",
              "num_of_item          0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Duplicated data\n",
        "orders.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AnMTQQeCwkrF",
        "outputId": "b0989175-2f4f-47ed-dad4-82248aff3891"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rows = []\n",
        "for col in df_cat_orders :\n",
        "  rows.append(\n",
        "    {\n",
        "      'Nama Kolom': col,\n",
        "      'Total Unique Values': len(orders[col].unique()),\n",
        "      'Unique Values':  orders[col].unique()\n",
        "    }\n",
        "  )\n",
        "\n",
        "# pd.options.display.max_colwidth = 100 #maksimal tampil 100 karakter\n",
        "#maksimal tampil 70 karakter\n",
        "pd.options.display.max_colwidth = 70 \n",
        "\n",
        "# pd.options.display.max_colwidth \n",
        "#tanpa batas \n",
        "unik_orders = pd.DataFrame(rows)\n",
        "unik_orders.sort_values(by='Total Unique Values', ascending=False).reset_index().drop('index', 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 275
        },
        "id": "bu87xfXYxXyT",
        "outputId": "6d73a46a-728a-4f94-e236-4ac9884b0fdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Nama Kolom  Total Unique Values  \\\n",
              "0    created_at               113885   \n",
              "1    shipped_at                77626   \n",
              "2  delivered_at                42495   \n",
              "3   returned_at                12066   \n",
              "4        status                    5   \n",
              "5        gender                    2   \n",
              "\n",
              "                                                           Unique Values  \n",
              "0  [2021-12-11 05:34:00+00:00, 2022-01-18 05:34:00+00:00, 2020-08-18 ...  \n",
              "1  [2021-12-12 10:01:00+00:00, 2022-01-19 20:20:00+00:00, 2020-08-20 ...  \n",
              "2  [nan, 2022-02-19 03:08:00+00:00, 2021-01-20 02:36:00+00:00, 2021-0...  \n",
              "3  [nan, 2022-02-01 03:01:00+00:00, 2021-01-07 01:12:00+00:00, 2020-0...  \n",
              "4                   [Shipped, Complete, Returned, Cancelled, Processing]  \n",
              "5                                                                 [F, M]  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-e31c5d1a-5d87-4fa6-a8f1-817cfc9fea33\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nama Kolom</th>\n",
              "      <th>Total Unique Values</th>\n",
              "      <th>Unique Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>created_at</td>\n",
              "      <td>113885</td>\n",
              "      <td>[2021-12-11 05:34:00+00:00, 2022-01-18 05:34:00+00:00, 2020-08-18 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>shipped_at</td>\n",
              "      <td>77626</td>\n",
              "      <td>[2021-12-12 10:01:00+00:00, 2022-01-19 20:20:00+00:00, 2020-08-20 ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>delivered_at</td>\n",
              "      <td>42495</td>\n",
              "      <td>[nan, 2022-02-19 03:08:00+00:00, 2021-01-20 02:36:00+00:00, 2021-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>returned_at</td>\n",
              "      <td>12066</td>\n",
              "      <td>[nan, 2022-02-01 03:01:00+00:00, 2021-01-07 01:12:00+00:00, 2020-0...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>status</td>\n",
              "      <td>5</td>\n",
              "      <td>[Shipped, Complete, Returned, Cancelled, Processing]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>gender</td>\n",
              "      <td>2</td>\n",
              "      <td>[F, M]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-e31c5d1a-5d87-4fa6-a8f1-817cfc9fea33')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "      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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-e31c5d1a-5d87-4fa6-a8f1-817cfc9fea33 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-e31c5d1a-5d87-4fa6-a8f1-817cfc9fea33');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "YxxYq6J5Lpkw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Table events"
      ],
      "metadata": {
        "id": "WuxmhZS-y5jZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "events = pd.read_csv('/content/drive/MyDrive/Project Ruang Guru Kel 255/Dataset Final Project DBA/events.csv')\n",
        "events.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nrEw4VaZhRdk",
        "outputId": "c1bddd6a-45a3-4956-f860-68280897641e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     id  user_id  sequence_number                            session_id  \\\n",
              "0   252     14.0                1  4ee0d7d3-ed29-4b1c-b038-1e3f78942acd   \n",
              "1  4473    301.0                1  a77d6232-0a03-4616-b836-446a32b9e4f4   \n",
              "2  5756    401.0                1  0e517fbd-e152-4918-8f01-6036a180813c   \n",
              "3  8944    683.0                1  4c2fada8-7095-4de2-9728-31c0eed658b0   \n",
              "4  9164    709.0                1  5a9c26be-01e9-4918-a908-77476667faa2   \n",
              "\n",
              "                  created_at      ip_address                 city     state  \\\n",
              "0  2020-08-18 08:46:32+00:00  138.65.151.151              Daejeon   Daejeon   \n",
              "1  2020-08-19 03:47:46+00:00  153.204.205.22             São Luís  Maranhão   \n",
              "2  2020-06-07 07:03:58+00:00  209.47.200.122  Sant Adrià de Besòs  Cataluña   \n",
              "3  2020-09-23 04:52:13+00:00    60.42.59.118            Inhambupe     Bahia   \n",
              "4  2021-07-29 16:05:33+00:00  194.208.170.21             Bayreuth    Bayern   \n",
              "\n",
              "  postal_code browser traffic_source    uri event_type  \n",
              "0     302-160  Chrome          Email  /home       home  \n",
              "1   65000-000      IE          Email  /home       home  \n",
              "2        8930  Chrome          Email  /home       home  \n",
              "3   48490-000  Chrome          Email  /home       home  \n",
              "4       95448      IE          Email  /home       home  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7a0ee682-b2b5-4eaf-9394-700db25a75f1\">\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>user_id</th>\n",
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              "      <th>session_id</th>\n",
              "      <th>created_at</th>\n",
              "      <th>ip_address</th>\n",
              "      <th>city</th>\n",
              "      <th>state</th>\n",
              "      <th>postal_code</th>\n",
              "      <th>browser</th>\n",
              "      <th>traffic_source</th>\n",
              "      <th>uri</th>\n",
              "      <th>event_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>252</td>\n",
              "      <td>14.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4ee0d7d3-ed29-4b1c-b038-1e3f78942acd</td>\n",
              "      <td>2020-08-18 08:46:32+00:00</td>\n",
              "      <td>138.65.151.151</td>\n",
              "      <td>Daejeon</td>\n",
              "      <td>Daejeon</td>\n",
              "      <td>302-160</td>\n",
              "      <td>Chrome</td>\n",
              "      <td>Email</td>\n",
              "      <td>/home</td>\n",
              "      <td>home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4473</td>\n",
              "      <td>301.0</td>\n",
              "      <td>1</td>\n",
              "      <td>a77d6232-0a03-4616-b836-446a32b9e4f4</td>\n",
              "      <td>2020-08-19 03:47:46+00:00</td>\n",
              "      <td>153.204.205.22</td>\n",
              "      <td>São Luís</td>\n",
              "      <td>Maranhão</td>\n",
              "      <td>65000-000</td>\n",
              "      <td>IE</td>\n",
              "      <td>Email</td>\n",
              "      <td>/home</td>\n",
              "      <td>home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>5756</td>\n",
              "      <td>401.0</td>\n",
              "      <td>1</td>\n",
              "      <td>0e517fbd-e152-4918-8f01-6036a180813c</td>\n",
              "      <td>2020-06-07 07:03:58+00:00</td>\n",
              "      <td>209.47.200.122</td>\n",
              "      <td>Sant Adrià de Besòs</td>\n",
              "      <td>Cataluña</td>\n",
              "      <td>8930</td>\n",
              "      <td>Chrome</td>\n",
              "      <td>Email</td>\n",
              "      <td>/home</td>\n",
              "      <td>home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>8944</td>\n",
              "      <td>683.0</td>\n",
              "      <td>1</td>\n",
              "      <td>4c2fada8-7095-4de2-9728-31c0eed658b0</td>\n",
              "      <td>2020-09-23 04:52:13+00:00</td>\n",
              "      <td>60.42.59.118</td>\n",
              "      <td>Inhambupe</td>\n",
              "      <td>Bahia</td>\n",
              "      <td>48490-000</td>\n",
              "      <td>Chrome</td>\n",
              "      <td>Email</td>\n",
              "      <td>/home</td>\n",
              "      <td>home</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>9164</td>\n",
              "      <td>709.0</td>\n",
              "      <td>1</td>\n",
              "      <td>5a9c26be-01e9-4918-a908-77476667faa2</td>\n",
              "      <td>2021-07-29 16:05:33+00:00</td>\n",
              "      <td>194.208.170.21</td>\n",
              "      <td>Bayreuth</td>\n",
              "      <td>Bayern</td>\n",
              "      <td>95448</td>\n",
              "      <td>IE</td>\n",
              "      <td>Email</td>\n",
              "      <td>/home</td>\n",
              "      <td>home</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7a0ee682-b2b5-4eaf-9394-700db25a75f1')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Descriptive Statistic"
      ],
      "metadata": {
        "id": "Cj4mxVqlJ3yC"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Table inventory_items"
      ],
      "metadata": {
        "id": "eOM5WvkEqoM7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fungsi describe():**\n",
        "Get the following characteristics of the numerical variables namely to count (number of non-missing values), mean, standard deviation, and the 5 point summary which includes minimum, first quartile, second quartile, third quartile, and maximum."
      ],
      "metadata": {
        "id": "ngQ5TU0oKvqs"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.describe()"
      ],
      "metadata": {
        "id": "7rmeaweyhzgv",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "4c590f6b-ce12-44d9-dea9-9f029f0410ae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  id     product_id           cost  product_retail_price  \\\n",
              "count  487394.000000  487394.000000  487394.000000         487394.000000   \n",
              "mean   243697.500000   15292.257424      28.746374             59.772834   \n",
              "std    140698.672888    8408.118539      30.629290             65.871768   \n",
              "min         1.000000       1.000000       0.008300              0.020000   \n",
              "25%    121849.250000    8047.000000      11.394300             24.780001   \n",
              "50%    243697.500000   16032.000000      19.944301             39.990002   \n",
              "75%    365545.750000   22558.000000      34.698691             69.949997   \n",
              "max    487394.000000   29120.000000     557.151002            999.000000   \n",
              "\n",
              "       product_distribution_center_id  \n",
              "count                   487394.000000  \n",
              "mean                         4.992101  \n",
              "std                          2.895917  \n",
              "min                          1.000000  \n",
              "25%                          2.000000  \n",
              "50%                          5.000000  \n",
              "75%                          8.000000  \n",
              "max                         10.000000  "
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              "      <th>product_retail_price</th>\n",
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              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>243697.500000</td>\n",
              "      <td>15292.257424</td>\n",
              "      <td>28.746374</td>\n",
              "      <td>59.772834</td>\n",
              "      <td>4.992101</td>\n",
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              "      <th>std</th>\n",
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              "      <td>8408.118539</td>\n",
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              "      <td>65.871768</td>\n",
              "      <td>2.895917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <td>0.020000</td>\n",
              "      <td>1.000000</td>\n",
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              "      <th>25%</th>\n",
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              "      <td>24.780001</td>\n",
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              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>39.990002</td>\n",
              "      <td>5.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
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              "      <td>22558.000000</td>\n",
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              "      <td>69.949997</td>\n",
              "      <td>8.000000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>557.151002</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Beberapa pengamatan dari *statistical summary* kolom numerik di atas:\n",
        "- Distribusi nilai di kolom `id` dan `product_id` terlihat normal (*mean* dan *median* selisihnya tidak terlalu jauh).\n",
        "- Nilai Q2 berada tepat di antara Q1 - Q3)\n",
        "- Kolom `cost`, `product_retail_price`, dan `product_distribution_center_id` tampaknya tidak simetrik, karena *mean* > *median*."
      ],
      "metadata": {
        "id": "LQWqLxvUrbBa"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.describe(include='all')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "GWoPWPZxKBkT",
        "outputId": "ba3b299a-d0be-4d36-8f85-597fd8ce7ea2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   id     product_id                 created_at  \\\n",
              "count   487394.000000  487394.000000                     487394   \n",
              "unique            NaN            NaN                     444495   \n",
              "top               NaN            NaN  2021-01-18 09:21:00+00:00   \n",
              "freq              NaN            NaN                          5   \n",
              "mean    243697.500000   15292.257424                        NaN   \n",
              "std     140698.672888    8408.118539                        NaN   \n",
              "min          1.000000       1.000000                        NaN   \n",
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              "50%     243697.500000   16032.000000                        NaN   \n",
              "75%     365545.750000   22558.000000                        NaN   \n",
              "max     487394.000000   29120.000000                        NaN   \n",
              "\n",
              "                          sold_at           cost product_category  \\\n",
              "count                      180508  487394.000000           487394   \n",
              "unique                     180172            NaN               26   \n",
              "top     2022-03-10 07:49:24+00:00            NaN        Intimates   \n",
              "freq                            3            NaN            36319   \n",
              "mean                          NaN      28.746374              NaN   \n",
              "std                           NaN      30.629290              NaN   \n",
              "min                           NaN       0.008300              NaN   \n",
              "25%                           NaN      11.394300              NaN   \n",
              "50%                           NaN      19.944301              NaN   \n",
              "75%                           NaN      34.698691              NaN   \n",
              "max                           NaN     557.151002              NaN   \n",
              "\n",
              "                                             product_name product_brand  \\\n",
              "count                                              487359        487020   \n",
              "unique                                              27247          2754   \n",
              "top     Wrangler Men's Premium Performance Cowboy Cut ...     Allegra K   \n",
              "freq                                                  147         16392   \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_retail_price product_department  \\\n",
              "count          487394.000000             487394   \n",
              "unique                   NaN                  2   \n",
              "top                      NaN                Men   \n",
              "freq                     NaN             244421   \n",
              "mean               59.772834                NaN   \n",
              "std                65.871768                NaN   \n",
              "min                 0.020000                NaN   \n",
              "25%                24.780001                NaN   \n",
              "50%                39.990002                NaN   \n",
              "75%                69.949997                NaN   \n",
              "max               999.000000                NaN   \n",
              "\n",
              "                             product_sku  product_distribution_center_id  \n",
              "count                             487394                   487394.000000  \n",
              "unique                             29050                             NaN  \n",
              "top     CD79499E36195AC3C3296EB666F9D39E                             NaN  \n",
              "freq                                  55                             NaN  \n",
              "mean                                 NaN                        4.992101  \n",
              "std                                  NaN                        2.895917  \n",
              "min                                  NaN                        1.000000  \n",
              "25%                                  NaN                        2.000000  \n",
              "50%                                  NaN                        5.000000  \n",
              "75%                                  NaN                        8.000000  \n",
              "max                                  NaN                       10.000000  "
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              "      <td>487020</td>\n",
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              "      <td>487394</td>\n",
              "      <td>487394</td>\n",
              "      <td>487394.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>444495</td>\n",
              "      <td>180172</td>\n",
              "      <td>NaN</td>\n",
              "      <td>26</td>\n",
              "      <td>27247</td>\n",
              "      <td>2754</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2</td>\n",
              "      <td>29050</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2021-01-18 09:21:00+00:00</td>\n",
              "      <td>2022-03-10 07:49:24+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Intimates</td>\n",
              "      <td>Wrangler Men's Premium Performance Cowboy Cut ...</td>\n",
              "      <td>Allegra K</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Men</td>\n",
              "      <td>CD79499E36195AC3C3296EB666F9D39E</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>36319</td>\n",
              "      <td>147</td>\n",
              "      <td>16392</td>\n",
              "      <td>NaN</td>\n",
              "      <td>244421</td>\n",
              "      <td>55</td>\n",
              "      <td>NaN</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>243697.500000</td>\n",
              "      <td>15292.257424</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>28.746374</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>59.772834</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>4.992101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>140698.672888</td>\n",
              "      <td>8408.118539</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>30.629290</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>65.871768</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.895917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.008300</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>121849.250000</td>\n",
              "      <td>8047.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>11.394300</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>24.780001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>243697.500000</td>\n",
              "      <td>16032.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>19.944301</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>39.990002</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>365545.750000</td>\n",
              "      <td>22558.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>34.698691</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>69.949997</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>487394.000000</td>\n",
              "      <td>29120.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>557.151002</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
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              "</table>\n",
              "</div>\n",
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              "  </svg>\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "  </style>\n",
              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-66e79988-f35d-46dc-9389-da2b5a95c06a');\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",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Data Numerical"
      ],
      "metadata": {
        "id": "piZ2sFfQwWp7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Numerical \n",
        "numerics = ['int8','int16', 'int32', 'int64', 'float16', 'float32', 'float64']\n",
        "\n",
        "display(inv_items.select_dtypes(include = numerics).columns)\n",
        "print(inv_items.select_dtypes(include = numerics).shape)\n",
        "\n",
        "df_num_inv_items = inv_items.select_dtypes(include = numerics)\n",
        "df_num_inv_items.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 276
        },
        "id": "s6HWiSRFjWH9",
        "outputId": "7cb3db25-57ee-4384-f040-2dc7071ac5a5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['id', 'product_id', 'cost', 'product_retail_price',\n",
              "       'product_distribution_center_id'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(487394, 5)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  product_id    cost  product_retail_price  \\\n",
              "0  90967       16898  13.925                  25.0   \n",
              "1  90968       16898  13.925                  25.0   \n",
              "2  90969       16898  13.925                  25.0   \n",
              "3  90970       16898  13.925                  25.0   \n",
              "4  96715       16898  13.925                  25.0   \n",
              "\n",
              "   product_distribution_center_id  \n",
              "0                               3  \n",
              "1                               3  \n",
              "2                               3  \n",
              "3                               3  \n",
              "4                               3  "
            ],
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              "\n",
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              "\n",
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              "  </style>\n",
              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
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              "          const element = document.querySelector('#df-f2608142-8971-4418-81b3-f832cbdefc6a');\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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              "  "
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_num_inv_items.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lfJr5FJTkcii",
        "outputId": "e4b77a4f-1a79-4958-98c8-bc7245555dae"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  id     product_id           cost  product_retail_price  \\\n",
              "count  487394.000000  487394.000000  487394.000000         487394.000000   \n",
              "mean   243697.500000   15292.257424      28.746374             59.772834   \n",
              "std    140698.672888    8408.118539      30.629290             65.871768   \n",
              "min         1.000000       1.000000       0.008300              0.020000   \n",
              "25%    121849.250000    8047.000000      11.394300             24.780001   \n",
              "50%    243697.500000   16032.000000      19.944301             39.990002   \n",
              "75%    365545.750000   22558.000000      34.698691             69.949997   \n",
              "max    487394.000000   29120.000000     557.151002            999.000000   \n",
              "\n",
              "       product_distribution_center_id  \n",
              "count                   487394.000000  \n",
              "mean                         4.992101  \n",
              "std                          2.895917  \n",
              "min                          1.000000  \n",
              "25%                          2.000000  \n",
              "50%                          5.000000  \n",
              "75%                          8.000000  \n",
              "max                         10.000000  "
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              "      <th>product_id</th>\n",
              "      <th>cost</th>\n",
              "      <th>product_retail_price</th>\n",
              "      <th>product_distribution_center_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>243697.500000</td>\n",
              "      <td>15292.257424</td>\n",
              "      <td>28.746374</td>\n",
              "      <td>59.772834</td>\n",
              "      <td>4.992101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>140698.672888</td>\n",
              "      <td>8408.118539</td>\n",
              "      <td>30.629290</td>\n",
              "      <td>65.871768</td>\n",
              "      <td>2.895917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.008300</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>121849.250000</td>\n",
              "      <td>8047.000000</td>\n",
              "      <td>11.394300</td>\n",
              "      <td>24.780001</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>243697.500000</td>\n",
              "      <td>16032.000000</td>\n",
              "      <td>19.944301</td>\n",
              "      <td>39.990002</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>365545.750000</td>\n",
              "      <td>22558.000000</td>\n",
              "      <td>34.698691</td>\n",
              "      <td>69.949997</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>487394.000000</td>\n",
              "      <td>29120.000000</td>\n",
              "      <td>557.151002</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-91a55702-30c5-4066-9a32-b39c4e6a8199')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      fill: #174EA6;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-91a55702-30c5-4066-9a32-b39c4e6a8199 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-91a55702-30c5-4066-9a32-b39c4e6a8199');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Keterangan:**\n",
        "\n",
        "1. Ada 487.394 baris data, mungkin terdapat duplikasi / missing value\n",
        "2. 25 % = Q1; 50 % = Q2; 75 % = Q3\n",
        "3. Mean ~ 50% (Median), semua kolom tidak ada yang memiliki distribusi simetris. | Mean > 50% (Median), semua kolom memiliki skewed distribution ke kanan / positif. | Mean ~ 50% (Median), tidak ada kolum dgn skewed negatif / ke kiri."
      ],
      "metadata": {
        "id": "ujQhuA7NkvMv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items[inv_nums].describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "v6SkKCySvzL2",
        "outputId": "ee6775ba-2823-4c70-8bd8-6374a9596ab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  id     product_id           cost  product_retail_price  \\\n",
              "count  487394.000000  487394.000000  487394.000000         487394.000000   \n",
              "mean   243697.500000   15292.257424      28.746374             59.772834   \n",
              "std    140698.672888    8408.118539      30.629290             65.871768   \n",
              "min         1.000000       1.000000       0.008300              0.020000   \n",
              "25%    121849.250000    8047.000000      11.394300             24.780001   \n",
              "50%    243697.500000   16032.000000      19.944301             39.990002   \n",
              "75%    365545.750000   22558.000000      34.698691             69.949997   \n",
              "max    487394.000000   29120.000000     557.151002            999.000000   \n",
              "\n",
              "       product_distribution_center_id  \n",
              "count                   487394.000000  \n",
              "mean                         4.992101  \n",
              "std                          2.895917  \n",
              "min                          1.000000  \n",
              "25%                          2.000000  \n",
              "50%                          5.000000  \n",
              "75%                          8.000000  \n",
              "max                         10.000000  "
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>product_id</th>\n",
              "      <th>cost</th>\n",
              "      <th>product_retail_price</th>\n",
              "      <th>product_distribution_center_id</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "      <td>487394.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>243697.500000</td>\n",
              "      <td>15292.257424</td>\n",
              "      <td>28.746374</td>\n",
              "      <td>59.772834</td>\n",
              "      <td>4.992101</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>140698.672888</td>\n",
              "      <td>8408.118539</td>\n",
              "      <td>30.629290</td>\n",
              "      <td>65.871768</td>\n",
              "      <td>2.895917</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.008300</td>\n",
              "      <td>0.020000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>121849.250000</td>\n",
              "      <td>8047.000000</td>\n",
              "      <td>11.394300</td>\n",
              "      <td>24.780001</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>243697.500000</td>\n",
              "      <td>16032.000000</td>\n",
              "      <td>19.944301</td>\n",
              "      <td>39.990002</td>\n",
              "      <td>5.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>365545.750000</td>\n",
              "      <td>22558.000000</td>\n",
              "      <td>34.698691</td>\n",
              "      <td>69.949997</td>\n",
              "      <td>8.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>487394.000000</td>\n",
              "      <td>29120.000000</td>\n",
              "      <td>557.151002</td>\n",
              "      <td>999.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8b3e7be0-0fdc-43e9-a6eb-59e2fc892e6a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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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",
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              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-8b3e7be0-0fdc-43e9-a6eb-59e2fc892e6a 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-8b3e7be0-0fdc-43e9-a6eb-59e2fc892e6a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "##### Data Categorical"
      ],
      "metadata": {
        "id": "K-6OIBX8weBt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Data Categorical\n",
        "display(inv_items.select_dtypes(include = ['object']).columns)\n",
        "print(inv_items.select_dtypes(include = object).shape)\n",
        "\n",
        "df_cat_inv_items = inv_items.select_dtypes(include = ['object'])\n",
        "df_cat_inv_items.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        },
        "id": "5AI6qSZQj_i4",
        "outputId": "fb25214a-8ebe-48d2-b750-67b3a619daca"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "Index(['created_at', 'sold_at', 'product_category', 'product_name',\n",
              "       'product_brand', 'product_department', 'product_sku'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(487394, 7)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  created_at                    sold_at product_category  \\\n",
              "0  2021-07-19 02:20:10+00:00  2021-07-20 14:34:10+00:00      Tops & Tees   \n",
              "1  2020-04-13 07:46:00+00:00                        NaN      Tops & Tees   \n",
              "2  2020-09-02 17:43:00+00:00                        NaN      Tops & Tees   \n",
              "3  2021-08-19 15:17:00+00:00                        NaN      Tops & Tees   \n",
              "4  2020-05-09 11:16:10+00:00  2020-06-23 23:59:10+00:00      Tops & Tees   \n",
              "\n",
              "                            product_name product_brand product_department  \\\n",
              "0  Quiksilver Waterman Men's On The Rise           NaN                Men   \n",
              "1  Quiksilver Waterman Men's On The Rise           NaN                Men   \n",
              "2  Quiksilver Waterman Men's On The Rise           NaN                Men   \n",
              "3  Quiksilver Waterman Men's On The Rise           NaN                Men   \n",
              "4  Quiksilver Waterman Men's On The Rise           NaN                Men   \n",
              "\n",
              "                        product_sku  \n",
              "0  22811EE19846217512507785E74D12CC  \n",
              "1  22811EE19846217512507785E74D12CC  \n",
              "2  22811EE19846217512507785E74D12CC  \n",
              "3  22811EE19846217512507785E74D12CC  \n",
              "4  22811EE19846217512507785E74D12CC  "
            ],
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              "\n",
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              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "        vertical-align: top;\n",
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              "</style>\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>created_at</th>\n",
              "      <th>sold_at</th>\n",
              "      <th>product_category</th>\n",
              "      <th>product_name</th>\n",
              "      <th>product_brand</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>2021-07-19 02:20:10+00:00</td>\n",
              "      <td>2021-07-20 14:34:10+00:00</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Men</td>\n",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2020-04-13 07:46:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Men</td>\n",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2020-09-02 17:43:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Men</td>\n",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2021-08-19 15:17:00+00:00</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Men</td>\n",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2020-05-09 11:16:10+00:00</td>\n",
              "      <td>2020-06-23 23:59:10+00:00</td>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Men</td>\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
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        "df_cat_inv_items.describe()"
      ],
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          "height": 308
        },
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      },
      "execution_count": null,
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        {
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            "text/plain": [
              "                       created_at                    sold_at product_category  \\\n",
              "count                      487394                     180508           487394   \n",
              "unique                     444495                     180172               26   \n",
              "top     2021-01-18 09:21:00+00:00  2022-03-10 07:49:24+00:00        Intimates   \n",
              "freq                            5                          3            36319   \n",
              "\n",
              "                                             product_name product_brand  \\\n",
              "count                                              487359        487020   \n",
              "unique                                              27247          2754   \n",
              "top     Wrangler Men's Premium Performance Cowboy Cut ...     Allegra K   \n",
              "freq                                                  147         16392   \n",
              "\n",
              "       product_department                       product_sku  \n",
              "count              487394                            487394  \n",
              "unique                  2                             29050  \n",
              "top                   Men  CD79499E36195AC3C3296EB666F9D39E  \n",
              "freq               244421                                55  "
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "rows = []\n",
        "for col in df_cat_inv_items :\n",
        "  rows.append(\n",
        "    {\n",
        "      'Nama Kolom': col,\n",
        "      'Total Unique Values': len(inv_items[col].unique()),\n",
        "      'Unique Values':  inv_items[col].unique()\n",
        "    }\n",
        "  )\n",
        "# pd.options.display.max_colwidth = 100 #maksimal tampil 100 karakter\n",
        "# maksimal tampil 70 karakter\n",
        "pd.options.display.max_colwidth = 70 \n",
        "\n",
        "# pd.options.display.max_colwidth \n",
        "# tanpa batas \n",
        "unik_inv_items = pd.DataFrame(rows)\n",
        "unik_inv_items.sort_values(by='Total Unique Values', ascending=False).reset_index().drop('index', 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "n-yQ5Ho_kv_W",
        "outputId": "caa91666-a410-4ac3-9e5b-5c6a3a8d5f36"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:17: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Nama Kolom  Total Unique Values  \\\n",
              "0          created_at               444495   \n",
              "1             sold_at               180173   \n",
              "2         product_sku                29050   \n",
              "3        product_name                27248   \n",
              "4       product_brand                 2755   \n",
              "5    product_category                   26   \n",
              "6  product_department                    2   \n",
              "\n",
              "                                                           Unique Values  \n",
              "0  [2021-07-19 02:20:10+00:00, 2020-04-13 07:46:00+00:00, 2020-09-02 ...  \n",
              "1  [2021-07-20 14:34:10+00:00, nan, 2020-06-23 23:59:10+00:00, 2021-0...  \n",
              "2  [22811EE19846217512507785E74D12CC, C5A3C867A3DFB776552DCFC804D4937...  \n",
              "3  [Quiksilver Waterman Men's On The Rise, KEEN Women Bellingham Low ...  \n",
              "4  [nan, H, BB, BG, CB, DC, GM, I5, JM, Ks, L1, MG, MR, MW, QT, SY, S...  \n",
              "5  [Tops & Tees, Socks & Hosiery, Accessories, Socks, Fashion Hoodies...  \n",
              "6                                                           [Men, Women]  "
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              "      <th>1</th>\n",
              "      <td>sold_at</td>\n",
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              "      <th>2</th>\n",
              "      <td>product_sku</td>\n",
              "      <td>29050</td>\n",
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              "      <th>3</th>\n",
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              "      <td>[Quiksilver Waterman Men's On The Rise, KEEN Women Bellingham Low ...</td>\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-a76132c6-da10-4033-81f2-9da6d94c2521');\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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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "created_at = inv_items['created_at'].value_counts().reset_index()\n",
        "created_at.columns = ['created_at', 'FREQ']\n",
        "created_at['PERCENTAGE'] = round((created_at['FREQ']/created_at['FREQ'].sum())*100,2)\n",
        "created_at = created_at[0:5]\n",
        "created_at.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "1m3Li4qMDZ7n",
        "outputId": "a6834f44-be08-4d7a-ab77-a8e48422677b"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  created_at  FREQ  PERCENTAGE\n",
              "0  2021-01-18 09:21:00+00:00     5         0.0\n",
              "1  2020-03-06 14:57:00+00:00     5         0.0\n",
              "2  2022-03-20 14:50:00+00:00     5         0.0\n",
              "3  2021-08-19 13:30:00+00:00     5         0.0\n",
              "4  2022-04-10 18:04:00+00:00     5         0.0"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sold_at = inv_items['sold_at'].value_counts().reset_index()\n",
        "sold_at.columns = ['sold_at', 'FREQ']\n",
        "sold_at['PERCENTAGE'] = round((sold_at['FREQ']/sold_at['FREQ'].sum())*100,2)\n",
        "sold_at = sold_at[0:5]\n",
        "sold_at.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "AzoL4O-ADaGM",
        "outputId": "298b092b-4916-4b0a-f9c6-b91d202b0bc4"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     sold_at  FREQ  PERCENTAGE\n",
              "0  2022-03-10 07:49:24+00:00     3         0.0\n",
              "1  2021-09-03 00:28:46+00:00     2         0.0\n",
              "2  2022-04-18 07:59:47+00:00     2         0.0\n",
              "3  2020-12-26 01:47:37+00:00     2         0.0\n",
              "4  2022-03-16 15:58:05+00:00     2         0.0"
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              "          const element = document.querySelector('#df-fd0a6d4b-a94b-45e8-b81e-72c2557156e5');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_sku = inv_items['product_sku'].value_counts().reset_index()\n",
        "product_sku.columns = ['product_sku', 'FREQ']\n",
        "product_sku['PERCENTAGE'] = round((product_sku['FREQ']/product_sku['FREQ'].sum())*100,2)\n",
        "product_sku = product_sku[0:5]\n",
        "product_sku.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OJyCEa6ZEB7l",
        "outputId": "28ad161b-00f6-44c1-aca9-5972b8b0e0f2"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        product_sku  FREQ  PERCENTAGE\n",
              "0  CD79499E36195AC3C3296EB666F9D39E    55        0.01\n",
              "1  2328FB211593BF76F1E22FF0554CB420    55        0.01\n",
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        "product_name['PERCENTAGE'] = round((product_name['FREQ']/product_name['FREQ'].sum())*100,2)\n",
        "product_name = product_name[0:5]\n",
        "product_name.head(10)"
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            "text/plain": [
              "                                        product_name  FREQ  PERCENTAGE\n",
              "0  Wrangler Men's Premium Performance Cowboy Cut ...   147        0.03\n",
              "1  7 For All Mankind Men's Standard Classic Strai...   144        0.03\n",
              "2        Wrangler Men's Rugged Wear Classic Fit Jean    98        0.02\n",
              "3                 Thorlo Unisex Experia Running Sock    95        0.02\n",
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        "product_brand = inv_items['product_brand'].value_counts().reset_index()\n",
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      "execution_count": 67,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "  product_brand   FREQ  PERCENTAGE\n",
              "0     Allegra K  16392        3.37\n",
              "1  Calvin Klein   8607        1.77\n",
              "2      Carhartt   6819        1.40\n",
              "3         Hanes   5227        1.07\n",
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        "product_category.columns = ['product_category', 'FREQ']\n",
        "product_category['PERCENTAGE'] = round((product_category['FREQ']/product_category['FREQ'].sum())*100,2)\n",
        "product_category = product_category[0:5]\n",
        "product_category.head(10)"
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      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                product_category   FREQ  PERCENTAGE\n",
              "0                      Intimates  36319        7.45\n",
              "1                          Jeans  34319        7.04\n",
              "2  Fashion Hoodies & Sweatshirts  31926        6.55\n",
              "3                    Tops & Tees  31842        6.53\n",
              "4                           Swim  30228        6.20"
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              "      <td>36319</td>\n",
              "      <td>7.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Jeans</td>\n",
              "      <td>34319</td>\n",
              "      <td>7.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fashion Hoodies &amp; Sweatshirts</td>\n",
              "      <td>31926</td>\n",
              "      <td>6.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Tops &amp; Tees</td>\n",
              "      <td>31842</td>\n",
              "      <td>6.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Swim</td>\n",
              "      <td>30228</td>\n",
              "      <td>6.20</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_department = inv_items['product_department'].value_counts().reset_index()\n",
        "product_department.columns = ['product_department', 'FREQ']\n",
        "product_department['PERCENTAGE'] = round((product_department['FREQ']/product_department['FREQ'].sum())*100,2)\n",
        "product_department = product_department[0:5]\n",
        "product_department.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "sY0mum9HExRy",
        "outputId": "9d788376-ed71-4bf4-b8a9-4b1b45f28cd0"
      },
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  product_department    FREQ  PERCENTAGE\n",
              "0                Men  244421       50.15\n",
              "1              Women  242973       49.85"
            ],
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              "\n",
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              "      <th>1</th>\n",
              "      <td>Women</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "inv_items[inv_cats].describe().transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "IEnFs7cdvjPZ",
        "outputId": "2cb05759-601d-41ce-f38e-f5a744516dfe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     count  unique  \\\n",
              "created_at          487394  444495   \n",
              "sold_at             180508  180172   \n",
              "product_category    487394      26   \n",
              "product_name        487359   27247   \n",
              "product_brand       487020    2754   \n",
              "product_department  487394       2   \n",
              "product_sku         487394   29050   \n",
              "\n",
              "                                                                  top    freq  \n",
              "created_at                                  2021-01-18 09:21:00+00:00       5  \n",
              "sold_at                                     2022-03-10 07:49:24+00:00       3  \n",
              "product_category                                            Intimates   36319  \n",
              "product_name        Wrangler Men's Premium Performance Cowboy Cut ...     147  \n",
              "product_brand                                               Allegra K   16392  \n",
              "product_department                                                Men  244421  \n",
              "product_sku                          CD79499E36195AC3C3296EB666F9D39E      55  "
            ],
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              "      <th>freq</th>\n",
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              "      <td>5</td>\n",
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              "      <th>sold_at</th>\n",
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              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>product_name</th>\n",
              "      <td>487359</td>\n",
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              "      <td>Wrangler Men's Premium Performance Cowboy Cut ...</td>\n",
              "      <td>147</td>\n",
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              "      <th>product_brand</th>\n",
              "      <td>487020</td>\n",
              "      <td>2754</td>\n",
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              "    <tr>\n",
              "      <th>product_department</th>\n",
              "      <td>487394</td>\n",
              "      <td>2</td>\n",
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              "      <th>product_sku</th>\n",
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              "      <td>29050</td>\n",
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              "\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",
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              "      fill: #FFFFFF;\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7e0335d9-797e-45c4-9ed1-285c67d51058 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-7e0335d9-797e-45c4-9ed1-285c67d51058');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Beberapa pengamatan dari *statistical summary* kolom data categorical di atas:\n",
        "* Kolom `created_at` memiliki jumlah nilai unik terbanyak, ada kemungkinan tidak terlalu berpengaruh untuk menjadi data predictor."
      ],
      "metadata": {
        "id": "eIYbMKnWyQhn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Jumlah baris cost yang negative\n",
        "inv_items[inv_items['cost']<0].shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9W7q7gQFxxsD",
        "outputId": "e502ac94-abae-4955-b53c-13325354a4b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Jumlah baris product_distribution_center_id yang negative\n",
        "inv_items[inv_items['product_distribution_center_id']<0].shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sq4YkRfx7x-",
        "outputId": "bae294d9-f927-48e5-e807-fcf639e7778d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek missing value\n",
        "inv_items.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1tlN9eTRv1KH",
        "outputId": "a7f8e9f6-64fa-441e-c9eb-ca53c76388fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                     0\n",
              "product_id                             0\n",
              "created_at                             0\n",
              "sold_at                           306886\n",
              "cost                                   0\n",
              "product_category                       0\n",
              "product_name                          35\n",
              "product_brand                        374\n",
              "product_retail_price                   0\n",
              "product_department                     0\n",
              "product_sku                            0\n",
              "product_distribution_center_id         0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Table products"
      ],
      "metadata": {
        "id": "p9M9D34cqwwN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Fungsi (include='all'):**\n",
        "By providing the include argument and assigning it the value ‘all’ we get the summary of the categorical variables too. For the categorical variables, we get the characteristics: count (number of non-missing values) , unique (number of unique values), top (the most frequent value), and the frequency of the most frequent value."
      ],
      "metadata": {
        "id": "8S2Jgjo8Lah9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "products.describe().transpose()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "AAmSjT05KVlM",
        "outputId": "aee7dbc3-bd3f-4803-ea40-5c3a6b78a7dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "                  id          cost   category  \\\n",
              "count   29120.000000  29120.000000      29120   \n",
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              "\n",
              "                                                     name      brand  \\\n",
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              "        retail_price department                               sku  \\\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a47738e3-71c9-4b00-9e8d-bcbc9685fc3c 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-a47738e3-71c9-4b00-9e8d-bcbc9685fc3c');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Univariate Analysis"
      ],
      "metadata": {
        "id": "jyt993enkv1N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualisasi Boxplot untuk analisa outlier\n",
        "for i in range(0, len(inv_nums)):\n",
        "    plt.subplot(1, len(inv_nums), i+1)\n",
        "    sns.boxplot(y=inv_items[inv_nums[i]], color='grey', orient='v')\n",
        "    plt.tight_layout()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "LejcwvZLzoBZ",
        "outputId": "069e6565-cfa5-431a-9d15-5e3430355506"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 5 Axes>"
            ],
            "image/png": 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m9nI/9rVNkIqZ/bL7dt2DTlasWMHixYsZNGjQNvt84403AFi5cmXJY27atKnHfHkB5Wqi0g8XKv94e/PDda4WRo8ezaJFi3jf+96Xd1bKkvQ5M/uxpHO6pQOQnry0D/vcJkglOU56u1JBJ4MGDWLfffft89+xZMmSHrfxAsrVTC1/uC7w+aCytWjRIgAWLlyYc04qekd83znDfW4NUgGQdAfwEeDHFb9VYx4k4VyB+XxQ2TnvvPO6LP/rv/5rTjmpzMyui+//XuqVbCfp/D7stlSQyuIevlNzXkA5V2DHH398xWXXe0ntKdHgtaje6PUzOjN7hDAix6PAQkLZMLVG+eo1b+JzrsBee+21isuupfVp6KcyQSq58gIqpdYhk845V0eF7xfmBVRKrUMmnXOujgo/eK4XUN145Jkrkh133LFLs96OO+Y+Oo2rA0kDgDPN7IoKm/2sXvmpFQ+ScK7Attuu63/hAQMG5JQTV09m9iZwYg/bTKlTdmrGCyjnCmzjxo1dll999dWcclJ83Yc2KsBQR7+V9J+S/kHSB5NX3pnKkjfxOedcMR0Q3y9MpRlhksem4AWUc/0UnwfMB1ab2ccltQEzCRPoLQA+b2avSxoI3AwcRJga4gQzW5lTtl0JBag1bWVmH8s7D7XmTXzO9d9ZdO11fylwhZntQxhh+rSYfhrwYky/Im7nXFUkDZV0g6S74/JoSaf19L0i8QLKuX6QNBz4J+D6uCxCE8ttcZMZwCfj5wlxmbj+SCUjfDrXdzcB9wJ7xuX/B5ydW25qwJv4nOuf7wPfonPgzj2Al8xsS1xeBewVP+8F/BnAzLZIejlu/9f0DrtPaeDqp2DzQb3TzGYlY+7F39SbeWcqS16Dcq5Kkj4OrI3z8mTGzKaaWbuZtQ8ZMiTLXbvm8qqkPYgjRkgaA1Q9J1Qj8gLKueodCoyXtJIQFHEEcCWwq6SkdWI4sDp+Xg3sDRDX70IIlnANIF17KrXcgM4BOoD3SPotIQDnzHyzlC0voJyrkpmdb2bDzWwkYcrx+83sZOAB4Li42SnAz+PnjrhMXH+/mRV+vDSXm6eB/0WYt+krwHuBP+Sao4x5AVVgkvaW9ICkRZKelnRWTN9d0hxJS+P7bjFdkq6StEzSk+lOfZJOidsvlXRKKv0gSQvjd67yh/q9ci5wjqRlhGdMN8T0G4A9Yvo5wHllvu9cbzxsZlvM7Gkze8rM3gAezjtTWfIgiWLbAnzDzB6VtDOwQNIc4IvAfWZ2iaTzCBfCc4FjgVHxdQhwDXCIpN0Jw+y3E9qzF0jqMLMX4zYTgUeA2cBY4O46/o2FYGYPAg/Gz8uBg0ts8xp9mKOnCCT9LTDKzH4laRCwvZltyDtfzUzS3xACbgZJOpDOQWEHA2/PLWM14AVUgZnZGmBN/LxB0mLCD3cCcHjcbAbhwnluTL85NivNlbSrpGFx2zlm9gJALOTGSnoQGGxmc2P6zYSQaS+gHJImEqINdwfeQ3jedi1hNlZXO8cQbkKHA5en0jcA5ecDKiAvoJqEpJHAgYSaztBYeAE8CwyNn7eGOUdJCHSl9FUl0ksd30OjW8/phJriIwBmtlTSu/LNUvMzsxnADEmfNrPb885PLXkB1QQk7QTcDpxtZuvTj4nMzCTV/EG8mU0lThHd3t7uD/5bw+Y4hBOwNTKxsP/2HR0dResH9QtJJwEjSV3LzezCst8oGA+SKDhJbyMUTreY2R0x+bnYdEd8XxvTt4Y5R0kIdKX04SXSnQP4H0mTCc9CjiLMP3RXznlqJT8nNNtvAV5NvZqG16AKLEbU3QAsNrN0W3QSznwJ24Y5nyFpJiFI4mUzWyPpXmBKEu0HHA2cb2YvSFofOwA+AnwBuLrmf5grivMI4wsuJIQ5zyYO+VRUBag1pQ03s7F5Z6KWvIAqtkOBzwMLJT0e0yYTCqZZceDIZ4Dj47rZwDhgGbAROBUgFkQXAfPidhcmARPA1whjfg0iBEd4gIRLDAKmm9k02Dqq+yDCb8vV3u8kvc/MFuadkVrxAqrAzOwhOkNMu9smkipG751eZl/Tgekl0ucD+/cjm6553Qf8I/BKXB4E/JLQcbSQCvYM6jDgi5JWAJsJ1wIzs/fnm63seAHlnKvWjmaWFE6Y2SuSmqofToM7Nu8M1JoHSTjnqvVqt9FIDgI25ZiffinaWHxm9gwhuOmI+HkjTXZN9xqUc65aZwM/k/QXQvPS3wAn5Jul1iEpGf1lX+BG4G3AjwnPppuCF1DOuaqY2TxJ+xEukABL4nhwrj7+mdA5/1EAM/tLHPKsaXgB5ZzrE0lHmNn9kj7VbdXfSSLVH8/V1uvpjviS3pF3hrLmBZRzBbbTTjvxyitb4xTYeee63ED/L+B+4BMl1hngBVR9zJJ0HWH+sYnAl4BpOecpU15AOVdg6cIJYMOG2g8kbmYXSNoOuNvMZtX8gHVStKGOzOx7cQSP9YRm1v9rZnNyzlammiriwzlXH2b2FvCtvPORpYsuuqjL8pQpU3LKSe9IagN+Y2b/x8y+CTwUB41uGjUroCTtKOn3kp6Ik+n9e0xvk/RInADvVkk7xPSBcXlZXD8yta/zY/oSScek0sfGtGVx3iMqHcM5l6lfSfpmnDhz9+SVd6aqNW/evC7Lc+fOzSknvfYz4K3U8psxrWnUsga1mRCf/wHgAML8QmOAS4ErzGwf4EXCWF7E9xdj+hVxOySNJkyn/V7CZHk/lDQgDqvyA0JntdHAiXFbKhzDOZedEwgjk/waWBBf83PNUWvZ3sxeTxbi56a6Ga9ZAWVB0kD+tvgy4Ajgtpg+gzABHoRReWfEz7cBR8bBUCcAM81ss5mtIIwjd3B8LTOz5fEfZiYwIX6n3DGccxkxs7YSr3fnna8Wsk7S1odmkiYAf80xP5mr6TOoWNN5nDDdwxzgj8BLZrYlbpKeAG/rpHlx/cvAHvR9kr09Khyje/4mSZovaf66dev686c613JiM/45ku6QdLuksyXtmHe+qvWhD32oy/KYMWNyykmv/W9gsqQ/SfoTYdbsSTnnKVM1LaDM7E0zO4Awj9DBwH61PF5fmdlUM2s3s/YhQ4bknR3niuZmQtP71cB/xs8/qnZnknaVdJukP0haLOnD8bnWHElL4/tucVtJuio+Z34yPeRStb797W93WZ48ubFnTzezP5rZGMIjjtFm9hEz+2OyXtIp+eUuG3WJ4jOzl4AHgA8TYvaT8Pb0BHhbJ82L63cBnqfvk+w9X+EYzrns7G9mp5nZA/E1kVBIVetK4B4z2w/4ALCYMOfUfWY2ijB6ehIMdSwwKr4mAdf047hA8cbiS5jZK+lBe1POqntmMlbLKL4hknaNnwcBRxF+cA8Ax8XNuk+ml5T4xwH3x+khOoDPxii/NsIP8veEuYtGxYi9HQiBFB3xO+WO4ZzLzqMx8AkASYdQZZCEpF2AjxIm4MTMXo83tuln092fWd8cn3XPJdyUDqvuz2ha5abiKYxadtQdBsyI0XbbAbPM7BeSFgEzJf0H8BjxBxnffyRpGfACocDBzJ6WNAtYRJja+HQzexNA0hnAvcAAwsRpT8d9nVvmGM657BxEmDTvT3F5BLBE0kL6Pi9RG7AOuFHSBwgRgWcBQ81sTdzmWWBo/FzuGfSaVBqSJhGfy4wYMaIP2WkKlncG+qtmBZSZPUkYyLB7+nLC86ju6a8Bnymzr4uBi0ukzybMEturYzQiSVdT4YdkZmfWMTvO9UXF6cYl7WZmL/ZyX9sDHwS+bmaPSLqSzuY8IJR4ybhzvWVmU4GpAO3t7YW/YPeR16BcvyVNIocSHnbeGpc/Q6g1OteQ4hxEZUl6lFDo9MYqYJWZPRKXbyMUUM9JGmZma2IT3tq4vtwzaNfpt3lnoL+8gMqZmc0AkPRV4LAkPF7StcBv8sybc/3U6zt4M3tW0p8l7WtmS4AjCTdoiwjPkS9h22fWZ0iaCRwCvJxqCmwJkgYCnwZGkrqWm9mF8f2MfHKWHS+gGsduwGDC8zeAnWKac2Xtueee/OUvf9m6PHz48Bxzs42+Nql9HbglBj0tB04lPr+WdBrwDHB83HY2MI7QcX9j3LbV/JzQX3QBYeSepuMFVOO4BHhM0gOEO8+PAt/JNUeu4V177bVdwqF/+MMf5pib/jGzxwkzxHZ3ZIltjTDMUisbbmYVnwMWnY9m3iDM7EZCU8WdhPl0Ppw0/zlXyZ577gk0XO0JmuAhfYP7naT35Z2JWvIaVM4k7Wdmf0j1hE9CZ/eUtKeZPZpX3lwxvP76613ea62nEcvNLGmm3qbm4zJ1GPBFSSsITXyi7+H9W8V+q9cD+xOaZ79kZg9nldlqeAGVv3MI/TQuK7EuGVzXubL++tcwPujatWt72DIzCwi/zVI1JAPeDV0KqkIYNWoUS5cu3bq8334NNTJbKcdmvL9kJI/j4nPAt2e8/z7zAipnZjYpvn+s0naSjmq22TJd/33pS1/qsvzlL3+Z66+/vqbHNLO2mh4gJ5dddlmX53nf/e53c8xNz8zsmdip+R9i0m/M7Ilq9pUayeOLcd+vA/Wpklfgz6CK49K8M+AaT1J7StSjFiVpv/j+wVKvmmegRiZMmFBxudFIOgu4BXhXfP1Y0ter3F16JI/HJF0v6R0ZZbVqXoMqjkI9cF6zZg0bN25kyZIlff7uxo0bWbOm8bu0xKklfg0MJPxfus3MLohjRs4kTP2yAPi8mb0e+63cTBgi6HngBDNbmUvm+6cpm6VDYGD55QZ0GnCImb0KIOlS4GHC6PJ9VW4kjy5DvNd76CivQRVHw/9vaUGZzBpdNOlm6RKvQhZOBSXCNO+JN6n+RrbUSB7b1IbrPUWR16BcTQwbNozNmzez77779vm7S5YsYdiwxh+YOvbFKTdr9EkxfQahP9s1hBG4vxPTbwP+U5KsALfq5UjanzBE19aJCs3s5vxy1FJuBB6RdGdc/iRVDoxdYSSPXHkB1SAkDTSzzRXSVtY/V64ncbT+BcA+wA/ow6zRkpJZows5TbekC4DDCQXUbEJU2UOEZszCkdSlWU9q7FZ1M7tc0oOEcHOAU83ssX7sstRIHrnyAqpxPMy2VeqtaWb2qbrnyPUoTv1yQOxDcicZzBpdoCkijiNMLPiYmZ0qaSjw45zzVLVhw4Z1GTZqr732qrB1fiQNNrP1sT/aSlI3r5J2rza8v8JIHrnxAipnkv6GcGc9SNKBdLYhD6YB+iG0CkmfMbOf9ZRWjpm9FIep2jprdKxFlZo1elW3WaO776soU0RsMrO3JG2RNJgw0vjePX2pUaULJ4BVq1bllJMe/QT4OJ390RIi1Q+tGXiQRP6OAb5HuJBdlnr9CzC5py9Lmi5praSnUmnfkbRa0uPxNS617nxJyyQtkXRMKn1sTFsm6bxUepukR2L6rbH634zO72XaVhnOGl1U8+PfP41wsXyUUOt3NWRmH4/vbWb27tSrzcyapnACr0HlLo63N0PSp83s9ip2cRPwn2zb7n+FmX0vnSBpNGGm4vcCewK/kvR3cfUPCBfYVcA8SR1mtojOiLSZcQqQ0wgP/JuCpGMJo2LvJemq1KrBhBmcK8lk1uiiMrOvxY/XSroHGBwnKnV1IOk+Mzuyp7Qi8wKqcRwUf1wvQZiNFPiGmf1bpS+Z2a8ljezlMSYAM2PgxYp4oUxmHl4WZyImzrEzQdJiykekNYu/ECaNHE+oBSQ2EGqxZWU5a3QRpS+GSX+uZrtANqLY/+7twDvjdSL9WKAxH5xVyQuoxnGsmW1t0jOzF2PTXMUCqoIzJH2BcPH9Rpx6ey9gbmqbdITZn7ulH0KIMCsXkdZFgR7sdxGHhnlC0k/M7A3YenOwdx+mK28prXSBbFBfAc4mtIKkB5NeT2hNaRr+DKpxDIgjDQBbn2kMrLB9JdcA7yF0Hl1D6R7/map3B74amCNpcIyMehSYJumKvDPVoL5CqG3uRzhXC+Lr5xT4AtnR0VFxuVGY2ZVxPMRvxudOyesDZlbY819KxRqUpIqhzWZ2R7bZaWm3APdJujEun0poUuszM3su+SxpGvCLuJhEkSXSEWal0p+nfERas9klhu5+Gbg5Dlnkz1NKMLMrgSslfd3MqhlWpyGlB4pNluREoD8AABgmSURBVBu1kIpejq0kXTRTR+memvg+Ed/fBXwEuD8ufwz4HWFivaaR5/hxZnZpvCAm7fcXmdm91exL0jAzSzLzz0AS4dcB/ETS5YTmgVHA7wlNNKPiGHKrCQ/vTzIzi6HTxxHGlktHpDWb7SUNI0wp/q95Z6Ygpkv6N2CEmU2SNArY18x+0dMXXSY+lPq8I+Ha8SgF7ShdSsUCysxOBZD0S2B0ctGL/5FvqnnuWoyZ3Q3c3ZfvSPopoTf/OyWtAi4ADpd0AKFPxEpCkwxm9rSkWYQhTLYAp8eOpkg6A7gXGABMN7On4yHOpXREWrO5kPD3/9bM5kl6N7C0h++0uumEpr2PxOXVwM/orLG7GjKzLiOXx5D/mTllpyZ6GySxd+qOHOA5oDhPwnspz/HjJG2gs9PdDoRx3V41s8GVvmdmJ5ZILluImNnFwMUl0mcThqvpnl4yIq3ZxA65P0stLwc+nV+OCuE9ZnaCpBMBzGyjGn18oOb2KmHajKbR2wLqPkn3Aj+NyycAv6pNllqTme2cfI7/yScAY/LLUWuRNJwwTcGhMek3wFlm1rDDCTSA12MwjwFIeg9hhHdXB5LuovOmdgDw98Cs/HKUvV4VUGZ2RgyYSGZunGpmd1b6jqteHF3gv+JgnOf1tL3LxI2EIWSSfkqfi2lH5ZajxncBcA+wt6RbCIX7F3PNUWtJd8TfAjzTbDdUve4HFSP2miooopF0i5jcjjBo42s5ZacVDTGzG1PLN0k6O7fcNDhJ2wG7AZ8i1PRFqHEWcmT2IjKz/4ljeR5MqEn9MecsZa6nMPOHzOywbs9HIA5K2NPzEdcnn0h93kIIbmjsOaeby/OSPkdnM/aJlBjI1QVxkNhvmdks4L/zzk8ril0i/i8hulrA1ZIuNLPp+eYsOz1F8R0W33eutJ3rvyRi0uXmS4RnUFcQbsZ+hzdX9eRXkr4J3Ep4QA9AtdM9uD77P8CBZvY8gKQ9CL/b1iigXO1JupoK07mb2Zl1zE4ruxA4JRneKI4o8T1CweVKOyG+n55Ka6rpHhrc84QxIxMbaLJavxdQ+Zsf3w8lzEx6a1z+DA0w5XILeX967D0zeyHOz+XKiMPtlCXpKDObU6/8tApJ58SPywhTvv+ccGMwAWiq0U+8gMpZnG4DSV8FDksGZo1TW/wmz7y1mO0k7datBuX/P/rnUsALqOwlj1z+SNfAiKYb5cX/AzaO3QijQSft9zvFNFcflwEPS0o6636GEh2aXZ94p90aMLN/zzsP9eIFVOO4BHgsjn0n4KOEuZdcHZjZzZLmE+a/AvhUnLDRVa/IswU3LEnfN7Ozu3XU3crMxpf4WiF5AdUgzOxGSXcT5mEy4FwzezbnbLWUWCB5oeQa3Y/i+/cqblUHtR5g2wuoxnIwnaN1GHBXjnlxriJJA+PszOXSVtY/V83PzBZIGgBMMrOT885PLXkB1SAkXUIYPv+WmHSmpA+nZ9l1rsE8DHywXJqZVZxPzlXPzN6U9LeSdjCz1/PKR60H2PYCqnGMAw4ws7cAJM0gTG/hBZRrKHF4nb2AQTEUPz3l+9tzy1jrWQ78VlIHXTtKX55flrLlBVRj2ZXOKL5d8syIcxUcQxhlYzgh+jEpoNbjN1T1lISZb0dn6HlTBaZ4AdU4prBtFJ+PZO4aTuy7N0PSp83s9rzz08IWxXnMtpL0mXIbF9F2tdqxpL0lPSBpkaSnJZ0V03eXNEfS0vi+W0yXpKskLZP0pKQPpvZ1Stx+qaRTUukHSVoYv3NVMllauWM0qjgy9FuEUaHvAG4HPmxmt1b8onP5OijO4gqApN3izMuuPs7vZVph1ayAIozI/Q0zG0248J4uaTShVnCfmY0C7qOzlnAsMCq+JgHXwNYe/RcQwq8PBi5IFTjXABNT3xsb08sdoyHF507fMrM1ZtYRXx5i7hrdsWb2UrIQR+EYl2N+WoKkY+MYnnvFG/PkdRPhuts0alZAxYvto/HzBmAx4cHqBGBG3GwG8Mn4eQJwswVzgV0lDSO0d88xsxfif4A5wNi4brCZzY0T/N3cbV+ljtHIfiXpm7HmuXvyyjtTzlUwQNLAZCHOrjuwwvY9kjRA0mOSfhGX2yQ9EltJbpW0Q0wfGJeXxfUj+3PcgvkLYQzP14AFqVcH4XrZNOryDCr+eA4EHgGGmlnSO+tZYGj8vBfw59TXVsW0SumrSqRT4Rjd8zWJUFtjxIgRffyrMncC4QHn17ql+8jQrlHdAtwnKZno8VQ6bwyrdRbhZjaZa+5S4AozmxnHpzyN0HJyGvCime0j6bNxuxNK7bDZmNkTwBOSfmJmb0BoXgX2Tg943Axq2cQHgKSdCM9Uzjaz9el1seZT06iTSscws6lm1m5m7UOGDKllNnpjNPAD4AngccLcRO/NNUfOVWBmlxLGK/z7+LrIzL5b7f4kDQf+Cbg+Losw9NRtcZPuLS5JYXgbcGTyDLqFzJE0OLa0PApMk3RF3pnKUk1rUJLeRiicbolTxgM8J2mYma2JzXRrY/pqYO/U14fHtNXA4d3SH4zpw0tsX+kYjWwGIUz3qrh8Ukw7PrccOdcDM7sbuDuj3X0f+BadIdN7AC8lI/zTtZVka8uKmW2R9HLcvpWmnN/FzNbHmXVvNrMLJDXVdBu1jOITcAOwuFvHsQ4gicQ7hc4h4juAL8RovjHAy7GZ7l7g6BghtBtwNHBvXLde0ph4rC9021epYzSy/c3sy2b2QHxNBPbPO1POlSNpg6T18fWapDclre/5myX39XFgrZktyDiPkyTNlzR/3bp1We66EWwfb8CPB36Rd2ZqoZY1qEOBzwMLJT0e0yYTRu2eJek04Bk6awizCRFAy4CNhPbsZOK4i4B5cbsLU1NKfw24CRhEuItL7uTKHaORPSppTAwQQdIhdE5m6FzDMbOkppPckE4gROxW41BgvKRxwI6EZ1BXEoKlto+1qHQrSdLiskrS9oSO7dvMJmtmU4GpAO3t7U3ViZUwC/S9wENmNk/Su4GlOecpUzUroMzsIcrPB3Nkie2NrlNHp9dNB6aXSJ9PiVqGmT1f6hgN7iDgd5L+FJdHAEskLSScnvfnlzXnKov/f/9L0gVU0a3DzM4n9uGRdDjwTTM7WWF+ruOAmWzb4nIKYey/44D7Yx5aRuyk+7PU8nLg0/nlKHs+kkTjGNvzJs41DknpwWC3A9oJoc9ZOheYGTsAP0Z4bEB8/5GkZYThwT6b8XEblqRvmdl3Y1+oUvNBnZlDtmrCC6gGYWbP5J0H5/roE6nPWwjTa0zo707N7EFCIFRSKzi4xDavEWY9bkWL43vTPwLwAso5VxUzOzXvPLQiM7srvve3z1nD8wLKuSpJ2pswgslQQlPLVDO7MvZLuRUYSahVHG9mL8ZAgisJwUAbgS8mo60USbmmpUQzNTE1IpWZ6j3RTFO+17yjrnNNLJPxJgtoPmFonR0JkxMuja8DgB1yzFer+B5hmpMVwCZgWny9Qph+o2l4Dcq5KsW+eGvi5w2S0uNNHh43m0F4nnIuqfEmgbmSdk06lNc77/2RNC1J+ipwWNKRNg5F9Js889YKzOx/ACRdZmbtqVV3SWqq51Jeg3IuA/0cb7KodqNzzDyAnWKaq493xL5PQBhYF3hHjvnJnBdQBSdpuqS1kp5KpdV8zi3XKevxJgs0+sElhEk2b5I0gzAe3JSc89RK/gV4UNKDkv4HeIAw2G7T8AKq+G5i2z5U9Zhzy1F5vMm4vjfjTXbRYIMYl2VmNxJ+M3fSOclm00eWNQozu4fwf/Is4ExgXzP7ZbJe0lF55S0rXkAVnJn9mtBRMa0ec261vAzHmyyyg4F/AD4KfCjnvLQcM9tsZk/E1+Zuqy/t6/7UbT6uvHmQRHOqx5xbXaix5taql0zGmywqSZcQCqVbYtKZkj5sZpNzzJbrVE1zfPf5uHLlBVSTMzOTVPMxypp8UM6SshxvsqDGAQeY2VsA8TnUY4RC2uWvr88+k/m4LgbOqUmO+sib+JpTX5+BVEovN+eWcwC7pj7vklsuXBaS+bjeyjsjCS+gmlM95txybgpdo/gWEO6+XR1IGthD2so+7KtX83HVO8LUm/gKTtJPCZ1C3ylpFSEarx5zbrkWJmk7wp32GDqDI841s2fzy1XLeZgwkkfJNDP71DbfKG+b+bgk/djMPpfeqN5N+V5AFZyZnVhmVU3n3HKtzczeitM+zCLUzF2dSPobQrDSIEkH0vkcdDDw9mr2WWY+rs9V/FIdeAHlnKvWryR9kzAw7qtJYqr27WrjGOCLhGfCl9FZQK2nyQJUvIByzlXrBEKk2Ne6pb+7xLYuI7Ez9AxJnzaz22uw/weJ83HlzYMknHPVGg38AHgCeBy4GnhvrjlqLQdJ2hpFGYOc/iPPDGXNCyjnXLVmAH8PXEUonEbTOYKJq71jzeylZCGOAjMux/xkzpv4nHPV2j/OhZV4QNKi3HLTegZIGpgMcSRpELBN6HmReQHlnKvWo5LGxHEdkXQIYTJDVx+3APdJujEun0qT1WC9gHLOVesg4HeS/hSXRwBLJC0k9Gp4f35Za35mdqmkJ+nsUnKRmd2bZ56y5gWUc65aPvVKzsysqTvPewHlnKuKmT2Tdx5amaQNdA4IuwPwNuBVM2uIkciz4AWUq5lNmzaxZMmSkus2bw5T1wwcuO0z3U2bNtU0X841AzPbOfkcx8qcQBh6qml4AeVqoq2treL65cuXAzBy5Miqvu+c6xSHMfsvSRfQOYN24XkB5Wpi4sSJFddPnhxGZJkyZUo9suNc05GUHgx2O6AdeC2n7NSEF1DOOVdMn0h93kKYXmNCPlmpDS+gnHOugMzs1LzzUGteQDnnXIFIupoK07mb2Zl1zE5N+Vh8zjlXLPMJsxfvSJiccGl8HUAIN28aXoPqplxodKWw6OR7zjlXa3G6DSR9FTjMzLbE5WuB3+SZt6x5AZVSKbS5p7Donr7vnHMZ240wi24yQeROMa1peAGVUik02sOinXMN5hLgMUkPEGbV/SjwnVxzlDEvoJxzroDM7EZJdwOHEIImzjWzZ3POVqa8gHLOueI6GPiH+NmAu3LMS+Y8is855wpI0iXAWcCi+DpTUlM9g/AalHPOFdM44AAzewtA0gzgMWByrrnKkNegnHOuuHZNfd4lt1zUSM0KKEnTJa2V9FQqbXdJcyQtje+7xXRJukrSMklPSvpg6junxO2XSjollX6QpIXxO1fF4ebLHsM555rMFEIU302x9rQAuDjnPGWqljWom9h2xs3zgPvMbBRwH53Dwh8LjIqvScA1EAob4AJClMrBwAWpAucaYGLqe2N7OIZzrkFJ2lvSA5IWSXpa0lkxvc83ta1A0nbAW4T5n+4Abgc+bGa35pqxjNWsgDKzX9PZgSwxAZgRP88APplKv9mCucCukoYBxwBzzOwFM3sRmAOMjesGm9ncOA/Kzd32VeoYzrnGtQX4hpmNJlx0T5c0mj7e1LaK+NzpW2a2xsw64qupQsyh/s+ghprZmvj5WWBo/LwX8OfUdqtiWqX0VSXSKx1jG5ImSZovaf66deuq+HOcc1mIF9pH4+cNwGLC/+m+3tS2kl9J+masfe6evPLOVJZyi+IzM5NUdkTeehzDzKYCUwHa29trmhfnXO9IGgkcCDxC329q19A6TiD0ffpat/R355CXmqh3Deq55C4nvq+N6auBvVPbDY9pldKHl0ivdAznXIOTtBPhecrZZrY+vS425/fpRrLJW0lGAz8AngAeB64G3ptrjjJW7wKqA0gi8U4Bfp5K/0J88DkGeDneNd0LHC1pt/hw9Gjg3rhuvaQxMXrvC932VeoYzmUqq0hVF0h6G6FwusXM7ojJfb2p7cLMpppZu5m1DxkypHaZz8cM4O+BqwiF02g6m0ObQi3DzH8KPAzsK2mVpNMIgxseJWkp8I9xGWA2sBxYBkwjVlnN7AXgImBefF0Y04jbXB+/80fg7phe7hjOZe0m+hmp6oJ4o3kDsNjMLk+t6utNbSvZ38y+bGYPxNdEYP+8M5Wlmj2DMrMTy6w6ssS2BpxeZj/Tgekl0udT4h/DzJ4vdYxWJGklsAF4E9hiZu3xIeqtwEhgJXC8mb0YLxBXEnqnbwS+mDy0jv3P/i3u9j+S+WhanZn9Oj4vSZsAHB4/zwAeBM4l9VAfmCtpV0nDWvCiWs6hwOeBhZIej2mTCTeYs+IN7jPA8XHdbMJvdRnh99r005+X8KikMTFIBEmHECYzbBo+1FHz+5iZ/TW1nNzhXyLpvLh8Ll3v8A8h3OEfkuqL1k5o/18gqSOG/btt9fuhvqRJhFoWI0aMqF1OG4iZPUSYMqKUPt3UtpCDgN9J+lNcHgEskbSQcIren1/WsuEFVOvp0x1+3HZO0rQqaQ6hWeun9c128VQbqerRpa6XujcvNx0voJqbAb+MF8nr4oUvq75oXbTiXX8ZzyVNd9U81Heut8zsmbzzUGs+WGxzO8zMPkhovjtd0kfTK6sJ2y2nyaOl+sIf6juXES+gmpiZrY7va4E7CeMZZtUXreVlEanqnCvPm/ialKR3ANuZ2Yb4+WjgQjrv8C9h2zv8MyTNJARJvBybqe4FpqQG6T0aOL+Of0rDyipS1TlXmhdQzWsocGeIHmd74Cdmdo+kefQhbNfMXpCU9EWDrn3RnHOuZryAalJmthz4QIn0kv3EqumL5pxzteTPoJxzzjUkr0E551rWtGnTWLFiRdn1kydPpq2tjYkTJ9YxVy7hNSjnnHMNyWtQzrmWla4ZjR8/fpv1U6ZMqWd2XDdeQDlXMN4s5VqFN/E55xzQ0dFRcdnVn9egnCsYb5ZyWZO0N3Azof+kAVPN7Mp8c+UFlHPObbX//mGKuRYs5LcA3zCzRyXtTJhWZ46ZLcozU15AOVdgHR0dXWpR3izlqhEHLl4TP2+QtJgwa0GPBdSmTZtYsmTJNumbN28GYODAgWW/1xMvoJxzzm0VZ4k+EHikxLou0+q0tbWV3c/y5csBGDlyZNltKn0fvIByrvBauFnKZUzSTsDtwNlmtr77+u6TaVaKFJ08eTLQv9+lR/E555xD0tsIhdMtZnZH3vkBL6Ccc67lKUx7cAOw2Mwuzzs/CS+gnHPOHQp8HjhC0uPxNS7vTPkzKOeca3Fm9hCgvPPRndegnHPONSQvoJxzzjUkL6Ccc841JH8G5ZxrGT2NBJ90Lk368JTSU+dSlx0voJxrcP29qPrUG51WrFjB4sWLGTRoUMn1b7zxBgArV64sub43w/O47HgB5VyD689F1S+o2xo0aBD77rtvVd8tNeacqx0voJwrgGovqn5BdUXmQRLOOecakhdQzjnnGpIXUM455xqSP4NyzrWMNWvWsHHjxqqfzW3cuJE1a9ZknCtXjhdQzjW4/lxU/YLqiswLKOdcyxg2bBibN2/uV5j5sGHDMs6VK8cLKOcaXH8uqn5B3damTZvK1kY3b94MwMCBA8t+19WPF1DOFUC1F1W/oHbV0zBFyagcI0eOrHofLjteQDnX4Pp7UfULaqeehnxKhouaMmVKxe0mTZqUWZ5ceV5AOdfgsrqoOlc0TdsPStJYSUskLZN0Xt75KTI/l9ny85kdP5fNrSkLKEkDgB8AxwKjgRMljc43V8Xk5zJbfj6z4+ey+TVrE9/BwDIzWw4gaSYwAVjUl52kpzkoNaVBi0xjkPm5hG3PZ4ucS8jgfPZ0LqFlzqefywZSi3PZlDUoYC/gz6nlVTGtC0mTJM2XNH/dunUVdzho0KCy0x00uczPJfj5TC1vcz79XPaan8sGlsW5bNYaVK+Y2VRgKkB7e7t1X+93Tb3n5zI7fi6z4+eyfmpxLpu1BrUa2Du1PDymub7zc5ktP5/Z8XPZ5Jq1gJoHjJLUJmkH4LNAR855Kio/l9ny85kdP5dNrimb+Mxsi6QzgHuBAcB0M3s652wVkp/LbPn5zI6fy+bXlAUUgJnNBmbnnY9m4OcyW34+s+Pnsrk1axOfc865gvMCyjnnXEPyAso551xD8gLKOedcQ/ICyjnnXEPyAso551xDktk2o3+0JEnrgGd62OydwF97sbu/NbMh/c9VMfXyXELvzqefSz+XmfBzma16XDO9gOoDSfPNrD3vfDQLP5/Z8XOZHT+X2envufQmPueccw3JCyjnnHMNyQuovpmadwaajJ/P7Pi5zI6fy+z061z6MyjnnHMNyWtQzjnnGpIXUM455xqSF1DOOecakhdQzjnnGpIXUM455xrS/wfcGGZnbxCEWQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Hal penting yang harus diperhatikan dari *boxplot* adalah keberadaan *outlier*.\n",
        "- *Outlier* terlihat utamanya pada kolom `cost` dan `product_retail_price`\n",
        "- Dari visualisasi *boxplot*, terlihat mana distribusi yang terlihat agak *skewed* (tidak simetrik) yang ditandai dengan lokasi *box* yang menjauh dari daerah tengah sumbu Y: `cost` dan `product_retail_price`"
      ],
      "metadata": {
        "id": "nymAORxe0M13"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Multivariate Analysis"
      ],
      "metadata": {
        "id": "oVjSKSybVObJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.select_dtypes(['float64', 'int64']).corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 458
        },
        "id": "hBxd7pmaNuYp",
        "outputId": "d3f932eb-4802-4c89-9071-485515d2ac3f"
      },
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      id  product_id  created_at   sold_at  \\\n",
              "id                              1.000000   -0.002393   -0.000028  0.001420   \n",
              "product_id                     -0.002393    1.000000    0.003674  0.002778   \n",
              "created_at                     -0.000028    0.003674    1.000000  0.175701   \n",
              "sold_at                         0.001420    0.002778    0.175701  1.000000   \n",
              "cost                           -0.002823   -0.008774    0.001089  0.000444   \n",
              "product_name                   -0.001890    0.032078    0.003003  0.001895   \n",
              "product_brand                   0.000175    0.038268    0.001679  0.002182   \n",
              "product_retail_price           -0.003294   -0.001736    0.001101  0.000485   \n",
              "product_department              0.001742   -0.864948   -0.002616 -0.002185   \n",
              "product_sku                    -0.003578    0.006710   -0.000936 -0.000426   \n",
              "product_distribution_center_id  0.004094    0.018531   -0.001177 -0.001043   \n",
              "\n",
              "                                    cost  product_name  product_brand  \\\n",
              "id                             -0.002823     -0.001890       0.000175   \n",
              "product_id                     -0.008774      0.032078       0.038268   \n",
              "created_at                      0.001089      0.003003       0.001679   \n",
              "sold_at                         0.000444      0.001895       0.002182   \n",
              "cost                            1.000000      0.042227       0.033585   \n",
              "product_name                    0.042227      1.000000       0.822842   \n",
              "product_brand                   0.033585      0.822842       1.000000   \n",
              "product_retail_price            0.982524      0.040969       0.031826   \n",
              "product_department             -0.061215     -0.021115      -0.023175   \n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(inv_items.select_dtypes(['float64', 'int64']).corr(), annot=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "kT8vEeYnVOyN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "outputId": "069d5ce3-f357-4fb2-aded-d60e51383b5b"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dari *correlation heatmap* di atas dapat dilihat bahwa:\n",
        "- Kolom `product_distribution_center_id` memiliki korelasi positif lemah dengan `id`, `product_id`.\n",
        "- Kolom `cost` juga memiliki korelasi positif kuat dengan `product_retail_price`. Ada kemungkinan kedua *features* mengalami redundansi (berlebihan data informasi dalam korelasi).\n",
        "- Beberapa *features* terlihat tidak berkorelasi, sehingga diabaikan saja."
      ],
      "metadata": {
        "id": "QGCrVpb_9sqb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Missing Value"
      ],
      "metadata": {
        "id": "WyDTgPTOFXNQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek Missing Value\n",
        "data_missing_value = inv_items.isnull().sum().reset_index()\n",
        "data_missing_value.columns = ['feature','missing_value']\n",
        "data_missing_value['percentage'] = round((data_missing_value['missing_value']/len(inv_items))*100,2)\n",
        "data_missing_value = data_missing_value.sort_values('percentage', ascending=False).reset_index(drop=True)\n",
        "data_missing_value = data_missing_value[data_missing_value['percentage']>0]\n",
        "data_missing_value"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "upvBU0-cFa62",
        "outputId": "229dfd6f-c22a-4962-f627-40d6a4c283bf"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         feature  missing_value  percentage\n",
              "0        sold_at         306886       62.96\n",
              "1  product_brand            374        0.08\n",
              "2   product_name             35        0.01"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-36b93d7b-7bab-4027-9a4a-92e13c9e7e1f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>missing_value</th>\n",
              "      <th>percentage</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sold_at</td>\n",
              "      <td>306886</td>\n",
              "      <td>62.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>product_brand</td>\n",
              "      <td>374</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>product_name</td>\n",
              "      <td>35</td>\n",
              "      <td>0.01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36b93d7b-7bab-4027-9a4a-92e13c9e7e1f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-36b93d7b-7bab-4027-9a4a-92e13c9e7e1f 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-36b93d7b-7bab-4027-9a4a-92e13c9e7e1f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling Duplicated"
      ],
      "metadata": {
        "id": "2X0bnFbgLoJ4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Cek duplicated data\n",
        "inv_items.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXTQoNUVwTxu",
        "outputId": "f37a4371-ac9f-475b-cf91-cc51a8c97011"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Tidak ada duplicates rows pada dataset"
      ],
      "metadata": {
        "id": "I7f59ufKFt6Q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Duplicated data\n",
        "products.duplicated()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a31Yqs8LwgUX",
        "outputId": "44ab582b-221b-4043-9c55-2b491b100eb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        False\n",
              "1        False\n",
              "2        False\n",
              "3        False\n",
              "4        False\n",
              "         ...  \n",
              "29115    False\n",
              "29116    False\n",
              "29117    False\n",
              "29118    False\n",
              "29119    False\n",
              "Length: 29120, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Label Encoding"
      ],
      "metadata": {
        "id": "32msaio6Fxof"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler, LabelEncoder,OneHotEncoder"
      ],
      "metadata": {
        "id": "J3hxHhslHDln"
      },
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_cat_inv_items.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 308
        },
        "id": "pbUrPdZMF1AK",
        "outputId": "58c4f3e3-3dc1-40f8-851f-df860a7c2c46"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       created_at                    sold_at product_category  \\\n",
              "count                      487394                     180508           487394   \n",
              "unique                     444495                     180172               26   \n",
              "top     2021-01-18 09:21:00+00:00  2022-03-10 07:49:24+00:00        Intimates   \n",
              "freq                            5                          3            36319   \n",
              "\n",
              "                                             product_name product_brand  \\\n",
              "count                                              487359        487020   \n",
              "unique                                              27247          2754   \n",
              "top     Wrangler Men's Premium Performance Cowboy Cut ...     Allegra K   \n",
              "freq                                                  147         16392   \n",
              "\n",
              "       product_department                       product_sku  \n",
              "count              487394                            487394  \n",
              "unique                  2                             29050  \n",
              "top                   Men  CD79499E36195AC3C3296EB666F9D39E  \n",
              "freq               244421                                55  "
            ],
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              "      <th></th>\n",
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              "      <th>product_name</th>\n",
              "      <th>product_brand</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>487394</td>\n",
              "      <td>180508</td>\n",
              "      <td>487394</td>\n",
              "      <td>487359</td>\n",
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              "      <td>487394</td>\n",
              "      <td>487394</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>unique</th>\n",
              "      <td>444495</td>\n",
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              "    <tr>\n",
              "      <th>top</th>\n",
              "      <td>2021-01-18 09:21:00+00:00</td>\n",
              "      <td>2022-03-10 07:49:24+00:00</td>\n",
              "      <td>Intimates</td>\n",
              "      <td>Wrangler Men's Premium Performance Cowboy Cut ...</td>\n",
              "      <td>Allegra K</td>\n",
              "      <td>Men</td>\n",
              "      <td>CD79499E36195AC3C3296EB666F9D39E</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>freq</th>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>36319</td>\n",
              "      <td>147</td>\n",
              "      <td>16392</td>\n",
              "      <td>244421</td>\n",
              "      <td>55</td>\n",
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              "  </tbody>\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\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",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-997ad8be-2ffb-4d31-a9c3-b089a0fd7a57 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-997ad8be-2ffb-4d31-a9c3-b089a0fd7a57');\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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import label encoder\n",
        "from sklearn import preprocessing\n",
        " \n",
        "# Label_encoder object knows how to understand word labels.\n",
        "label_encoder = preprocessing.LabelEncoder()\n",
        " \n",
        "# Encode labels in column 'marital'.\n",
        "inv_items['created_at']= label_encoder.fit_transform(inv_items['created_at'])\n",
        "inv_items['sold_at']= label_encoder.fit_transform(inv_items['sold_at'])\n",
        "inv_items['product_sku']= label_encoder.fit_transform(inv_items['product_sku']) \n",
        "inv_items['product_name']= label_encoder.fit_transform(inv_items['product_name']) \n",
        "inv_items['product_brand']= label_encoder.fit_transform(inv_items['product_brand'])\n",
        "inv_items['product_department']= label_encoder.fit_transform(inv_items['product_department']) "
      ],
      "metadata": {
        "id": "rpVeMa7kHVcp"
      },
      "execution_count": 74,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rows = []\n",
        "for col in inv_items :\n",
        "  rows.append(\n",
        "    {\n",
        "      'Nama Kolom': col,\n",
        "      'Jumlah Unique Values': len(inv_items[col].unique()),\n",
        "      'Unique Values': inv_items[col].unique()\n",
        "    }\n",
        "  )\n",
        "\n",
        "# pd.options.display.max_colwidth = 100 #maksimal tampil 100 karakter\n",
        "# maksimal tampil 70 karakter\n",
        "pd.options.display.max_colwidth = 70 \n",
        "\n",
        "# pd.options.display.max_colwidth \n",
        "# tanpa batas \n",
        "unik_inv_items = pd.DataFrame(rows)\n",
        "unik_inv_items.sort_values(by='Jumlah Unique Values', ascending=True).reset_index().drop('index', 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "id": "HWb_hT_KHrUA",
        "outputId": "ead6c476-c86a-4ff2-ed85-b7b906b57400"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:18: FutureWarning: In a future version of pandas all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        Nama Kolom  Jumlah Unique Values  \\\n",
              "0               product_department                     2   \n",
              "1   product_distribution_center_id                    10   \n",
              "2                 product_category                    26   \n",
              "3                    product_brand                  2755   \n",
              "4             product_retail_price                  4187   \n",
              "5                             cost                 26315   \n",
              "6                     product_name                 27248   \n",
              "7                       product_id                 29050   \n",
              "8                      product_sku                 29050   \n",
              "9                          sold_at                180173   \n",
              "10                      created_at                444495   \n",
              "11                              id                487394   \n",
              "\n",
              "                                                            Unique Values  \n",
              "0                                                                  [0, 1]  \n",
              "1                                         [3, 8, 6, 4, 7, 9, 10, 1, 2, 5]  \n",
              "2   [Tops & Tees, Socks & Hosiery, Accessories, Socks, Fashion Hoodies...  \n",
              "3   [2754, 1057, 197, 207, 423, 644, 974, 1156, 1225, 1377, 1386, 1530...  \n",
              "4   [25.0, 16.0, 24.25, 22.479999542236328, 45.950000762939446, 4.2100...  \n",
              "5   [13.924999965820462, 5.71199995279312, 9.554499962599948, 12.47639...  \n",
              "6   [20400, 13654, 12240, 25488, 5854, 17516, 22524, 12815, 25333, 220...  \n",
              "7   [16898, 9482, 13863, 25135, 1629, 13921, 25187, 10598, 15723, 1138...  \n",
              "8   [3984, 22405, 13787, 1624, 10449, 1499, 7733, 26170, 11792, 8185, ...  \n",
              "9   [69900, 180172, 19300, 74459, 51750, 115794, 54016, 148754, 92504,...  \n",
              "10  [245084, 46770, 101869, 261410, 56545, 330110, 67072, 246908, 6262...  \n",
              "11  [90967, 90968, 90969, 90970, 96715, 96716, 96717, 147918, 147919, ...  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a1133a8a-9c7c-485d-b1cf-592ebb94363a\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Nama Kolom</th>\n",
              "      <th>Jumlah Unique Values</th>\n",
              "      <th>Unique Values</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>product_department</td>\n",
              "      <td>2</td>\n",
              "      <td>[0, 1]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>product_distribution_center_id</td>\n",
              "      <td>10</td>\n",
              "      <td>[3, 8, 6, 4, 7, 9, 10, 1, 2, 5]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>product_category</td>\n",
              "      <td>26</td>\n",
              "      <td>[Tops &amp; Tees, Socks &amp; Hosiery, Accessories, Socks, Fashion Hoodies...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>product_brand</td>\n",
              "      <td>2755</td>\n",
              "      <td>[2754, 1057, 197, 207, 423, 644, 974, 1156, 1225, 1377, 1386, 1530...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>product_retail_price</td>\n",
              "      <td>4187</td>\n",
              "      <td>[25.0, 16.0, 24.25, 22.479999542236328, 45.950000762939446, 4.2100...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>cost</td>\n",
              "      <td>26315</td>\n",
              "      <td>[13.924999965820462, 5.71199995279312, 9.554499962599948, 12.47639...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>product_name</td>\n",
              "      <td>27248</td>\n",
              "      <td>[20400, 13654, 12240, 25488, 5854, 17516, 22524, 12815, 25333, 220...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>product_id</td>\n",
              "      <td>29050</td>\n",
              "      <td>[16898, 9482, 13863, 25135, 1629, 13921, 25187, 10598, 15723, 1138...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>product_sku</td>\n",
              "      <td>29050</td>\n",
              "      <td>[3984, 22405, 13787, 1624, 10449, 1499, 7733, 26170, 11792, 8185, ...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sold_at</td>\n",
              "      <td>180173</td>\n",
              "      <td>[69900, 180172, 19300, 74459, 51750, 115794, 54016, 148754, 92504,...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>created_at</td>\n",
              "      <td>444495</td>\n",
              "      <td>[245084, 46770, 101869, 261410, 56545, 330110, 67072, 246908, 6262...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>id</td>\n",
              "      <td>487394</td>\n",
              "      <td>[90967, 90968, 90969, 90970, 96715, 96716, 96717, 147918, 147919, ...</td>\n",
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              "          document.querySelector('#df-a1133a8a-9c7c-485d-b1cf-592ebb94363a 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-a1133a8a-9c7c-485d-b1cf-592ebb94363a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.head()"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 270
        },
        "id": "Lw3wGyFRJ3Wx",
        "outputId": "6c09f7da-ff58-493c-9b13-729be9d1d403"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  product_id  created_at  sold_at    cost product_category  \\\n",
              "0  90967       16898      245084    69900  13.925      Tops & Tees   \n",
              "1  90968       16898       46770   180172  13.925      Tops & Tees   \n",
              "2  90969       16898      101869   180172  13.925      Tops & Tees   \n",
              "3  90970       16898      261410   180172  13.925      Tops & Tees   \n",
              "4  96715       16898       56545    19300  13.925      Tops & Tees   \n",
              "\n",
              "   product_name  product_brand  product_retail_price  product_department  \\\n",
              "0         20400           2754                  25.0                   0   \n",
              "1         20400           2754                  25.0                   0   \n",
              "2         20400           2754                  25.0                   0   \n",
              "3         20400           2754                  25.0                   0   \n",
              "4         20400           2754                  25.0                   0   \n",
              "\n",
              "   product_sku  product_distribution_center_id  \n",
              "0         3984                               3  \n",
              "1         3984                               3  \n",
              "2         3984                               3  \n",
              "3         3984                               3  \n",
              "4         3984                               3  "
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            ]
          },
          "metadata": {},
          "execution_count": 76
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### One Hot Encoding"
      ],
      "metadata": {
        "id": "273RNBSKJyb-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.info()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "BpDZ7SVqJ0pH",
        "outputId": "1d4379c4-8ae5-4774-a428-8f8a631c9961"
      },
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 487394 entries, 0 to 487393\n",
            "Data columns (total 12 columns):\n",
            " #   Column                          Non-Null Count   Dtype  \n",
            "---  ------                          --------------   -----  \n",
            " 0   id                              487394 non-null  int64  \n",
            " 1   product_id                      487394 non-null  int64  \n",
            " 2   created_at                      487394 non-null  int64  \n",
            " 3   sold_at                         487394 non-null  int64  \n",
            " 4   cost                            487394 non-null  float64\n",
            " 5   product_category                487394 non-null  object \n",
            " 6   product_name                    487394 non-null  int64  \n",
            " 7   product_brand                   487394 non-null  int64  \n",
            " 8   product_retail_price            487394 non-null  float64\n",
            " 9   product_department              487394 non-null  int64  \n",
            " 10  product_sku                     487394 non-null  int64  \n",
            " 11  product_distribution_center_id  487394 non-null  int64  \n",
            "dtypes: float64(2), int64(9), object(1)\n",
            "memory usage: 44.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Lihat sekilas hasil One hot encode column dan definisikan ke one_hot_columns\n",
        "one_hot_columns = pd.get_dummies(inv_items[['product_category']])\n",
        "\n",
        "# kode singkat dari kode di atas\n",
        "# one_hot_columns = pd.get_dummies(object_columns)"
      ],
      "metadata": {
        "id": "wE7q3WsoKDwg"
      },
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# kolom yang berada dalam one_hot_columns ingin di one hot encoding\n",
        "dumies_one_hot_encod = one_hot_columns \n",
        "\n",
        "# bagian one hot encoding \n",
        "inv_items_updated = pd.concat([inv_items, dumies_one_hot_encod], axis = 1)\n",
        "\n",
        "# hapus kolom yang berada di dalam one_hot_columns dari dataframe awal\n",
        "inv_items_updated.drop(['product_category'], axis = 1, inplace = True)\n",
        "\n",
        "inv_items_updated.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "zqPeKl3ZKD84",
        "outputId": "f9b79a96-0543-45f3-e804-9bbf36251dd8"
      },
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  product_id  created_at  sold_at    cost  product_name  \\\n",
              "0  90967       16898      245084    69900  13.925         20400   \n",
              "1  90968       16898       46770   180172  13.925         20400   \n",
              "2  90969       16898      101869   180172  13.925         20400   \n",
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              "4  96715       16898       56545    19300  13.925         20400   \n",
              "\n",
              "   product_brand  product_retail_price  product_department  product_sku  ...  \\\n",
              "0           2754                  25.0                   0         3984  ...   \n",
              "1           2754                  25.0                   0         3984  ...   \n",
              "2           2754                  25.0                   0         3984  ...   \n",
              "3           2754                  25.0                   0         3984  ...   \n",
              "4           2754                  25.0                   0         3984  ...   \n",
              "\n",
              "   product_category_Skirts  product_category_Sleep & Lounge  \\\n",
              "0                        0                                0   \n",
              "1                        0                                0   \n",
              "2                        0                                0   \n",
              "3                        0                                0   \n",
              "4                        0                                0   \n",
              "\n",
              "   product_category_Socks  product_category_Socks & Hosiery  \\\n",
              "0                       0                                 0   \n",
              "1                       0                                 0   \n",
              "2                       0                                 0   \n",
              "3                       0                                 0   \n",
              "4                       0                                 0   \n",
              "\n",
              "   product_category_Suits  product_category_Suits & Sport Coats  \\\n",
              "0                       0                                     0   \n",
              "1                       0                                     0   \n",
              "2                       0                                     0   \n",
              "3                       0                                     0   \n",
              "4                       0                                     0   \n",
              "\n",
              "   product_category_Sweaters  product_category_Swim  \\\n",
              "0                          0                      0   \n",
              "1                          0                      0   \n",
              "2                          0                      0   \n",
              "3                          0                      0   \n",
              "4                          0                      0   \n",
              "\n",
              "   product_category_Tops & Tees  product_category_Underwear  \n",
              "0                             1                           0  \n",
              "1                             1                           0  \n",
              "2                             1                           0  \n",
              "3                             1                           0  \n",
              "4                             1                           0  \n",
              "\n",
              "[5 rows x 37 columns]"
            ],
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>90970</td>\n",
              "      <td>16898</td>\n",
              "      <td>261410</td>\n",
              "      <td>180172</td>\n",
              "      <td>13.925</td>\n",
              "      <td>20400</td>\n",
              "      <td>2754</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3984</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>96715</td>\n",
              "      <td>16898</td>\n",
              "      <td>56545</td>\n",
              "      <td>19300</td>\n",
              "      <td>13.925</td>\n",
              "      <td>20400</td>\n",
              "      <td>2754</td>\n",
              "      <td>25.0</td>\n",
              "      <td>0</td>\n",
              "      <td>3984</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 37 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-cf55ea74-ec51-4ab4-a86c-929b1cffbf5b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-cf55ea74-ec51-4ab4-a86c-929b1cffbf5b 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-cf55ea74-ec51-4ab4-a86c-929b1cffbf5b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items_updated.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oKMsB6R-MJ7k",
        "outputId": "0b5bb04b-95f2-43d6-ba4c-d6cb96ff71c5"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 487394 entries, 0 to 487393\n",
            "Data columns (total 37 columns):\n",
            " #   Column                                          Non-Null Count   Dtype  \n",
            "---  ------                                          --------------   -----  \n",
            " 0   id                                              487394 non-null  int64  \n",
            " 1   product_id                                      487394 non-null  int64  \n",
            " 2   created_at                                      487394 non-null  int64  \n",
            " 3   sold_at                                         487394 non-null  int64  \n",
            " 4   cost                                            487394 non-null  float64\n",
            " 5   product_name                                    487394 non-null  int64  \n",
            " 6   product_brand                                   487394 non-null  int64  \n",
            " 7   product_retail_price                            487394 non-null  float64\n",
            " 8   product_department                              487394 non-null  int64  \n",
            " 9   product_sku                                     487394 non-null  int64  \n",
            " 10  product_distribution_center_id                  487394 non-null  int64  \n",
            " 11  product_category_Accessories                    487394 non-null  uint8  \n",
            " 12  product_category_Active                         487394 non-null  uint8  \n",
            " 13  product_category_Blazers & Jackets              487394 non-null  uint8  \n",
            " 14  product_category_Clothing Sets                  487394 non-null  uint8  \n",
            " 15  product_category_Dresses                        487394 non-null  uint8  \n",
            " 16  product_category_Fashion Hoodies & Sweatshirts  487394 non-null  uint8  \n",
            " 17  product_category_Intimates                      487394 non-null  uint8  \n",
            " 18  product_category_Jeans                          487394 non-null  uint8  \n",
            " 19  product_category_Jumpsuits & Rompers            487394 non-null  uint8  \n",
            " 20  product_category_Leggings                       487394 non-null  uint8  \n",
            " 21  product_category_Maternity                      487394 non-null  uint8  \n",
            " 22  product_category_Outerwear & Coats              487394 non-null  uint8  \n",
            " 23  product_category_Pants                          487394 non-null  uint8  \n",
            " 24  product_category_Pants & Capris                 487394 non-null  uint8  \n",
            " 25  product_category_Plus                           487394 non-null  uint8  \n",
            " 26  product_category_Shorts                         487394 non-null  uint8  \n",
            " 27  product_category_Skirts                         487394 non-null  uint8  \n",
            " 28  product_category_Sleep & Lounge                 487394 non-null  uint8  \n",
            " 29  product_category_Socks                          487394 non-null  uint8  \n",
            " 30  product_category_Socks & Hosiery                487394 non-null  uint8  \n",
            " 31  product_category_Suits                          487394 non-null  uint8  \n",
            " 32  product_category_Suits & Sport Coats            487394 non-null  uint8  \n",
            " 33  product_category_Sweaters                       487394 non-null  uint8  \n",
            " 34  product_category_Swim                           487394 non-null  uint8  \n",
            " 35  product_category_Tops & Tees                    487394 non-null  uint8  \n",
            " 36  product_category_Underwear                      487394 non-null  uint8  \n",
            "dtypes: float64(2), int64(9), uint8(26)\n",
            "memory usage: 53.0 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling Missing Value"
      ],
      "metadata": {
        "id": "_817sVHhM87w"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.isna().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IdyRWrhfNDzT",
        "outputId": "8a6509df-8382-4220-8949-a28da3390e7c"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "product_id                        0\n",
              "created_at                        0\n",
              "sold_at                           0\n",
              "cost                              0\n",
              "product_category                  0\n",
              "product_name                      0\n",
              "product_brand                     0\n",
              "product_retail_price              0\n",
              "product_department                0\n",
              "product_sku                       0\n",
              "product_distribution_center_id    0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.isna().sum()/inv_items.shape[0]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GbfC69qONUcd",
        "outputId": "639e17af-3f9d-4b6a-def1-bb0af9f6661d"
      },
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0.0\n",
              "product_id                        0.0\n",
              "created_at                        0.0\n",
              "sold_at                           0.0\n",
              "cost                              0.0\n",
              "product_category                  0.0\n",
              "product_name                      0.0\n",
              "product_brand                     0.0\n",
              "product_retail_price              0.0\n",
              "product_department                0.0\n",
              "product_sku                       0.0\n",
              "product_distribution_center_id    0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Handling Outlier"
      ],
      "metadata": {
        "id": "OqV4PP-2MEX7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "BDdicIfbNFgu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Splitting"
      ],
      "metadata": {
        "id": "777ATLpjpdAD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = inv_items.iloc[:, :-1].values\n",
        "print(X)"
      ],
      "metadata": {
        "id": "kB30nPjRplOh",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a28c9e6-407f-497a-f421-e8afc71f34fe"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[90967 16898 245084 ... 25.0 0 3984]\n",
            " [90968 16898 46770 ... 25.0 0 3984]\n",
            " [90969 16898 101869 ... 25.0 0 3984]\n",
            " ...\n",
            " [290891 14392 422000 ... 35.720001220703125 1 11141]\n",
            " [301361 14392 213175 ... 35.720001220703125 1 11141]\n",
            " [301362 14392 73299 ... 35.720001220703125 1 11141]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Y = inv_items.iloc[:, :-1].values\n",
        "print(Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZtOD1QTAP300",
        "outputId": "45f25e6e-a255-48f3-b5c8-8cfe73e06b9d"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[90967 16898 245084 ... 25.0 0 3984]\n",
            " [90968 16898 46770 ... 25.0 0 3984]\n",
            " [90969 16898 101869 ... 25.0 0 3984]\n",
            " ...\n",
            " [290891 14392 422000 ... 35.720001220703125 1 11141]\n",
            " [301361 14392 213175 ... 35.720001220703125 1 11141]\n",
            " [301362 14392 73299 ... 35.720001220703125 1 11141]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(inv_items[\"cost\"].min())\n",
        "print(inv_items[\"cost\"].max())\n",
        "print(inv_items[\"cost\"].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qjo_X9j1QHt-",
        "outputId": "243857a4-e1ac-40e6-e312-eb9789c90511"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.0082999997779726\n",
            "557.1510021798313\n",
            "28.74637397598631\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Join Table"
      ],
      "metadata": {
        "id": "e7R_hfuLs6S5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fungsi Inner Join\n",
        "inner_join = pd.merge(inv_items, products, on ='id', how ='inner')"
      ],
      "metadata": {
        "id": "JPOVQFmE0TeT"
      },
      "execution_count": 91,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join.info()"
      ],
      "metadata": {
        "id": "TkWaA_Uan2J-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "52fab2b9-9705-46de-a480-37dc5018da8f"
      },
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 29120 entries, 0 to 29119\n",
            "Data columns (total 20 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   id                              29120 non-null  int64  \n",
            " 1   product_id                      29120 non-null  int64  \n",
            " 2   created_at                      29120 non-null  int64  \n",
            " 3   sold_at                         29120 non-null  int64  \n",
            " 4   cost_x                          29120 non-null  float64\n",
            " 5   product_category                29120 non-null  object \n",
            " 6   product_name                    29120 non-null  int64  \n",
            " 7   product_brand                   29120 non-null  int64  \n",
            " 8   product_retail_price            29120 non-null  float64\n",
            " 9   product_department              29120 non-null  int64  \n",
            " 10  product_sku                     29120 non-null  int64  \n",
            " 11  product_distribution_center_id  29120 non-null  int64  \n",
            " 12  cost_y                          29120 non-null  float64\n",
            " 13  category                        29120 non-null  object \n",
            " 14  name                            29118 non-null  object \n",
            " 15  brand                           29096 non-null  object \n",
            " 16  retail_price                    29120 non-null  float64\n",
            " 17  department                      29120 non-null  object \n",
            " 18  sku                             29120 non-null  object \n",
            " 19  distribution_center_id          29120 non-null  int64  \n",
            "dtypes: float64(4), int64(10), object(6)\n",
            "memory usage: 4.7+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modelling"
      ],
      "metadata": {
        "id": "iviLuqrCoUHi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "6PX5j1rLoW-P"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}