(Baru) Department_Product.ipynb

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      "name": "(Baru) Department Product.ipynb",
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        "<a href=\"https://colab.research.google.com/github/NabilahSharfina/Ruangguru-Bootcamp/blob/main/(Baru)%20Department_Product.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load data from google drive\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DDWEYInqvpn3",
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      "outputs": [
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          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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    {
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      "execution_count": 42,
      "metadata": {
        "id": "3mlqgGzGvcYm"
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      "source": [
        "import numpy as np # for performing mathematical calculations behind ML algorithms\n",
        "import matplotlib.pyplot as plt # for visualization\n",
        "import pandas as pd # for handling and cleaning the dataset\n",
        "import seaborn as sns # for visualization\n",
        "import sklearn # for model evaluation and development"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# READING DATA "
      ],
      "metadata": {
        "id": "_Pd6jTSbOudr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataframe Tabel inventory_items\n",
        "inv_items = pd.read_csv('/content/drive/MyDrive/KAMPUS MERDEKA BATCH 2/Project Ruang Guru Kel 255/Dataset Final Project DBA/inventory_items.csv')\n",
        "inv_items.head()"
      ],
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        "outputId": "27fadc3c-cd8d-4433-a488-99a95cf175c2"
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      "execution_count": 63,
      "outputs": [
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              "      id  product_id                 created_at                    sold_at  \\\n",
              "0  90967       16898  2021-07-19 02:20:10+00:00  2021-07-20 14:34:10+00:00   \n",
              "1  90968       16898  2020-04-13 07:46:00+00:00                        NaN   \n",
              "2  90969       16898  2020-09-02 17:43:00+00:00                        NaN   \n",
              "3  90970       16898  2021-08-19 15:17:00+00:00                        NaN   \n",
              "4  96715       16898  2020-05-09 11:16:10+00:00  2020-06-23 23:59:10+00:00   \n",
              "\n",
              "     cost product_category                           product_name  \\\n",
              "0  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
              "1  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
              "2  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
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              "4  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
              "\n",
              "  product_brand  product_retail_price product_department  \\\n",
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              "2           NaN                  25.0                Men   \n",
              "3           NaN                  25.0                Men   \n",
              "4           NaN                  25.0                Men   \n",
              "\n",
              "                        product_sku  product_distribution_center_id  \n",
              "0  22811EE19846217512507785E74D12CC                               3  \n",
              "1  22811EE19846217512507785E74D12CC                               3  \n",
              "2  22811EE19846217512507785E74D12CC                               3  \n",
              "3  22811EE19846217512507785E74D12CC                               3  \n",
              "4  22811EE19846217512507785E74D12CC                               3  "
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      "source": [
        "# Melihat jumlah baris dan kolom\n",
        "inv_items.shape"
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      "source": [
        "# Rename kolom id menjadi inv_item_id\n",
        "inv_items.rename(columns={\"id\":\"inv_item_id\"})"
      ],
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            "text/plain": [
              "        inv_item_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",
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              "      <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",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>90969</td>\n",
              "      <td>16898</td>\n",
              "      <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",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>90970</td>\n",
              "      <td>16898</td>\n",
              "      <td>2021-08-19 15:17:00+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",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <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",
              "      <td>Men</td>\n",
              "      <td>22811EE19846217512507785E74D12CC</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487389</th>\n",
              "      <td>234443</td>\n",
              "      <td>14392</td>\n",
              "      <td>2020-04-09 17:38:00+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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487390</th>\n",
              "      <td>290890</td>\n",
              "      <td>14392</td>\n",
              "      <td>2021-03-18 14:42:49+00:00</td>\n",
              "      <td>2021-05-15 04:49:49+00:00</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487391</th>\n",
              "      <td>290891</td>\n",
              "      <td>14392</td>\n",
              "      <td>2022-04-25 03:04:00+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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487392</th>\n",
              "      <td>301361</td>\n",
              "      <td>14392</td>\n",
              "      <td>2021-05-15 11:44:11+00:00</td>\n",
              "      <td>2021-06-27 04:00:11+00:00</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>487393</th>\n",
              "      <td>301362</td>\n",
              "      <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",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>487394 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9c94b003-7ac8-4117-a9e6-761a6506ae33')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "      <script>\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-9c94b003-7ac8-4117-a9e6-761a6506ae33');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dataframe Tabel products\n",
        "products = pd.read_csv('/content/drive/MyDrive/KAMPUS MERDEKA BATCH 2/Project Ruang Guru Kel 255/Dataset Final Project DBA/products.csv')\n",
        "products.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 496
        },
        "id": "nM-4WCIoylcm",
        "outputId": "c909c002-d159-4e06-e8ec-ce68e6d176c6"
      },
      "execution_count": 64,
      "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",
              "                                                name brand  retail_price  \\\n",
              "0     2XU Men's Swimmers Compression Long Sleeve Top   2XU    150.410004   \n",
              "1         TYR Sport Men's Square Leg Short Swim Suit   TYR     38.990002   \n",
              "2    TYR Sport Men's Solid Durafast Jammer Swim Suit   TYR     27.600000   \n",
              "3  TYR Sport Men's Swim Short/Resistance Short Sw...   TYR     30.000000   \n",
              "4                    TYR Alliance Team Splice Jammer   TYR     45.950001   \n",
              "\n",
              "  department                               sku  distribution_center_id  \n",
              "0        Men  B23C5765E165D83AA924FA8F13C05F25                       1  \n",
              "1        Men  2AB7D3B23574C3DEA2BD278AFD0939AB                       1  \n",
              "2        Men  8F831227B0EB6C6D09A0555531365933                       1  \n",
              "3        Men  67317D6DCC4CB778AEB9219565F5456B                       1  \n",
              "4        Men  213C888198806EF1A0E2BBF2F4855C6C                       1  "
            ],
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              "\n",
              "  <div id=\"df-36d58ae0-b109-4fa0-b49f-2123c6091fa4\">\n",
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              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>cost</th>\n",
              "      <th>category</th>\n",
              "      <th>name</th>\n",
              "      <th>brand</th>\n",
              "      <th>retail_price</th>\n",
              "      <th>department</th>\n",
              "      <th>sku</th>\n",
              "      <th>distribution_center_id</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
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              "      <td>92.652563</td>\n",
              "      <td>Swim</td>\n",
              "      <td>2XU Men's Swimmers Compression Long Sleeve Top</td>\n",
              "      <td>2XU</td>\n",
              "      <td>150.410004</td>\n",
              "      <td>Men</td>\n",
              "      <td>B23C5765E165D83AA924FA8F13C05F25</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>27445</td>\n",
              "      <td>24.719661</td>\n",
              "      <td>Swim</td>\n",
              "      <td>TYR Sport Men's Square Leg Short Swim Suit</td>\n",
              "      <td>TYR</td>\n",
              "      <td>38.990002</td>\n",
              "      <td>Men</td>\n",
              "      <td>2AB7D3B23574C3DEA2BD278AFD0939AB</td>\n",
              "      <td>1</td>\n",
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              "      <td>15.897600</td>\n",
              "      <td>Swim</td>\n",
              "      <td>TYR Sport Men's Solid Durafast Jammer Swim Suit</td>\n",
              "      <td>TYR</td>\n",
              "      <td>27.600000</td>\n",
              "      <td>Men</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>27466</td>\n",
              "      <td>17.850000</td>\n",
              "      <td>Swim</td>\n",
              "      <td>TYR Sport Men's Swim Short/Resistance Short Sw...</td>\n",
              "      <td>TYR</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>Men</td>\n",
              "      <td>67317D6DCC4CB778AEB9219565F5456B</td>\n",
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              "      <td>29.408001</td>\n",
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              "      <td>TYR Alliance Team Splice Jammer</td>\n",
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              "      <td>45.950001</td>\n",
              "      <td>Men</td>\n",
              "      <td>213C888198806EF1A0E2BBF2F4855C6C</td>\n",
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              "\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-36d58ae0-b109-4fa0-b49f-2123c6091fa4');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Melihat jumlah baris dan kolom\n",
        "products.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0zydH4RRR5Xo",
        "outputId": "f70d92f2-fe94-403f-a73c-3a39735d01d1"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(29120, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rename column \n",
        "df1 = products.groupby(['id']).count().reset_index()\n",
        "df2 = df1.rename(columns={\"id\":\"product_id\"})\n",
        "df2.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "xWcoSzMRFzDK",
        "outputId": "120d583f-81f7-4256-8f6a-179f3b5e1f2c"
      },
      "execution_count": 8,
      "outputs": [
        {
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            "text/plain": [
              "   product_id  cost  category  name  brand  retail_price  department  sku  \\\n",
              "0           1     1         1     1      1             1           1    1   \n",
              "1           2     1         1     1      1             1           1    1   \n",
              "2           3     1         1     1      1             1           1    1   \n",
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              "\n",
              "   distribution_center_id  \n",
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    {
      "cell_type": "code",
      "source": [
        "df2.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s4OUneloH0AN",
        "outputId": "45de6bc7-ff84-4db3-c744-b7e344107d7b"
      },
      "execution_count": 9,
      "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   product_id              29120 non-null  int64\n",
            " 1   cost                    29120 non-null  int64\n",
            " 2   category                29120 non-null  int64\n",
            " 3   name                    29120 non-null  int64\n",
            " 4   brand                   29120 non-null  int64\n",
            " 5   retail_price            29120 non-null  int64\n",
            " 6   department              29120 non-null  int64\n",
            " 7   sku                     29120 non-null  int64\n",
            " 8   distribution_center_id  29120 non-null  int64\n",
            "dtypes: int64(9)\n",
            "memory usage: 2.0 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# JOIN DATA"
      ],
      "metadata": {
        "id": "dDTxgUZivvnK"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Fungsi Inner Join\n",
        "inner_join = pd.merge(inv_items, df2, \n",
        "                      on = 'product_id',\n",
        "                      how = 'inner')\n",
        "inner_join.head()"
      ],
      "metadata": {
        "id": "yFRmoxXTyyBr",
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          "height": 531
        },
        "outputId": "a8f7148b-084d-44a9-c91f-a7a8a0536529"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      id  product_id                 created_at                    sold_at  \\\n",
              "0  90967       16898  2021-07-19 02:20:10+00:00  2021-07-20 14:34:10+00:00   \n",
              "1  90968       16898  2020-04-13 07:46:00+00:00                        NaN   \n",
              "2  90969       16898  2020-09-02 17:43:00+00:00                        NaN   \n",
              "3  90970       16898  2021-08-19 15:17:00+00:00                        NaN   \n",
              "4  96715       16898  2020-05-09 11:16:10+00:00  2020-06-23 23:59:10+00:00   \n",
              "\n",
              "   cost_x product_category                           product_name  \\\n",
              "0  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
              "1  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
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              "3  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
              "4  13.925      Tops & Tees  Quiksilver Waterman Men's On The Rise   \n",
              "\n",
              "  product_brand  product_retail_price product_department  \\\n",
              "0           NaN                  25.0                Men   \n",
              "1           NaN                  25.0                Men   \n",
              "2           NaN                  25.0                Men   \n",
              "3           NaN                  25.0                Men   \n",
              "4           NaN                  25.0                Men   \n",
              "\n",
              "                        product_sku  product_distribution_center_id  cost_y  \\\n",
              "0  22811EE19846217512507785E74D12CC                               3       1   \n",
              "1  22811EE19846217512507785E74D12CC                               3       1   \n",
              "2  22811EE19846217512507785E74D12CC                               3       1   \n",
              "3  22811EE19846217512507785E74D12CC                               3       1   \n",
              "4  22811EE19846217512507785E74D12CC                               3       1   \n",
              "\n",
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              "0         1     1      0             1           1    1   \n",
              "1         1     1      0             1           1    1   \n",
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              "0                       1  \n",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      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-b74fea21-c50b-4764-807d-5b12d456b7e1 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-b74fea21-c50b-4764-807d-5b12d456b7e1');\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": 65
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# DATA EXPLORATION"
      ],
      "metadata": {
        "id": "K3zy4TNAJsVM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Detail info tiap variabel\n",
        "inner_join.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GvbnmnHyCwPE",
        "outputId": "3d55ef94-66cd-4d7d-8c2a-db2176cb2649"
      },
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 487394 entries, 0 to 487393\n",
            "Data columns (total 20 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_x                          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",
            " 12  cost_y                          487394 non-null  int64  \n",
            " 13  category                        487394 non-null  int64  \n",
            " 14  name                            487394 non-null  int64  \n",
            " 15  brand                           487394 non-null  int64  \n",
            " 16  retail_price                    487394 non-null  int64  \n",
            " 17  department                      487394 non-null  int64  \n",
            " 18  sku                             487394 non-null  int64  \n",
            " 19  distribution_center_id          487394 non-null  int64  \n",
            "dtypes: float64(2), int64(11), object(7)\n",
            "memory usage: 78.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Melihat jumlah baris dan kolom\n",
        "inner_join.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WgclX6hnRsNo",
        "outputId": "63820f55-d9d7-41bb-d4fd-6c6cf9cb66c3"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(487394, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join.describe().transpose()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        },
        "id": "yuUKKPNPJzH-",
        "outputId": "6dabbcc5-9de0-4c47-c72a-844f7e355139"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                   count           mean            std  \\\n",
              "id                              487394.0  243697.500000  140698.672888   \n",
              "product_id                      487394.0   15292.257424    8408.118539   \n",
              "cost_x                          487394.0      28.746374      30.629290   \n",
              "product_retail_price            487394.0      59.772834      65.871768   \n",
              "product_distribution_center_id  487394.0       4.992101       2.895917   \n",
              "cost_y                          487394.0       1.000000       0.000000   \n",
              "category                        487394.0       1.000000       0.000000   \n",
              "name                            487394.0       0.999928       0.008474   \n",
              "brand                           487394.0       0.999233       0.027690   \n",
              "retail_price                    487394.0       1.000000       0.000000   \n",
              "department                      487394.0       1.000000       0.000000   \n",
              "sku                             487394.0       1.000000       0.000000   \n",
              "distribution_center_id          487394.0       1.000000       0.000000   \n",
              "\n",
              "                                   min            25%            50%  \\\n",
              "id                              1.0000  121849.250000  243697.500000   \n",
              "product_id                      1.0000    8047.000000   16032.000000   \n",
              "cost_x                          0.0083      11.394300      19.944301   \n",
              "product_retail_price            0.0200      24.780001      39.990002   \n",
              "product_distribution_center_id  1.0000       2.000000       5.000000   \n",
              "cost_y                          1.0000       1.000000       1.000000   \n",
              "category                        1.0000       1.000000       1.000000   \n",
              "name                            0.0000       1.000000       1.000000   \n",
              "brand                           0.0000       1.000000       1.000000   \n",
              "retail_price                    1.0000       1.000000       1.000000   \n",
              "department                      1.0000       1.000000       1.000000   \n",
              "sku                             1.0000       1.000000       1.000000   \n",
              "distribution_center_id          1.0000       1.000000       1.000000   \n",
              "\n",
              "                                          75%            max  \n",
              "id                              365545.750000  487394.000000  \n",
              "product_id                       22558.000000   29120.000000  \n",
              "cost_x                              34.698691     557.151002  \n",
              "product_retail_price                69.949997     999.000000  \n",
              "product_distribution_center_id       8.000000      10.000000  \n",
              "cost_y                               1.000000       1.000000  \n",
              "category                             1.000000       1.000000  \n",
              "name                                 1.000000       1.000000  \n",
              "brand                                1.000000       1.000000  \n",
              "retail_price                         1.000000       1.000000  \n",
              "department                           1.000000       1.000000  \n",
              "sku                                  1.000000       1.000000  \n",
              "distribution_center_id               1.000000       1.000000  "
            ],
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              "\n",
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              "      <td>487394.0</td>\n",
              "      <td>243697.500000</td>\n",
              "      <td>140698.672888</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>121849.250000</td>\n",
              "      <td>243697.500000</td>\n",
              "      <td>365545.750000</td>\n",
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              "      <td>487394.0</td>\n",
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              "      <td>8408.118539</td>\n",
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              "      <td>8047.000000</td>\n",
              "      <td>16032.000000</td>\n",
              "      <td>22558.000000</td>\n",
              "      <td>29120.000000</td>\n",
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              "    <tr>\n",
              "      <th>cost_x</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>28.746374</td>\n",
              "      <td>30.629290</td>\n",
              "      <td>0.0083</td>\n",
              "      <td>11.394300</td>\n",
              "      <td>19.944301</td>\n",
              "      <td>34.698691</td>\n",
              "      <td>557.151002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>product_retail_price</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>59.772834</td>\n",
              "      <td>65.871768</td>\n",
              "      <td>0.0200</td>\n",
              "      <td>24.780001</td>\n",
              "      <td>39.990002</td>\n",
              "      <td>69.949997</td>\n",
              "      <td>999.000000</td>\n",
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              "    <tr>\n",
              "      <th>product_distribution_center_id</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>4.992101</td>\n",
              "      <td>2.895917</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>8.000000</td>\n",
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              "    <tr>\n",
              "      <th>cost_y</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>category</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>name</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>0.999928</td>\n",
              "      <td>0.008474</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
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              "    <tr>\n",
              "      <th>brand</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>0.999233</td>\n",
              "      <td>0.027690</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>retail_price</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>department</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sku</th>\n",
              "      <td>487394.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>distribution_center_id</th>\n",
              "      <td>487394.0</td>\n",
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              "      <td>1.0000</td>\n",
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              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ec97a92-df3b-4717-9a21-493bb0d6e599')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display: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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              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1ec97a92-df3b-4717-9a21-493bb0d6e599 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-1ec97a92-df3b-4717-9a21-493bb0d6e599');\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": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Summary statistics pada variabel\n",
        "print (inner_join.describe().T)\n",
        "print ('\\n\\t==================================================\\n')\n",
        "\n",
        "# Pembulatan decimal 2 angka di belakang koma\n",
        "print(inner_join.describe().T.round(2))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "30hB5QZsOGp4",
        "outputId": "f227a29f-c62b-4aef-e252-013cc9f72ad7"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "                                   count           mean            std  \\\n",
            "id                              487394.0  243697.500000  140698.672888   \n",
            "product_id                      487394.0   15292.257424    8408.118539   \n",
            "cost_x                          487394.0      28.746374      30.629290   \n",
            "product_retail_price            487394.0      59.772834      65.871768   \n",
            "product_distribution_center_id  487394.0       4.992101       2.895917   \n",
            "cost_y                          487394.0       1.000000       0.000000   \n",
            "category                        487394.0       1.000000       0.000000   \n",
            "name                            487394.0       0.999928       0.008474   \n",
            "brand                           487394.0       0.999233       0.027690   \n",
            "retail_price                    487394.0       1.000000       0.000000   \n",
            "department                      487394.0       1.000000       0.000000   \n",
            "sku                             487394.0       1.000000       0.000000   \n",
            "distribution_center_id          487394.0       1.000000       0.000000   \n",
            "\n",
            "                                   min            25%            50%  \\\n",
            "id                              1.0000  121849.250000  243697.500000   \n",
            "product_id                      1.0000    8047.000000   16032.000000   \n",
            "cost_x                          0.0083      11.394300      19.944301   \n",
            "product_retail_price            0.0200      24.780001      39.990002   \n",
            "product_distribution_center_id  1.0000       2.000000       5.000000   \n",
            "cost_y                          1.0000       1.000000       1.000000   \n",
            "category                        1.0000       1.000000       1.000000   \n",
            "name                            0.0000       1.000000       1.000000   \n",
            "brand                           0.0000       1.000000       1.000000   \n",
            "retail_price                    1.0000       1.000000       1.000000   \n",
            "department                      1.0000       1.000000       1.000000   \n",
            "sku                             1.0000       1.000000       1.000000   \n",
            "distribution_center_id          1.0000       1.000000       1.000000   \n",
            "\n",
            "                                          75%            max  \n",
            "id                              365545.750000  487394.000000  \n",
            "product_id                       22558.000000   29120.000000  \n",
            "cost_x                              34.698691     557.151002  \n",
            "product_retail_price                69.949997     999.000000  \n",
            "product_distribution_center_id       8.000000      10.000000  \n",
            "cost_y                               1.000000       1.000000  \n",
            "category                             1.000000       1.000000  \n",
            "name                                 1.000000       1.000000  \n",
            "brand                                1.000000       1.000000  \n",
            "retail_price                         1.000000       1.000000  \n",
            "department                           1.000000       1.000000  \n",
            "sku                                  1.000000       1.000000  \n",
            "distribution_center_id               1.000000       1.000000  \n",
            "\n",
            "\t==================================================\n",
            "\n",
            "                                   count       mean        std   min  \\\n",
            "id                              487394.0  243697.50  140698.67  1.00   \n",
            "product_id                      487394.0   15292.26    8408.12  1.00   \n",
            "cost_x                          487394.0      28.75      30.63  0.01   \n",
            "product_retail_price            487394.0      59.77      65.87  0.02   \n",
            "product_distribution_center_id  487394.0       4.99       2.90  1.00   \n",
            "cost_y                          487394.0       1.00       0.00  1.00   \n",
            "category                        487394.0       1.00       0.00  1.00   \n",
            "name                            487394.0       1.00       0.01  0.00   \n",
            "brand                           487394.0       1.00       0.03  0.00   \n",
            "retail_price                    487394.0       1.00       0.00  1.00   \n",
            "department                      487394.0       1.00       0.00  1.00   \n",
            "sku                             487394.0       1.00       0.00  1.00   \n",
            "distribution_center_id          487394.0       1.00       0.00  1.00   \n",
            "\n",
            "                                      25%        50%        75%        max  \n",
            "id                              121849.25  243697.50  365545.75  487394.00  \n",
            "product_id                        8047.00   16032.00   22558.00   29120.00  \n",
            "cost_x                              11.39      19.94      34.70     557.15  \n",
            "product_retail_price                24.78      39.99      69.95     999.00  \n",
            "product_distribution_center_id       2.00       5.00       8.00      10.00  \n",
            "cost_y                               1.00       1.00       1.00       1.00  \n",
            "category                             1.00       1.00       1.00       1.00  \n",
            "name                                 1.00       1.00       1.00       1.00  \n",
            "brand                                1.00       1.00       1.00       1.00  \n",
            "retail_price                         1.00       1.00       1.00       1.00  \n",
            "department                           1.00       1.00       1.00       1.00  \n",
            "sku                                  1.00       1.00       1.00       1.00  \n",
            "distribution_center_id               1.00       1.00       1.00       1.00  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> Dapat dilihat tipe data pada '*sold_at*' masih berupa *object*, sehingga perlu diubah menjadi tipe data *datetime* menggunakan function pandas yaitu *`to_datetime()`*"
      ],
      "metadata": {
        "id": "O3D7RwtTNqp5"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Set kolom 'sold_at' sebagai index\n",
        "inner_join = inner_join.set_index(['sold_at'])\n",
        "inner_join.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "91HoCAskNHA5",
        "outputId": "504ab8dc-356f-4a38-934e-30e55ff1a5df"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              id  product_id                 created_at  \\\n",
              "sold_at                                                                   \n",
              "2021-07-20 14:34:10+00:00  90967       16898  2021-07-19 02:20:10+00:00   \n",
              "NaT                        90968       16898  2020-04-13 07:46:00+00:00   \n",
              "NaT                        90969       16898  2020-09-02 17:43:00+00:00   \n",
              "NaT                        90970       16898  2021-08-19 15:17:00+00:00   \n",
              "2020-06-23 23:59:10+00:00  96715       16898  2020-05-09 11:16:10+00:00   \n",
              "\n",
              "                           cost_x product_category  \\\n",
              "sold_at                                              \n",
              "2021-07-20 14:34:10+00:00  13.925      Tops & Tees   \n",
              "NaT                        13.925      Tops & Tees   \n",
              "NaT                        13.925      Tops & Tees   \n",
              "NaT                        13.925      Tops & Tees   \n",
              "2020-06-23 23:59:10+00:00  13.925      Tops & Tees   \n",
              "\n",
              "                                                    product_name  \\\n",
              "sold_at                                                            \n",
              "2021-07-20 14:34:10+00:00  Quiksilver Waterman Men's On The Rise   \n",
              "NaT                        Quiksilver Waterman Men's On The Rise   \n",
              "NaT                        Quiksilver Waterman Men's On The Rise   \n",
              "NaT                        Quiksilver Waterman Men's On The Rise   \n",
              "2020-06-23 23:59:10+00:00  Quiksilver Waterman Men's On The Rise   \n",
              "\n",
              "                          product_brand  product_retail_price  \\\n",
              "sold_at                                                         \n",
              "2021-07-20 14:34:10+00:00           NaN                  25.0   \n",
              "NaT                                 NaN                  25.0   \n",
              "NaT                                 NaN                  25.0   \n",
              "NaT                                 NaN                  25.0   \n",
              "2020-06-23 23:59:10+00:00           NaN                  25.0   \n",
              "\n",
              "                          product_department  \\\n",
              "sold_at                                        \n",
              "2021-07-20 14:34:10+00:00                Men   \n",
              "NaT                                      Men   \n",
              "NaT                                      Men   \n",
              "NaT                                      Men   \n",
              "2020-06-23 23:59:10+00:00                Men   \n",
              "\n",
              "                                                product_sku  \\\n",
              "sold_at                                                       \n",
              "2021-07-20 14:34:10+00:00  22811EE19846217512507785E74D12CC   \n",
              "NaT                        22811EE19846217512507785E74D12CC   \n",
              "NaT                        22811EE19846217512507785E74D12CC   \n",
              "NaT                        22811EE19846217512507785E74D12CC   \n",
              "2020-06-23 23:59:10+00:00  22811EE19846217512507785E74D12CC   \n",
              "\n",
              "                           product_distribution_center_id  cost_y  category  \\\n",
              "sold_at                                                                       \n",
              "2021-07-20 14:34:10+00:00                               3       1         1   \n",
              "NaT                                                     3       1         1   \n",
              "NaT                                                     3       1         1   \n",
              "NaT                                                     3       1         1   \n",
              "2020-06-23 23:59:10+00:00                               3       1         1   \n",
              "\n",
              "                           name  brand  retail_price  department  sku  \\\n",
              "sold_at                                                                 \n",
              "2021-07-20 14:34:10+00:00     1      0             1           1    1   \n",
              "NaT                           1      0             1           1    1   \n",
              "NaT                           1      0             1           1    1   \n",
              "NaT                           1      0             1           1    1   \n",
              "2020-06-23 23:59:10+00:00     1      0             1           1    1   \n",
              "\n",
              "                           distribution_center_id  \n",
              "sold_at                                            \n",
              "2021-07-20 14:34:10+00:00                       1  \n",
              "NaT                                             1  \n",
              "NaT                                             1  \n",
              "NaT                                             1  \n",
              "2020-06-23 23:59:10+00:00                       1  "
            ],
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              "\n",
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              "      <td>90967</td>\n",
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              "      <td>13.925</td>\n",
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              "      border: none;\n",
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              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# EDA"
      ],
      "metadata": {
        "id": "8HoHfvww6Pge"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Corellation Matrix"
      ],
      "metadata": {
        "id": "fPCAsB0okCdv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join.select_dtypes(['float64', 'int64']).corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 521
        },
        "id": "S6Aghr94jTYd",
        "outputId": "fbc99c12-9399-4437-877e-6aca2c7690d2"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      id  product_id    cost_x  \\\n",
              "id                              1.000000   -0.002393 -0.002823   \n",
              "product_id                     -0.002393    1.000000 -0.008774   \n",
              "cost_x                         -0.002823   -0.008774  1.000000   \n",
              "product_retail_price           -0.003294   -0.001736  0.982524   \n",
              "product_distribution_center_id  0.004094    0.018531  0.005684   \n",
              "cost_y                               NaN         NaN       NaN   \n",
              "category                             NaN         NaN       NaN   \n",
              "name                           -0.002172   -0.003083 -0.003795   \n",
              "brand                          -0.001242   -0.006033  0.011550   \n",
              "retail_price                         NaN         NaN       NaN   \n",
              "department                           NaN         NaN       NaN   \n",
              "sku                                  NaN         NaN       NaN   \n",
              "distribution_center_id               NaN         NaN       NaN   \n",
              "\n",
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              "      <script>\n",
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              "                                                     [key], {});\n",
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              "          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",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inference:** We can see that all 7 columns have a direct relationship, which is our target variable."
      ],
      "metadata": {
        "id": "gzYW_eWAjtPV"
      }
    },
    {
      "cell_type": "code",
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        "inner_join.corr()['cost_x'].sort_values()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "-yHRaArgpqpH",
        "outputId": "e126e807-6999-45de-926c-71b339caeed6"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "product_id                       -0.008774\n",
              "name                             -0.003795\n",
              "id                               -0.002823\n",
              "product_distribution_center_id    0.005684\n",
              "brand                             0.011550\n",
              "product_retail_price              0.982524\n",
              "cost_x                            1.000000\n",
              "cost_y                                 NaN\n",
              "category                               NaN\n",
              "retail_price                           NaN\n",
              "department                             NaN\n",
              "sku                                    NaN\n",
              "distribution_center_id                 NaN\n",
              "Name: cost_x, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.corr()['product_distribution_center_id'].sort_values()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
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      },
      "execution_count": 79,
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        {
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            "text/plain": [
              "product_retail_price             -0.007425\n",
              "id                                0.004094\n",
              "cost                              0.005684\n",
              "product_id                        0.018531\n",
              "product_distribution_center_id    1.000000\n",
              "Name: product_distribution_center_id, dtype: float64"
            ]
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          "metadata": {},
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      ],
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        {
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          "data": {
            "text/plain": [
              "retail_price             -0.012443\n",
              "cost                      0.000443\n",
              "id                        0.015581\n",
              "distribution_center_id    1.000000\n",
              "Name: distribution_center_id, dtype: float64"
            ]
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          "metadata": {},
          "execution_count": 78
        }
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    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(inner_join.select_dtypes(['float64', 'int64']).corr(), annot=True)\n",
        "plt.show()"
      ],
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          "height": 411
        },
        "id": "EobRHTNZnUkz",
        "outputId": "b5a609d9-ed19-4506-b56b-2e55da0988ab"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inv_items.select_dtypes(['float64', 'int64']).corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Fl4FsjDom_zM",
        "outputId": "09a3204f-5572-420f-9eeb-6a39037cc601"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                      id  product_id      cost  \\\n",
              "id                              1.000000   -0.002393 -0.002823   \n",
              "product_id                     -0.002393    1.000000 -0.008774   \n",
              "cost                           -0.002823   -0.008774  1.000000   \n",
              "product_retail_price           -0.003294   -0.001736  0.982524   \n",
              "product_distribution_center_id  0.004094    0.018531  0.005684   \n",
              "\n",
              "                                product_retail_price  \\\n",
              "id                                         -0.003294   \n",
              "product_id                                 -0.001736   \n",
              "cost                                        0.982524   \n",
              "product_retail_price                        1.000000   \n",
              "product_distribution_center_id             -0.007425   \n",
              "\n",
              "                                product_distribution_center_id  \n",
              "id                                                    0.004094  \n",
              "product_id                                            0.018531  \n",
              "cost                                                  0.005684  \n",
              "product_retail_price                                 -0.007425  \n",
              "product_distribution_center_id                        1.000000  "
            ],
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              "      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-67580fcf-04fe-4784-8040-d1c32929b039 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-67580fcf-04fe-4784-8040-d1c32929b039');\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": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(inv_items.select_dtypes(['float64', 'int64']).corr(), annot=True, cmap='Blues')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        },
        "id": "TecycDX_j_SY",
        "outputId": "7d490013-c5aa-41b9-f274-46cb7c9914eb"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "products.select_dtypes(['float64', 'int64']).corr()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "_nxPDecXnG6g",
        "outputId": "7ec7d2ec-6ec8-4d63-d4b1-a5884da49008"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                              id      cost  retail_price  \\\n",
              "id                      1.000000 -0.009885     -0.003223   \n",
              "cost                   -0.009885  1.000000      0.982506   \n",
              "retail_price           -0.003223  0.982506      1.000000   \n",
              "distribution_center_id  0.015581  0.000443     -0.012443   \n",
              "\n",
              "                        distribution_center_id  \n",
              "id                                    0.015581  \n",
              "cost                                  0.000443  \n",
              "retail_price                         -0.012443  \n",
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              "          document.querySelector('#df-a88768d8-473f-4379-abd6-28c5cce0b7d7 button.colab-df-convert');\n",
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              "          const element = document.querySelector('#df-a88768d8-473f-4379-abd6-28c5cce0b7d7');\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",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(products.select_dtypes(['float64', 'int64']).corr(), annot=True, cmap='Greens')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 368
        },
        "id": "Q5H_tIb5l_ad",
        "outputId": "d11617a5-a33e-4fa7-efc9-7c04e7b71dd6"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handling Outlier"
      ],
      "metadata": {
        "id": "SeySX0sGRdRb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Boxplot cost_x\n",
        "sns.boxplot(x='product_distribution_center_id', y='cost_x',data=inner_join)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "b8ExpqEwqrmv",
        "outputId": "8cebac3d-f3a8-414b-a74a-e8de1cd8dcbe"
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fe095fa6e90>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. All outliers presented are in all `product_distribution_center_id`.\n",
        "2. We can see the minimum cost and minimum distribution."
      ],
      "metadata": {
        "id": "L6A_R--rqq1W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Histogram cost_x\n",
        "sns.distplot(inner_join['cost_x'], bins=5, kde=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 335
        },
        "id": "xZ9c3CDlTnTT",
        "outputId": "c3b63190-6bfb-43eb-aa60-d0118a79d398"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/seaborn/distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).\n",
            "  warnings.warn(msg, FutureWarning)\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Inference:** The average cost_x (which < 100) is the most frequent i.e. this should be in the category of distribution plot."
      ],
      "metadata": {
        "id": "f4s0B8njs5cy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(x='cost_x', y='product_id', data=inner_join, estimator=sum, ci=None)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        },
        "id": "o-Xyhp3pzMLj",
        "outputId": "f502bfaa-8af6-40e0-c88f-2edb306f70ea"
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Handling Missing Value"
      ],
      "metadata": {
        "id": "THh1zkNERac-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wkCT60U0RfoA",
        "outputId": "bb089961-8ec5-4b1c-9d60-d77d62ae81d2"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                  0\n",
              "product_id                          0\n",
              "created_at                          0\n",
              "cost_x                              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",
              "cost_y                              0\n",
              "category                            0\n",
              "name                                0\n",
              "brand                               0\n",
              "retail_price                        0\n",
              "department                          0\n",
              "sku                                 0\n",
              "distribution_center_id              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join[inner_join.isnull().any(axis=1)][['id', 'product_name', 'product_brand']]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "BjmtSUBiRzGM",
        "outputId": "9c6721cd-0551-487c-e6ce-5db98596c4cc"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                               id                           product_name  \\\n",
              "sold_at                                                                    \n",
              "2021-07-20 14:34:10+00:00   90967  Quiksilver Waterman Men's On The Rise   \n",
              "NaT                         90968  Quiksilver Waterman Men's On The Rise   \n",
              "NaT                         90969  Quiksilver Waterman Men's On The Rise   \n",
              "NaT                         90970  Quiksilver Waterman Men's On The Rise   \n",
              "2020-06-23 23:59:10+00:00   96715  Quiksilver Waterman Men's On The Rise   \n",
              "...                           ...                                    ...   \n",
              "NaT                        414448                                    NaN   \n",
              "NaT                        414449                                    NaN   \n",
              "2022-03-22 12:57:47+00:00  443537                                    NaN   \n",
              "NaT                        443538                                    NaN   \n",
              "NaT                        443539                                    NaN   \n",
              "\n",
              "                             product_brand  \n",
              "sold_at                                     \n",
              "2021-07-20 14:34:10+00:00              NaN  \n",
              "NaT                                    NaN  \n",
              "NaT                                    NaN  \n",
              "NaT                                    NaN  \n",
              "2020-06-23 23:59:10+00:00              NaN  \n",
              "...                                    ...  \n",
              "NaT                        Josie by Natori  \n",
              "NaT                        Josie by Natori  \n",
              "2022-03-22 12:57:47+00:00  Josie by Natori  \n",
              "NaT                        Josie by Natori  \n",
              "NaT                        Josie by Natori  \n",
              "\n",
              "[409 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64bfd4b1-1f73-4b81-8ca1-6e4df6757052\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id</th>\n",
              "      <th>product_name</th>\n",
              "      <th>product_brand</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sold_at</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-07-20 14:34:10+00:00</th>\n",
              "      <td>90967</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaT</th>\n",
              "      <td>90968</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaT</th>\n",
              "      <td>90969</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaT</th>\n",
              "      <td>90970</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020-06-23 23:59:10+00:00</th>\n",
              "      <td>96715</td>\n",
              "      <td>Quiksilver Waterman Men's On The Rise</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaT</th>\n",
              "      <td>414448</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Josie by Natori</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaT</th>\n",
              "      <td>414449</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Josie by Natori</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-03-22 12:57:47+00:00</th>\n",
              "      <td>443537</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Josie by Natori</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaT</th>\n",
              "      <td>443538</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Josie by Natori</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NaT</th>\n",
              "      <td>443539</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Josie by Natori</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>409 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64bfd4b1-1f73-4b81-8ca1-6e4df6757052')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
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              "\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-64bfd4b1-1f73-4b81-8ca1-6e4df6757052 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "          const element = document.querySelector('#df-64bfd4b1-1f73-4b81-8ca1-6e4df6757052');\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> We can't see them all in this display, but it looks like they may all have delay of 0. Let's check by looking at the summary statistics for these records:"
      ],
      "metadata": {
        "id": "3l7tmeQ3TSuE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join[inner_join.isnull().any(axis=1)].product_name.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q-lND_tbSNmT",
        "outputId": "a8df08e1-5ef2-4f3b-f377-d4539141524e"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                                                   374\n",
              "unique                                                   22\n",
              "top       NEW Aluminum Credit Card Wallet - RFID Blockin...\n",
              "freq                                                     26\n",
              "Name: product_name, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join[inner_join.isnull().any(axis=1)].product_brand.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OyZQ8Y_jSfH4",
        "outputId": "cf005427-94a0-4027-cb03-418742ba071e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                  35\n",
              "unique                  2\n",
              "top       Josie by Natori\n",
              "freq                   18\n",
              "Name: product_brand, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> The min, max, and mean are all 0. Let's replace the missing product_name and product_brand indicator with a 0 and confirm there are no more missing values."
      ],
      "metadata": {
        "id": "S_MVJiGPTC9y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "inner_join.product_name = inner_join.product_name.fillna(0)\n",
        "inner_join.product_brand = inner_join.product_brand.fillna(0)\n",
        "\n",
        "inner_join.isnull().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "odR8ksqrSx_F",
        "outputId": "b331fd67-42de-4252-ab3a-6ffb4cd2d836"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                                0\n",
              "product_id                        0\n",
              "created_at                        0\n",
              "cost_x                            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",
              "cost_y                            0\n",
              "category                          0\n",
              "name                              0\n",
              "brand                             0\n",
              "retail_price                      0\n",
              "department                        0\n",
              "sku                               0\n",
              "distribution_center_id            0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MODEL DEVELOPMENT"
      ],
      "metadata": {
        "id": "QrTa0HV4zxla"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import LabelEncoder"
      ],
      "metadata": {
        "id": "cFEgbphj5CfW"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Kita dapat memakai model linear regression menggunakan fitur `product_distribution_center_id` dan menghitung R^2."
      ],
      "metadata": {
        "id": "ybsQbvmg97-q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = inner_join[['product_distribution_center_id']]\n",
        "Y = inner_join['cost_x']\n",
        "lm = LinearRegression()\n",
        "lm.fit(X, Y)\n",
        "lm.score(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uq5nvUGx6S4H",
        "outputId": "186fc1ca-b9f2-4e85-a7ea-3c58a4f49fc8"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3.230269047505008e-05"
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gunakan model linear regression untuk memprediksi `cost_x` dengan menggunakan fitur `product_retail_price` kemudian hitung R^2. "
      ],
      "metadata": {
        "id": "zUOyi426-DWr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = inner_join[['product_retail_price']]\n",
        "Y = inner_join['cost_x']\n",
        "lr = LinearRegression()\n",
        "lr.fit(X, Y)\n",
        "lr.score(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tQE-ry499BQP",
        "outputId": "3ac1fa3e-5b71-4854-e9aa-21ef2b22eebd"
      },
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9653530311368302"
            ]
          },
          "metadata": {},
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Gunakan model linear regression untuk memprediksi `cost_x` menggunakan daftar fitur sbb:"
      ],
      "metadata": {
        "id": "EzXM6gdJ_mky"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "features = [\"brand\"]     "
      ],
      "metadata": {
        "id": "QxHx7vY_-Kri"
      },
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Menghitung R^2\n",
        "X = inner_join[features]\n",
        "Y = inner_join['cost_x']\n",
        "lr.fit(X, Y)\n",
        "lr.score(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tzZVKjZ92c4",
        "outputId": "ede3d043-b7ee-4a81-8787-ab73d59dbfd8"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00013340134394002057"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler,PolynomialFeatures\n",
        "from sklearn.linear_model import LinearRegression\n",
        "%matplotlib inline"
      ],
      "metadata": {
        "id": "kgzcjspdANfX"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Input = [('scale', StandardScaler()),\n",
        "       ('polynomial', PolynomialFeatures(include_bias=False)),\n",
        "       ('model',LinearRegression())]"
      ],
      "metadata": {
        "id": "39IbnGe__8ul"
      },
      "execution_count": 84,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X = inner_join[features]\n",
        "Y = inner_join['cost_x']\n",
        "pipe = Pipeline(Input)\n",
        "pipe.fit(X, Y)\n",
        "pipe.score(X, Y)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rm_16tINAPij",
        "outputId": "555015f8-bd6e-4e9b-cfe3-6617a1536879"
      },
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.00013340134390915637"
            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# MODEL EVALUATION"
      ],
      "metadata": {
        "id": "SEdvJzOaAcpJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "from sklearn.model_selection import train_test_split\n",
        "print(\"done\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qY2N4BT3AZZG",
        "outputId": "50f49593-d6a3-4787-b2f3-a5c03c6033c7"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "done\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features =[\"brand\"]    \n",
        "X = inner_join[features]\n",
        "Y = inner_join['cost_x']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(X, Y, test_size=0.15, random_state=1)\n",
        "\n",
        "print(\"Number of test samples\\t\\t:\", x_test.shape[0])\n",
        "print(\"Number of training samples\\t:\",x_train.shape[0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LM5EhPiBAfab",
        "outputId": "095c042e-74e9-4b6b-da00-3bf4f459084f"
      },
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of test samples\t\t: 73110\n",
            "Number of training samples\t: 414284\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import Ridge"
      ],
      "metadata": {
        "id": "k_XVDyhzAjXt"
      },
      "execution_count": 92,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rm = Ridge(alpha=0.1)\n",
        "rm.fit(x_train, y_train)\n",
        "rm.score(x_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "guHUIovhAj8U",
        "outputId": "fe7836b3-8664-456c-abe6-d4d1492c1232"
      },
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.502585834357923e-05"
            ]
          },
          "metadata": {},
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pr = PolynomialFeatures(degree=2) #second order polynomial\n",
        "x_train_pr = pr.fit_transform(x_train) #train data\n",
        "x_test_pr = pr.fit_transform(x_test) #test data\n",
        "\n",
        "rr = Ridge(alpha=0.1) #Regularization strength; must be a positive float. Regularization improves the conditioning of the problem and reduces the variance of the estimates. Larger values specify stronger regularization.\n",
        "rr.fit(x_train_pr, y_train)\n",
        "rr.score(x_test_pr, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rLcUx7DAAmN3",
        "outputId": "01849cc4-d3f5-43c9-90c6-b66a4b7f1afe"
      },
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8.501901312374383e-05"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "E9P_DZ_XCgBJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "regressor = LinearRegression()\n",
        "regressor.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WJ0tMTO4CU5-",
        "outputId": "d45259a1-e8e3-4622-d1e8-ac0e6b66cbd2"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# melakukan prediksi untuk hasil test set\n",
        "y_pred = regressor.predict(x_test)\n",
        "print(y_pred)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJu9k-RmCewG",
        "outputId": "ec05cecd-b3dd-4546-8f3a-64be8cb2446b"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[28.76433684 28.76433684 28.76433684 ... 28.76433684 28.76433684\n",
            " 28.76433684]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.DataFrame(inner_join)\n",
        "df.head"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VcXw8U8YDdqI",
        "outputId": "b870c143-f65e-46cc-b388-ae020790ba2b"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method NDFrame.head of             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_x 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  cost_y  category  name  brand  \\\n",
              "0                                    3       1         1     1      0   \n",
              "1                                    3       1         1     1      0   \n",
              "2                                    3       1         1     1      0   \n",
              "3                                    3       1         1     1      0   \n",
              "4                                    3       1         1     1      0   \n",
              "...                                ...     ...       ...   ...    ...   \n",
              "487389                               9       1         1     1      1   \n",
              "487390                               9       1         1     1      1   \n",
              "487391                               9       1         1     1      1   \n",
              "487392                               9       1         1     1      1   \n",
              "487393                               9       1         1     1      1   \n",
              "\n",
              "        retail_price  department  sku  distribution_center_id  \n",
              "0                  1           1    1                       1  \n",
              "1                  1           1    1                       1  \n",
              "2                  1           1    1                       1  \n",
              "3                  1           1    1                       1  \n",
              "4                  1           1    1                       1  \n",
              "...              ...         ...  ...                     ...  \n",
              "487389             1           1    1                       1  \n",
              "487390             1           1    1                       1  \n",
              "487391             1           1    1                       1  \n",
              "487392             1           1    1                       1  \n",
              "487393             1           1    1                       1  \n",
              "\n",
              "[487394 rows x 20 columns]>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluasi model linear regression\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from math import sqrt\n",
        "\n",
        "print(\"Aktual\\t\\t: \", y_test)\n",
        "print(\"Prediksi\\t: \", y_pred)\n",
        "print(\"RMSE\\t\\t: \", sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P59YAAouBOh4",
        "outputId": "57d713d4-0660-4868-83c3-89feed455f70"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Aktual\t\t:  172274    23.306710\n",
            "333228    75.116000\n",
            "63126     11.940570\n",
            "27244     52.920000\n",
            "376627    42.283299\n",
            "            ...    \n",
            "140070    73.260000\n",
            "249632     7.241000\n",
            "204443    22.386000\n",
            "245820    49.196000\n",
            "364206     9.080000\n",
            "Name: cost_x, Length: 73110, dtype: float64\n",
            "Prediksi\t:  [28.76433684 28.76433684 28.76433684 ... 28.76433684 28.76433684\n",
            " 28.76433684]\n",
            "RMSE\t\t:  30.340172011414772\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        ""
      ],
      "metadata": {
        "id": "oSznywTOEiUX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import r2_score\n",
        "\n",
        "r2Score = r2_score(y_pred, y_test)\n",
        "print(\"R2 score of model is\\t: \" , r2Score*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1OOZjexWEhrX",
        "outputId": "8cbeba17-b187-4b63-b073-8b139d32166b"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "R2 score of model is\t:  -756968.7042715822\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_squared_error\n",
        "\n",
        "mse = mean_squared_error(y_pred, y_test)\n",
        "print(\"Mean Squarred Error is\\t: \" , mse*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "APlJXQ_sEt9a",
        "outputId": "ec4238c7-56ca-4f21-9439-63b0a7e7fe77"
      },
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Squarred Error is\t:  92052.60376822363\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = np.sqrt(mean_squared_error(y_pred, y_test))\n",
        "print(\"Root Mean Squarred Error is\\t: \", rmse*100)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bvfhdVNcEeKw",
        "outputId": "841a1a93-46d6-43a1-82f3-a36c1dc3c62c"
      },
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Root Mean Squarred Error is\t:  3034.017201141477\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import mean_absolute_error\n",
        "\n",
        "mae = mean_absolute_error(y_pred, y_test)\n",
        "print(\"Mean Absolute Error is\\t: \" , mae)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "llOdDDQMEx5H",
        "outputId": "1565aaf7-0068-4c14-9543-a4b6315ebb7e"
      },
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error is\t:  18.9359949881828\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "> **Conclusion**\n",
        "\n",
        "So, the mean absolute error is '18.9359949881828'. Therefore our predicted value can be 6503.577323580025 units more or less than the actual value."
      ],
      "metadata": {
        "id": "nT6ofhehE5kG"
      }
    }
  ]
}