algorithm-apriori.ipynb

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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/hadiprasetiyo/apriori-mining-algorithm-project/blob/master/algorithm-apriori.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Forcasting (peramalan)**"
      ],
      "metadata": {
        "id": "krewAZE3ScKv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Peramalan mengacu pada proses menganalisis dan menjelaskan keadaan masa depan mengenai setiap operasi yang dilakukan. Proses ini mempertimbangkan masa lalu dan informasi saat ini dalam upaya untuk memprediksi fakta untuk peristiwa masa depan. Singkatnya, peramalan mengacu pada proses melihat ke depan, dan menentukan tren masa depan dan dampaknya terhadap organisasi."
      ],
      "metadata": {
        "id": "7ASAROU8Ckhw"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Regresi (Regression)**"
      ],
      "metadata": {
        "id": "vkDxatSuCqT0"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        " Metode yang satu ini sedikit mirip dengan klasifikasi data mining. Bedanya, regresi merupakan metode yang bertujuan untuk mencari pola nilai numerik, bukan kelas. Hasil dari metode regression adalah sebuah fungsi sebagai penentu hasil yang didasarkan nilai dari input."
      ],
      "metadata": {
        "id": "kVhD5n1zCuYe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **3. Kluster (Clustering)**"
      ],
      "metadata": {
        "id": "_6yuKFv0D8yq"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "mengidentifikasi pengelompokkan hal-hal berdasarkan karakter-karakter yang sudah diketahui, seperti mengelompokkan pelanggan dalam segment-segment yang berbeda berdasarkan demographis dan perilaku pembelian di masa lalu."
      ],
      "metadata": {
        "id": "E2WFefq5D89V"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **4. Klasifikasi (Clasification)**"
      ],
      "metadata": {
        "id": "Ma_1pEGEFZkN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Klasifikasi juga bisa diartikan sebagai proses mengategorisasikan suatu hal menjadi beberapa kelompok berdasarkan persamaan dan perbedaannya. Contohnya klasifikasi sampah berdasarkan jenis bahannya terbagi menjadi sampah organik dan sampah anorganik."
      ],
      "metadata": {
        "id": "kZkM2RvrFXy2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **1. Data Mining - Analisis Asosiasi (Association)**"
      ],
      "metadata": {
        "id": "6Q91CE5sScJt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Analisis asosiasi atau association rule mining adalah teknik data mining untuk menemukan aturan assosiatif antara suatu kombinasi item (Mengetahui hubungan antara satu atribut dengan yang lainnya). Contoh aturan assosiatif dari analisa pembelian di suatu pasar swalayan contohnya adalah dapat diketahuinya berapa besar kemungkinan seorang pelanggan membeli roti bersamaan dengan susu. Dengan pengetahuan tersebut pemilik pasar swalayan dapat mengatur penempatan barangnya atau merancang kampanye pemasaran dengan memakai kupon diskon untuk kombinasi barang tertentu.\n"
      ],
      "metadata": {
        "id": "rjqevDl4SNYL"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Ruang Lingkup : **Product Purchases** <br>\n",
        "**Algoritma apriori** : Digunakan agar komputer dapat mempelajari aturan asosiasi, mencari pola hubungan antar satu atau lebih item dalam suatu dataset. <br>\n",
        "**Permasalahan** yang ingin di selesaikan adalah membantu pihak retail dalam menentukan product bundling dan product placement dari data transaksi yang hendak digunakan."
      ],
      "metadata": {
        "id": "lqD6KWgcSbVV"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Tahapan data mining"
      ],
      "metadata": {
        "id": "4UWzzOuESNZj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. **Data Selection** -> Pemilihan (seleksi) data dari sekumpulan data operasional perlu dilakukan sebelum tahap penggalian informasi. Data hasil seleksi yang digunakan untuk proses data mining, disimpan dalam suatu berkas, terpisah dari basis data operasional. <br>\n",
        "2. **Data Pre-processing** -> Merupakan proses mengubah data mentah atau biasa dikenal dengan raw data yang dikumpulkan dari berbagai sumber menjadi informasi yang lebih bersih dan bisa digunakan untuk pengolahan selanjutnya.\n",
        "> *2.1 Data Cleansing* -> Data cleaning mencakup proses membuang duplikasi data, memeriksa data yang inkonsisten, dan memperbaiki kesalahan yang ada pada data.<br> *2.2 Data Transformation* -> Merupakan proses transformasi pada data yang telah dipilih. Lebih lanjut dari itu, proses transformasi data merupakan proses mengubah data kedalam format operasional tertentu, mengkonversi tipe data, melakukan beberapa perhitungan, penyaringan data yang tidak relevan, dan meringkasnya (tergantung pada jenis atau pola informasi yang akan dicari dalam basis data). <br>\n",
        "3. **Data mining** -> Data mining adalah proses mencari pola atau informasi menarik dalam data terpilih dengan menggunakan teknik atau metode tertentu. Teknik, metode, atau algoritma dalam data mining sangat bervariasi. Pemilihan metode atau algoritma yang tepat sangat bergantung pada tujuan dan proses data mining secara keseluruhan. <br>\n",
        "4. **Interpretation / evalution** -> Pola informasi yang dihasilkan dari proses data mining perlu ditampilkan dalam bentuk yang mudah dimengerti oleh pihak yang berkepentingan. Tahap ini mencakup pemeriksaan apakah pola atau informasi yang ditemukan bertentangan dengan fakta atau hipotesis yang ada sebelumnya."
      ],
      "metadata": {
        "id": "IS5Z5XcvSrN5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Question"
      ],
      "metadata": {
        "id": "IoBeX6XwS0lM"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Mengidentifikasi produk-produk yang sering dibeli secara bersamaan oleh customer kemudian membantu pihak retail dalam menentukan product bundling dan product placement dari data transaksi yang digunakan. (Market Basket Analitcs) <br>"
      ],
      "metadata": {
        "id": "EMz80jzQSrM7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZWYNggSrTzqc",
        "outputId": "04dc770e-43a7-4ad0-8741-0ed190d6e544"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install mlxtend"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rOrDvbxtTx-J",
        "outputId": "51254780-04f2-4c8e-8a42-0561bd71da86"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: mlxtend in /usr/local/lib/python3.10/dist-packages (0.14.0)\n",
            "Requirement already satisfied: scipy>=0.17 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.10.1)\n",
            "Requirement already satisfied: numpy>=1.10.4 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.22.4)\n",
            "Requirement already satisfied: pandas>=0.17.1 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.5.3)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (1.2.2)\n",
            "Requirement already satisfied: matplotlib>=1.5.1 in /usr/local/lib/python3.10/dist-packages (from mlxtend) (3.7.1)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from mlxtend) (67.7.2)\n",
            "Requirement already satisfied: contourpy>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.5.1->mlxtend) (1.0.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.5.1->mlxtend) (0.11.0)\n",
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            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.5.1->mlxtend) (23.1)\n",
            "Requirement already satisfied: pillow>=6.2.0 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.5.1->mlxtend) (8.4.0)\n",
            "Requirement already satisfied: pyparsing>=2.3.1 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.5.1->mlxtend) (3.0.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib>=1.5.1->mlxtend) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas>=0.17.1->mlxtend) (2022.7.1)\n",
            "Requirement already satisfied: joblib>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->mlxtend) (1.2.0)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn>=0.18->mlxtend) (3.1.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib>=1.5.1->mlxtend) (1.16.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pip install apyori"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mlC854F1T-1b",
        "outputId": "bde97639-140a-4553-a5c9-a74f6e16dbe1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting apyori\n",
            "  Downloading apyori-1.1.2.tar.gz (8.6 kB)\n",
            "  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Building wheels for collected packages: apyori\n",
            "  Building wheel for apyori (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for apyori: filename=apyori-1.1.2-py3-none-any.whl size=5956 sha256=bdb423290396231f617a0a4ccc1e65163f5b9bdc05068cdd7b56f4d1768b1860\n",
            "  Stored in directory: /root/.cache/pip/wheels/c4/1a/79/20f55c470a50bb3702a8cb7c94d8ada15573538c7f4baebe2d\n",
            "Successfully built apyori\n",
            "Installing collected packages: apyori\n",
            "Successfully installed apyori-1.1.2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "from apyori import apriori\n",
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "import datetime"
      ],
      "metadata": {
        "id": "rC4Q4gQiUBq_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Import dataset\n",
        "df = pd.read_excel('/content/drive/MyDrive/dataset_tugas1_datmin/data_retail2.xlsx')"
      ],
      "metadata": {
        "id": "GJpcVWgaUEQw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 921
        },
        "id": "_fWcqZttYLqe",
        "outputId": "ac1adaa7-6b6e-49a7-b523-8c54d528428c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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              "...          ...                 ...          ...             ...   \n",
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              "                                   ADDRESS         KOTA          PROVINSI  \\\n",
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              "...           ...            ...              ...       ...   \n",
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              "      <td>8992753282401</td>\n",
              "      <td>85123A</td>\n",
              "      <td>123 BENDERA COKLAT 300G</td>\n",
              "      <td>SUSU</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>DUA PUTRI SLAMET RIYADI</td>\n",
              "      <td>JL. SLAMET RIYADI</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>13</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>20020</td>\n",
              "      <td>8999909192034</td>\n",
              "      <td>71053</td>\n",
              "      <td>2.3.4 FILTER</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>SANURI</td>\n",
              "      <td>JL. M. SAID</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>19</td>\n",
              "      <td>YOGYAKARTA</td>\n",
              "      <td>20021</td>\n",
              "      <td>8999909007147</td>\n",
              "      <td>84406B</td>\n",
              "      <td>234 KERETEK</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>EMI MBAK</td>\n",
              "      <td>PS. TALUN NO.63</td>\n",
              "      <td>MAGELANG</td>\n",
              "      <td>JAWA TENGAH</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>190105</td>\n",
              "      <td>PT. KTRI DISTRIBUSI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>13</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>20018</td>\n",
              "      <td>8999909172234</td>\n",
              "      <td>84029G</td>\n",
              "      <td>234 KERETEK 12</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>RISKA CELL ADAM MALIK</td>\n",
              "      <td>JL. ADAM MALIK</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>19</td>\n",
              "      <td>YOGYAKARTA</td>\n",
              "      <td>20019</td>\n",
              "      <td>8999909005860</td>\n",
              "      <td>84029E</td>\n",
              "      <td>234 KERETEK 16</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>ABADI MART</td>\n",
              "      <td>JL.GROWONG - PUCUNG REJO MUNTILAN.</td>\n",
              "      <td>MAGELANG</td>\n",
              "      <td>JAWA TENGAH</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>42</td>\n",
              "      <td>Mini Market</td>\n",
              "      <td>190105</td>\n",
              "      <td>PT. KTRI DISTRIBUSI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>541904</th>\n",
              "      <td>581587</td>\n",
              "      <td>2021-12-09 12:50:00</td>\n",
              "      <td>22</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>K0336</td>\n",
              "      <td>8888166603240</td>\n",
              "      <td>22613</td>\n",
              "      <td>KG CHOCO SHORT CAKE 225G</td>\n",
              "      <td>BISKUIT</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>KPRI DIAN HUSADA (DINAS KESEHATAN)</td>\n",
              "      <td>JLN SIMPANG LA SUCIPTO NO 45</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>JAWA TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>45</td>\n",
              "      <td>Koperasi</td>\n",
              "      <td>220109</td>\n",
              "      <td>PT. KOTAK AGRO SUBUR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541905</th>\n",
              "      <td>581587</td>\n",
              "      <td>2021-12-09 12:50:00</td>\n",
              "      <td>22</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>K1923</td>\n",
              "      <td>8888166993044</td>\n",
              "      <td>22899</td>\n",
              "      <td>KG CHOCO WAY FINGER 33G</td>\n",
              "      <td>BISKUIT</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>ASSALAM</td>\n",
              "      <td>JLN TELUK BAYUR NO 59</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>JAWA TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>220109</td>\n",
              "      <td>PT. KOTAK AGRO SUBUR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541906</th>\n",
              "      <td>581587</td>\n",
              "      <td>2021-12-09 12:50:00</td>\n",
              "      <td>22</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>K0290</td>\n",
              "      <td>8888166336841</td>\n",
              "      <td>23254</td>\n",
              "      <td>KG CISSY 120G CRACKER</td>\n",
              "      <td>BISKUIT</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>RAHMA TK</td>\n",
              "      <td>JLN SANAN NO. 1A/14 (BLKG LAPAS)</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>JAWA TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>220109</td>\n",
              "      <td>PT. KOTAK AGRO SUBUR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541907</th>\n",
              "      <td>581587</td>\n",
              "      <td>2021-12-09 12:50:00</td>\n",
              "      <td>22</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>K3270</td>\n",
              "      <td>8991102023986</td>\n",
              "      <td>23255</td>\n",
              "      <td>KIDDO JUNIOR</td>\n",
              "      <td>PASTA &amp; S-GIGI</td>\n",
              "      <td>4</td>\n",
              "      <td>...</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>KARUNIA</td>\n",
              "      <td>PERUM SUKUN PONDOK INDAH N-9</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>JAWA TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>220109</td>\n",
              "      <td>PT. KOTAK AGRO SUBUR</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>541908</th>\n",
              "      <td>581587</td>\n",
              "      <td>2021-12-09 12:50:00</td>\n",
              "      <td>22</td>\n",
              "      <td>MALANG</td>\n",
              "      <td>K2949</td>\n",
              "      <td>8992942356128</td>\n",
              "      <td>22138</td>\n",
              "      <td>KINO LOLLYPOP COKLAT SUSU 20PC</td>\n",
              "      <td>PERMEN</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>12680.0</td>\n",
              "      <td>ALEX</td>\n",
              "      <td>BANJARSAWAH 24 DS PANCORAN</td>\n",
              "      <td>PROBOLINGGO</td>\n",
              "      <td>JAWA TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>220110</td>\n",
              "      <td>PT. SENANTIASA ABADI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>541909 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ae8482c6-9158-4732-a713-df15a3431ebd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.dtypes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K3rg3iMdUSCd",
        "outputId": "5fdf254f-2744-4679-a28a-53ca00cc2a50"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo                      object\n",
              "InvoiceDate            datetime64[ns]\n",
              "BRANCH_SPLR                     int64\n",
              "BRANCHNAME_SPLR                object\n",
              "warehouseProductsID            object\n",
              "BARCODEID                       int64\n",
              "StockCode                      object\n",
              "PRODUCT                        object\n",
              "PRODUCT_CATEGORY               object\n",
              "Quantity                        int64\n",
              "UnitPrice                     float64\n",
              "UnitPriceRupiah               float64\n",
              "oldCUSTID                      object\n",
              "CustomerID                    float64\n",
              "CUSTNAME                       object\n",
              "ADDRESS                        object\n",
              "KOTA                           object\n",
              "PROVINSI                       object\n",
              "NEGARA                         object\n",
              "CHANNELID_SPLR                  int64\n",
              "CHANNELNAME_SPLR               object\n",
              "SUBDISTID                       int64\n",
              "SUBDIST_NAME                   object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe()"
      ],
      "metadata": {
        "colab": {
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          "height": 364
        },
        "id": "x5Kk_kYOnfQh",
        "outputId": "bc4666bd-1d5d-487c-aa5d-ef3306625c9f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         BRANCH_SPLR     BARCODEID       Quantity      UnitPrice  \\\n",
              "count  541909.000000  5.419090e+05  541909.000000  541909.000000   \n",
              "mean       25.762779  8.144577e+12       9.552250       4.611114   \n",
              "std        12.696276  5.977482e+12     218.081158      96.759853   \n",
              "min         1.000000  1.000000e+00  -80995.000000  -11062.060000   \n",
              "25%        14.000000  8.888036e+12       1.000000       1.250000   \n",
              "50%        28.000000  8.992920e+12       3.000000       2.080000   \n",
              "75%        36.000000  8.998103e+12      10.000000       4.130000   \n",
              "max        45.000000  8.999999e+14   80995.000000   38970.000000   \n",
              "\n",
              "       UnitPriceRupiah     CustomerID  CHANNELID_SPLR      SUBDISTID  \n",
              "count     5.419090e+05  406829.000000   541909.000000  541909.000000  \n",
              "mean      6.593892e+04   15287.690570       36.072115  261119.832586  \n",
              "std       1.383666e+06    1713.600303        9.862746  129491.622938  \n",
              "min      -1.581875e+08   12346.000000       14.000000   10209.000000  \n",
              "25%       1.787500e+04   13953.000000       32.000000  140223.000000  \n",
              "50%       2.974400e+04   15152.000000       32.000000  280305.000000  \n",
              "75%       5.905900e+04   16791.000000       41.000000  380109.000000  \n",
              "max       5.572710e+08   18287.000000       93.000000  450202.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-f5576845-3625-4592-9d86-81121d232c63\">\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BRANCH_SPLR</th>\n",
              "      <th>BARCODEID</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>UnitPriceRupiah</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>CHANNELID_SPLR</th>\n",
              "      <th>SUBDISTID</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>541909.000000</td>\n",
              "      <td>5.419090e+05</td>\n",
              "      <td>541909.000000</td>\n",
              "      <td>541909.000000</td>\n",
              "      <td>5.419090e+05</td>\n",
              "      <td>406829.000000</td>\n",
              "      <td>541909.000000</td>\n",
              "      <td>541909.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.762779</td>\n",
              "      <td>8.144577e+12</td>\n",
              "      <td>9.552250</td>\n",
              "      <td>4.611114</td>\n",
              "      <td>6.593892e+04</td>\n",
              "      <td>15287.690570</td>\n",
              "      <td>36.072115</td>\n",
              "      <td>261119.832586</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>12.696276</td>\n",
              "      <td>5.977482e+12</td>\n",
              "      <td>218.081158</td>\n",
              "      <td>96.759853</td>\n",
              "      <td>1.383666e+06</td>\n",
              "      <td>1713.600303</td>\n",
              "      <td>9.862746</td>\n",
              "      <td>129491.622938</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000e+00</td>\n",
              "      <td>-80995.000000</td>\n",
              "      <td>-11062.060000</td>\n",
              "      <td>-1.581875e+08</td>\n",
              "      <td>12346.000000</td>\n",
              "      <td>14.000000</td>\n",
              "      <td>10209.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>14.000000</td>\n",
              "      <td>8.888036e+12</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>1.787500e+04</td>\n",
              "      <td>13953.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>140223.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>28.000000</td>\n",
              "      <td>8.992920e+12</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>2.080000</td>\n",
              "      <td>2.974400e+04</td>\n",
              "      <td>15152.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>280305.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>36.000000</td>\n",
              "      <td>8.998103e+12</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>4.130000</td>\n",
              "      <td>5.905900e+04</td>\n",
              "      <td>16791.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>380109.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>8.999999e+14</td>\n",
              "      <td>80995.000000</td>\n",
              "      <td>38970.000000</td>\n",
              "      <td>5.572710e+08</td>\n",
              "      <td>18287.000000</td>\n",
              "      <td>93.000000</td>\n",
              "      <td>450202.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f5576845-3625-4592-9d86-81121d232c63')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-f5576845-3625-4592-9d86-81121d232c63 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-f5576845-3625-4592-9d86-81121d232c63');\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": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pd.unique(df.BRANCHNAME_SPLR)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q3ZJw21woHeU",
        "outputId": "eee64582-a929-4611-fbe2-edbe4490e871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['SAMARINDA', 'YOGYAKARTA', 'BANDUNG', 'BANJARMASIN', 'TANGERANG',\n",
              "       'KEDIRI', 'SEMARANG', 'BALIKPAPAN', 'CIREBON', 'PANGKALPINANG',\n",
              "       'PURWOKERTO', 'MEDAN', 'MAKASSAR', 'DENPASAR', 'MALANG', 'JEMBER',\n",
              "       'MANADO', 'SURABAYA 2', 'JAMBI', 'KUDUS', 'TEGAL',\n",
              "       'BANDAR LAMPUNG', 'BOGOR', 'MATARAM', 'SOLO', 'TASIKMALAYA',\n",
              "       'PALEMBANG', 'BATAM', 'SUKABUMI', 'PADANG', 'PEKANBARU',\n",
              "       'SURABAYA 1', 'PONTIANAK', 'PALU'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.nunique()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ouefcujnnjP",
        "outputId": "90399f07-552c-47c4-9a0b-4a291233e556"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo              25899\n",
              "InvoiceDate            23260\n",
              "BRANCH_SPLR               34\n",
              "BRANCHNAME_SPLR           34\n",
              "warehouseProductsID    32193\n",
              "BARCODEID              32181\n",
              "StockCode               4070\n",
              "PRODUCT                32104\n",
              "PRODUCT_CATEGORY          53\n",
              "Quantity                 722\n",
              "UnitPrice               1630\n",
              "UnitPriceRupiah         1630\n",
              "oldCUSTID              35757\n",
              "CustomerID              4372\n",
              "CUSTNAME               33210\n",
              "ADDRESS                29808\n",
              "KOTA                     440\n",
              "PROVINSI                  35\n",
              "NEGARA                     1\n",
              "CHANNELID_SPLR            42\n",
              "CHANNELNAME_SPLR          42\n",
              "SUBDISTID                 58\n",
              "SUBDIST_NAME              54\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 2. Data Pre-processing\n"
      ],
      "metadata": {
        "id": "xgoNBPqKYcnG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.1 Data Cleansing"
      ],
      "metadata": {
        "id": "kVe8JcbCYguY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 341
        },
        "id": "hSCTwDLiYp1G",
        "outputId": "f7c015c1-ba7e-4649-c93f-d8c0a54297fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo         InvoiceDate  BRANCH_SPLR BRANCHNAME_SPLR  \\\n",
              "0    536365 2020-12-01 08:26:00           13       SAMARINDA   \n",
              "1    536365 2020-12-01 08:26:00           13       SAMARINDA   \n",
              "2    536365 2020-12-01 08:26:00           19      YOGYAKARTA   \n",
              "\n",
              "  warehouseProductsID      BARCODEID StockCode                  PRODUCT  \\\n",
              "0               10001  8992753282401    85123A  123 BENDERA COKLAT 300G   \n",
              "1               20020  8999909192034     71053             2.3.4 FILTER   \n",
              "2               20021  8999909007147    84406B              234 KERETEK   \n",
              "\n",
              "  PRODUCT_CATEGORY  Quantity  ...  CustomerID                 CUSTNAME  \\\n",
              "0             SUSU         6  ...     17850.0  DUA PUTRI SLAMET RIYADI   \n",
              "1            ROKOK         6  ...     17850.0                   SANURI   \n",
              "2            ROKOK         8  ...     17850.0                 EMI MBAK   \n",
              "\n",
              "             ADDRESS       KOTA          PROVINSI     NEGARA CHANNELID_SPLR  \\\n",
              "0  JL. SLAMET RIYADI  SAMARINDA  KALIMANTAN TIMUR  INDONESIA             32   \n",
              "1        JL. M. SAID  SAMARINDA  KALIMANTAN TIMUR  INDONESIA             32   \n",
              "2    PS. TALUN NO.63   MAGELANG      JAWA TENGAH   INDONESIA             32   \n",
              "\n",
              "  CHANNELNAME_SPLR SUBDISTID         SUBDIST_NAME  \n",
              "0   Toko Kelontong    130113        CV. EKA PUTRA  \n",
              "1   Toko Kelontong    130113        CV. EKA PUTRA  \n",
              "2   Toko Kelontong    190105  PT. KTRI DISTRIBUSI  \n",
              "\n",
              "[3 rows x 23 columns]"
            ],
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              "\n",
              "  <div id=\"df-bfcecb7c-bef1-4fbb-8d01-1b5ec574c10d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
              "    .dataframe tbody tr th {\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>BRANCH_SPLR</th>\n",
              "      <th>BRANCHNAME_SPLR</th>\n",
              "      <th>warehouseProductsID</th>\n",
              "      <th>BARCODEID</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>PRODUCT</th>\n",
              "      <th>PRODUCT_CATEGORY</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>...</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>CUSTNAME</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>KOTA</th>\n",
              "      <th>PROVINSI</th>\n",
              "      <th>NEGARA</th>\n",
              "      <th>CHANNELID_SPLR</th>\n",
              "      <th>CHANNELNAME_SPLR</th>\n",
              "      <th>SUBDISTID</th>\n",
              "      <th>SUBDIST_NAME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>13</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>10001</td>\n",
              "      <td>8992753282401</td>\n",
              "      <td>85123A</td>\n",
              "      <td>123 BENDERA COKLAT 300G</td>\n",
              "      <td>SUSU</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>DUA PUTRI SLAMET RIYADI</td>\n",
              "      <td>JL. SLAMET RIYADI</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>13</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>20020</td>\n",
              "      <td>8999909192034</td>\n",
              "      <td>71053</td>\n",
              "      <td>2.3.4 FILTER</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>SANURI</td>\n",
              "      <td>JL. M. SAID</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>19</td>\n",
              "      <td>YOGYAKARTA</td>\n",
              "      <td>20021</td>\n",
              "      <td>8999909007147</td>\n",
              "      <td>84406B</td>\n",
              "      <td>234 KERETEK</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>EMI MBAK</td>\n",
              "      <td>PS. TALUN NO.63</td>\n",
              "      <td>MAGELANG</td>\n",
              "      <td>JAWA TENGAH</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>190105</td>\n",
              "      <td>PT. KTRI DISTRIBUSI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>3 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-bfcecb7c-bef1-4fbb-8d01-1b5ec574c10d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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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",
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              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\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",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-bfcecb7c-bef1-4fbb-8d01-1b5ec574c10d 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-bfcecb7c-bef1-4fbb-8d01-1b5ec574c10d');\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": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengganti kolom PERIODE menjadi tipe data datetime\n",
        "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])"
      ],
      "metadata": {
        "id": "v_KEqo2GYX9u"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Clean up spaces in product description, product category and remove any rows that don't have a valid invoice\n",
        "df['PRODUCT'] = df['PRODUCT'].str.strip()\n",
        "df['PRODUCT_CATEGORY'] = df['PRODUCT_CATEGORY'].str.strip()\n",
        "\n",
        "df.dropna(axis=0, subset=['InvoiceNo'], inplace=True)"
      ],
      "metadata": {
        "id": "GF7SN2YqYy_0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# menghapus variabel inoviceNO yang diawali dengan huruf C pada invoice numbernya\n",
        "df['InvoiceNo'] = df['InvoiceNo'].astype('str')\n",
        "df = df[~(df['InvoiceNo'].str[0] == 'C')]"
      ],
      "metadata": {
        "id": "S_G4ywd3Yy-e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### 2.2 Data Transformation"
      ],
      "metadata": {
        "id": "aomL_3s5Znxq"
      }
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    {
      "cell_type": "code",
      "source": [
        "basket = (df[df['KOTA'] ==\"BALIKPAPAN\"].groupby(['InvoiceNo', 'PRODUCT_CATEGORY'])['Quantity'].count()\\\n",
        "                                      .unstack().reset_index().fillna(0)\\\n",
        "                                      .set_index('InvoiceNo'))\n",
        "basket.head()"
      ],
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          "metadata": {},
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        "# Show a subset of columns\n",
        "basket.iloc[:,[0,1,2,3,4,5,6,7]].head()"
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      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        "id": "L6krRjB4Z6vr",
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          "metadata": {},
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        "# Melakukan proses encoding -> Mengubah data kebentuk angka, agar sistem atau komputer dapat memahami informasi dari dataset\n",
        "def encode_units(x):\n",
        "    if x <= 0:\n",
        "        return 0\n",
        "    if x >= 1:\n",
        "        return 1\n",
        "\n",
        "basket_sets = basket.applymap(encode_units)\n",
        "basket_sets.head(5)"
      ],
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          },
          "metadata": {},
          "execution_count": 31
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 3. Data Mining\n",
        "### Data mining dapat dikenal juga dengan istilah data exploration"
      ],
      "metadata": {
        "id": "xYQ98sDGakbA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Build up the frequent items, the rules, and model\n",
        "\n",
        "frequent_itemsets = apriori(basket_sets, min_support=0.1, use_colnames=True)\n",
        "frequent_itemsets"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
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        "id": "Y8qCGyJOag0U",
        "outputId": "5f39b98c-b181-45c4-d927-313d61ff4ea9"
      },
      "execution_count": null,
      "outputs": [
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              "    <tr>\n",
              "      <th>251</th>\n",
              "      <td>0.117473</td>\n",
              "      <td>(SUSU, KOSMETIK, SABUN &amp; SAMPHOO, MINUMAN)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>252</th>\n",
              "      <td>0.102081</td>\n",
              "      <td>(PARFUM, KOSMETIK, SABUN &amp; SAMPHOO, OBATAN)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>253</th>\n",
              "      <td>0.106760</td>\n",
              "      <td>(PARFUM, SUSU, KOSMETIK, SABUN &amp; SAMPHOO)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>254</th>\n",
              "      <td>0.102327</td>\n",
              "      <td>(PARFUM, SUSU, SABUN &amp; SAMPHOO, MINUMAN)</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>255 rows × 2 columns</p>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dimana perintah yang di gunakan adalah apriori, dengan data dari basket_sets dengan minimum nilai support 0.1/ 10%."
      ],
      "metadata": {
        "id": "2AEi9W_KasW6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules1 = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
        "rules1.head() # 1280 rows × 9 columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "kMmosOpMagzP",
        "outputId": "bb40ddbe-e8df-45e6-8c23-84fff078b18c"
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      "execution_count": null,
      "outputs": [
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              "           antecedents          consequents  antecedent support  \\\n",
              "0  (ALAT RUMAH TANGGA)            (BISKUIT)            0.326438   \n",
              "1            (BISKUIT)  (ALAT RUMAH TANGGA)            0.348726   \n",
              "2  (ALAT RUMAH TANGGA)           (DETERGEN)            0.326438   \n",
              "3           (DETERGEN)  (ALAT RUMAH TANGGA)            0.273488   \n",
              "4           (KOSMETIK)  (ALAT RUMAH TANGGA)            0.412757   \n",
              "\n",
              "   consequent support   support  confidence      lift  leverage  conviction  \n",
              "0            0.348726  0.177811    0.544700  1.561974  0.063973    1.430430  \n",
              "1            0.326438  0.177811    0.509887  1.561974  0.063973    1.374300  \n",
              "2            0.273488  0.140377    0.430026  1.572375  0.051100    1.274641  \n",
              "3            0.326438  0.140377    0.513282  1.572375  0.051100    1.383887  \n",
              "4            0.326438  0.181997    0.440931  1.350735  0.047258    1.204792  "
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              "      <th>antecedents</th>\n",
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              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
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              "      <th>1</th>\n",
              "      <td>(BISKUIT)</td>\n",
              "      <td>(ALAT RUMAH TANGGA)</td>\n",
              "      <td>0.348726</td>\n",
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              "      <th>2</th>\n",
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              "      <th>3</th>\n",
              "      <td>(DETERGEN)</td>\n",
              "      <td>(ALAT RUMAH TANGGA)</td>\n",
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              "      <td>0.326438</td>\n",
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              "      <td>1.383887</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>(ALAT RUMAH TANGGA)</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.326438</td>\n",
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              "      <td>0.440931</td>\n",
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          "metadata": {},
          "execution_count": 33
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    {
      "cell_type": "code",
      "source": [
        "result1 = rules1[(rules1['lift'] >= 1) &\n",
        "               (rules1['confidence'] >= 0.8)]\n",
        "\n",
        "apr_result = result1.sort_values(by='confidence', ascending=False)\n",
        "apr_result.head(20) # 64 rows × 9 columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 676
        },
        "id": "OADOibkYa71G",
        "outputId": "70300ecc-d015-4893-f8af-415a4d5c2e37"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                        antecedents consequents  \\\n",
              "1240              (PARFUM, SABUN & SAMPHOO, OBATAN)  (KOSMETIK)   \n",
              "1253                (PARFUM, SUSU, SABUN & SAMPHOO)  (KOSMETIK)   \n",
              "1084             (SABUN & SAMPHOO, OBATAN, BISKUIT)   (MINUMAN)   \n",
              "1184             (PARFUM, SABUN & SAMPHOO, MINUMAN)  (KOSMETIK)   \n",
              "1058             (PARFUM, SABUN & SAMPHOO, BISKUIT)  (KOSMETIK)   \n",
              "1168                       (SUSU, KOSMETIK, OBATAN)   (MINUMAN)   \n",
              "1045             (SABUN & SAMPHOO, OBATAN, BISKUIT)  (KOSMETIK)   \n",
              "1072               (SUSU, SABUN & SAMPHOO, BISKUIT)  (KOSMETIK)   \n",
              "1212              (SNACK, SABUN & SAMPHOO, MINUMAN)  (KOSMETIK)   \n",
              "1157             (SABUN & SAMPHOO, OBATAN, MINUMAN)  (KOSMETIK)   \n",
              "1226               (SUSU, SABUN & SAMPHOO, MINUMAN)  (KOSMETIK)   \n",
              "1197                        (PARFUM, SUSU, MINUMAN)  (KOSMETIK)   \n",
              "1112               (SUSU, SABUN & SAMPHOO, BISKUIT)   (MINUMAN)   \n",
              "972                     (KOSMETIK, OBATAN, BISKUIT)   (MINUMAN)   \n",
              "1028                      (SUSU, KOSMETIK, BISKUIT)   (MINUMAN)   \n",
              "1142                      (PARFUM, OBATAN, MINUMAN)  (KOSMETIK)   \n",
              "1128           (SABUN & SAMPHOO, DETERGEN, MINUMAN)  (KOSMETIK)   \n",
              "1002            (SABUN & SAMPHOO, BISKUIT, MINUMAN)  (KOSMETIK)   \n",
              "1196                       (PARFUM, SUSU, KOSMETIK)   (MINUMAN)   \n",
              "961   (SABUN & SAMPHOO, ALAT RUMAH TANGGA, MINUMAN)  (KOSMETIK)   \n",
              "\n",
              "      antecedent support  consequent support   support  confidence      lift  \\\n",
              "1240            0.115503            0.412757  0.102081    0.883795  2.141200   \n",
              "1253            0.121290            0.412757  0.106760    0.880203  2.132497   \n",
              "1084            0.117596            0.383327  0.102697    0.873298  2.278206   \n",
              "1184            0.140500            0.412757  0.122522    0.872042  2.112725   \n",
              "1058            0.127694            0.412757  0.111316    0.871745  2.112006   \n",
              "1168            0.120552            0.383327  0.104913    0.870276  2.270321   \n",
              "1045            0.117596            0.412757  0.102327    0.870157  2.108158   \n",
              "1072            0.119074            0.412757  0.103559    0.869700  2.107051   \n",
              "1212            0.118212            0.412757  0.102204    0.864583  2.094654   \n",
              "1157            0.133481            0.412757  0.115257    0.863469  2.091954   \n",
              "1226            0.136190            0.412757  0.117473    0.862568  2.089771   \n",
              "1197            0.125600            0.412757  0.107992    0.859804  2.083075   \n",
              "1112            0.119074            0.383327  0.102327    0.859359  2.241842   \n",
              "972             0.126709            0.383327  0.108607    0.857143  2.236061   \n",
              "1028            0.127447            0.383327  0.109100    0.856039  2.233180   \n",
              "1142            0.122029            0.412757  0.104298    0.854692  2.070691   \n",
              "1128            0.120675            0.412757  0.102943    0.853061  2.066739   \n",
              "1002            0.143086            0.412757  0.122029    0.852840  2.066203   \n",
              "1196            0.127447            0.383327  0.107992    0.847343  2.210495   \n",
              "961             0.126832            0.412757  0.107007    0.843689  2.044034   \n",
              "\n",
              "      leverage  conviction  \n",
              "1240  0.054406    5.053522  \n",
              "1253  0.056697    4.901986  \n",
              "1084  0.057619    4.867128  \n",
              "1184  0.064530    4.589344  \n",
              "1058  0.058610    4.578729  \n",
              "1168  0.058702    4.753722  \n",
              "1045  0.053789    4.522718  \n",
              "1072  0.054410    4.506857  \n",
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              "1157  0.060161    4.301158  \n",
              "1226  0.061260    4.272965  \n",
              "1197  0.056149    4.188726  \n",
              "1112  0.056683    4.384725  \n",
              "972   0.060036    4.316710  \n",
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              "1142  0.053929    4.041373  \n",
              "1128  0.053134    3.996515  \n",
              "1002  0.062970    3.990505  \n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1240</th>\n",
              "      <td>(PARFUM, SABUN &amp; SAMPHOO, OBATAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.115503</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.102081</td>\n",
              "      <td>0.883795</td>\n",
              "      <td>2.141200</td>\n",
              "      <td>0.054406</td>\n",
              "      <td>5.053522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1253</th>\n",
              "      <td>(PARFUM, SUSU, SABUN &amp; SAMPHOO)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.121290</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.106760</td>\n",
              "      <td>0.880203</td>\n",
              "      <td>2.132497</td>\n",
              "      <td>0.056697</td>\n",
              "      <td>4.901986</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1084</th>\n",
              "      <td>(SABUN &amp; SAMPHOO, OBATAN, BISKUIT)</td>\n",
              "      <td>(MINUMAN)</td>\n",
              "      <td>0.117596</td>\n",
              "      <td>0.383327</td>\n",
              "      <td>0.102697</td>\n",
              "      <td>0.873298</td>\n",
              "      <td>2.278206</td>\n",
              "      <td>0.057619</td>\n",
              "      <td>4.867128</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1184</th>\n",
              "      <td>(PARFUM, SABUN &amp; SAMPHOO, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.140500</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.122522</td>\n",
              "      <td>0.872042</td>\n",
              "      <td>2.112725</td>\n",
              "      <td>0.064530</td>\n",
              "      <td>4.589344</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1058</th>\n",
              "      <td>(PARFUM, SABUN &amp; SAMPHOO, BISKUIT)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.127694</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.111316</td>\n",
              "      <td>0.871745</td>\n",
              "      <td>2.112006</td>\n",
              "      <td>0.058610</td>\n",
              "      <td>4.578729</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1168</th>\n",
              "      <td>(SUSU, KOSMETIK, OBATAN)</td>\n",
              "      <td>(MINUMAN)</td>\n",
              "      <td>0.120552</td>\n",
              "      <td>0.383327</td>\n",
              "      <td>0.104913</td>\n",
              "      <td>0.870276</td>\n",
              "      <td>2.270321</td>\n",
              "      <td>0.058702</td>\n",
              "      <td>4.753722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1045</th>\n",
              "      <td>(SABUN &amp; SAMPHOO, OBATAN, BISKUIT)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.117596</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.102327</td>\n",
              "      <td>0.870157</td>\n",
              "      <td>2.108158</td>\n",
              "      <td>0.053789</td>\n",
              "      <td>4.522718</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1072</th>\n",
              "      <td>(SUSU, SABUN &amp; SAMPHOO, BISKUIT)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.119074</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.103559</td>\n",
              "      <td>0.869700</td>\n",
              "      <td>2.107051</td>\n",
              "      <td>0.054410</td>\n",
              "      <td>4.506857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1212</th>\n",
              "      <td>(SNACK, SABUN &amp; SAMPHOO, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.118212</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.102204</td>\n",
              "      <td>0.864583</td>\n",
              "      <td>2.094654</td>\n",
              "      <td>0.053411</td>\n",
              "      <td>4.336563</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1157</th>\n",
              "      <td>(SABUN &amp; SAMPHOO, OBATAN, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.133481</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.115257</td>\n",
              "      <td>0.863469</td>\n",
              "      <td>2.091954</td>\n",
              "      <td>0.060161</td>\n",
              "      <td>4.301158</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1226</th>\n",
              "      <td>(SUSU, SABUN &amp; SAMPHOO, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.136190</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.117473</td>\n",
              "      <td>0.862568</td>\n",
              "      <td>2.089771</td>\n",
              "      <td>0.061260</td>\n",
              "      <td>4.272965</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1197</th>\n",
              "      <td>(PARFUM, SUSU, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.125600</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.107992</td>\n",
              "      <td>0.859804</td>\n",
              "      <td>2.083075</td>\n",
              "      <td>0.056149</td>\n",
              "      <td>4.188726</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1112</th>\n",
              "      <td>(SUSU, SABUN &amp; SAMPHOO, BISKUIT)</td>\n",
              "      <td>(MINUMAN)</td>\n",
              "      <td>0.119074</td>\n",
              "      <td>0.383327</td>\n",
              "      <td>0.102327</td>\n",
              "      <td>0.859359</td>\n",
              "      <td>2.241842</td>\n",
              "      <td>0.056683</td>\n",
              "      <td>4.384725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>972</th>\n",
              "      <td>(KOSMETIK, OBATAN, BISKUIT)</td>\n",
              "      <td>(MINUMAN)</td>\n",
              "      <td>0.126709</td>\n",
              "      <td>0.383327</td>\n",
              "      <td>0.108607</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>2.236061</td>\n",
              "      <td>0.060036</td>\n",
              "      <td>4.316710</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1028</th>\n",
              "      <td>(SUSU, KOSMETIK, BISKUIT)</td>\n",
              "      <td>(MINUMAN)</td>\n",
              "      <td>0.127447</td>\n",
              "      <td>0.383327</td>\n",
              "      <td>0.109100</td>\n",
              "      <td>0.856039</td>\n",
              "      <td>2.233180</td>\n",
              "      <td>0.060246</td>\n",
              "      <td>4.283600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1142</th>\n",
              "      <td>(PARFUM, OBATAN, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.122029</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.104298</td>\n",
              "      <td>0.854692</td>\n",
              "      <td>2.070691</td>\n",
              "      <td>0.053929</td>\n",
              "      <td>4.041373</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1128</th>\n",
              "      <td>(SABUN &amp; SAMPHOO, DETERGEN, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.120675</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.102943</td>\n",
              "      <td>0.853061</td>\n",
              "      <td>2.066739</td>\n",
              "      <td>0.053134</td>\n",
              "      <td>3.996515</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1002</th>\n",
              "      <td>(SABUN &amp; SAMPHOO, BISKUIT, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.143086</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.122029</td>\n",
              "      <td>0.852840</td>\n",
              "      <td>2.066203</td>\n",
              "      <td>0.062970</td>\n",
              "      <td>3.990505</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1196</th>\n",
              "      <td>(PARFUM, SUSU, KOSMETIK)</td>\n",
              "      <td>(MINUMAN)</td>\n",
              "      <td>0.127447</td>\n",
              "      <td>0.383327</td>\n",
              "      <td>0.107992</td>\n",
              "      <td>0.847343</td>\n",
              "      <td>2.210495</td>\n",
              "      <td>0.059138</td>\n",
              "      <td>4.039597</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>961</th>\n",
              "      <td>(SABUN &amp; SAMPHOO, ALAT RUMAH TANGGA, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.126832</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.107007</td>\n",
              "      <td>0.843689</td>\n",
              "      <td>2.044034</td>\n",
              "      <td>0.054656</td>\n",
              "      <td>3.756896</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Melakukan filter untuk nilai lift ratio lebih dari sama dengan 1 dengan tingkat confidence minimal 0.8 (lebih dari sama dengan 80%)"
      ],
      "metadata": {
        "id": "3LhedQxmbEZP"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lift Ratio adalah suatu ukuran (parameter) untuk mengetahui kekuatan aturan asosiasi (association rule) yang telah terbentuk dari nilai support"
      ],
      "metadata": {
        "id": "V3KijYmrUf8g"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "rules2 = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=2)\n",
        "rules2.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ZQH7bGMFH-Iy",
        "outputId": "64552397-0f16-47c8-b368-f34054b81c27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                     antecedents                   consequents  \\\n",
              "0  (ALAT RUMAH TANGGA, DETERGEN)                     (BISKUIT)   \n",
              "1            (BISKUIT, DETERGEN)           (ALAT RUMAH TANGGA)   \n",
              "2   (ALAT RUMAH TANGGA, BISKUIT)                    (DETERGEN)   \n",
              "3                     (DETERGEN)  (ALAT RUMAH TANGGA, BISKUIT)   \n",
              "4            (ALAT RUMAH TANGGA)           (BISKUIT, DETERGEN)   \n",
              "\n",
              "   antecedent support  consequent support   support  confidence      lift  \\\n",
              "0            0.140377            0.348726  0.101219    0.721053  2.067680   \n",
              "1            0.151459            0.326438  0.101219    0.668293  2.047229   \n",
              "2            0.177811            0.273488  0.101219    0.569252  2.081448   \n",
              "3            0.273488            0.177811  0.101219    0.370104  2.081448   \n",
              "4            0.326438            0.151459  0.101219    0.310072  2.047229   \n",
              "\n",
              "   leverage  conviction  \n",
              "0  0.052266    2.334758  \n",
              "1  0.051777    2.030592  \n",
              "2  0.052590    1.686628  \n",
              "3  0.052590    1.305277  \n",
              "4  0.051777    1.229897  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
              "      <th>leverage</th>\n",
              "      <th>conviction</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>(ALAT RUMAH TANGGA, DETERGEN)</td>\n",
              "      <td>(BISKUIT)</td>\n",
              "      <td>0.140377</td>\n",
              "      <td>0.348726</td>\n",
              "      <td>0.101219</td>\n",
              "      <td>0.721053</td>\n",
              "      <td>2.067680</td>\n",
              "      <td>0.052266</td>\n",
              "      <td>2.334758</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(BISKUIT, DETERGEN)</td>\n",
              "      <td>(ALAT RUMAH TANGGA)</td>\n",
              "      <td>0.151459</td>\n",
              "      <td>0.326438</td>\n",
              "      <td>0.101219</td>\n",
              "      <td>0.668293</td>\n",
              "      <td>2.047229</td>\n",
              "      <td>0.051777</td>\n",
              "      <td>2.030592</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>(ALAT RUMAH TANGGA, BISKUIT)</td>\n",
              "      <td>(DETERGEN)</td>\n",
              "      <td>0.177811</td>\n",
              "      <td>0.273488</td>\n",
              "      <td>0.101219</td>\n",
              "      <td>0.569252</td>\n",
              "      <td>2.081448</td>\n",
              "      <td>0.052590</td>\n",
              "      <td>1.686628</td>\n",
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              "      <th>3</th>\n",
              "      <td>(DETERGEN)</td>\n",
              "      <td>(ALAT RUMAH TANGGA, BISKUIT)</td>\n",
              "      <td>0.273488</td>\n",
              "      <td>0.177811</td>\n",
              "      <td>0.101219</td>\n",
              "      <td>0.370104</td>\n",
              "      <td>2.081448</td>\n",
              "      <td>0.052590</td>\n",
              "      <td>1.305277</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(ALAT RUMAH TANGGA)</td>\n",
              "      <td>(BISKUIT, DETERGEN)</td>\n",
              "      <td>0.326438</td>\n",
              "      <td>0.151459</td>\n",
              "      <td>0.101219</td>\n",
              "      <td>0.310072</td>\n",
              "      <td>2.047229</td>\n",
              "      <td>0.051777</td>\n",
              "      <td>1.229897</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "  </style>\n",
              "\n",
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              "          document.querySelector('#df-dff6954f-a6af-4a9e-bb40-f4277945e20e 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-dff6954f-a6af-4a9e-bb40-f4277945e20e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "result2 = rules2[ (rules2['lift'] >= 1) &\n",
        "                (rules2['confidence'] >= 0.8) ]\n",
        "\n",
        "best_result = result2.sort_values(by='confidence', ascending=False)\n",
        "best_result.head() # 17 rows × 9 columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "zdkCqUDzH4Ol",
        "outputId": "692ee756-a35a-4183-8c55-6d0c66cdb969"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                            antecedents consequents  antecedent support  \\\n",
              "780   (PARFUM, SABUN & SAMPHOO, OBATAN)  (KOSMETIK)            0.115503   \n",
              "793     (PARFUM, SUSU, SABUN & SAMPHOO)  (KOSMETIK)            0.121290   \n",
              "624  (SABUN & SAMPHOO, OBATAN, BISKUIT)   (MINUMAN)            0.117596   \n",
              "724  (PARFUM, SABUN & SAMPHOO, MINUMAN)  (KOSMETIK)            0.140500   \n",
              "598  (PARFUM, SABUN & SAMPHOO, BISKUIT)  (KOSMETIK)            0.127694   \n",
              "\n",
              "     consequent support   support  confidence      lift  leverage  conviction  \n",
              "780            0.412757  0.102081    0.883795  2.141200  0.054406    5.053522  \n",
              "793            0.412757  0.106760    0.880203  2.132497  0.056697    4.901986  \n",
              "624            0.383327  0.102697    0.873298  2.278206  0.057619    4.867128  \n",
              "724            0.412757  0.122522    0.872042  2.112725  0.064530    4.589344  \n",
              "598            0.412757  0.111316    0.871745  2.112006  0.058610    4.578729  "
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              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
              "      <th>confidence</th>\n",
              "      <th>lift</th>\n",
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              "      <th>conviction</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>780</th>\n",
              "      <td>(PARFUM, SABUN &amp; SAMPHOO, OBATAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.115503</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.102081</td>\n",
              "      <td>0.883795</td>\n",
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              "    <tr>\n",
              "      <th>793</th>\n",
              "      <td>(PARFUM, SUSU, SABUN &amp; SAMPHOO)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.121290</td>\n",
              "      <td>0.412757</td>\n",
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              "    <tr>\n",
              "      <th>624</th>\n",
              "      <td>(SABUN &amp; SAMPHOO, OBATAN, BISKUIT)</td>\n",
              "      <td>(MINUMAN)</td>\n",
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              "      <td>0.383327</td>\n",
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              "    <tr>\n",
              "      <th>724</th>\n",
              "      <td>(PARFUM, SABUN &amp; SAMPHOO, MINUMAN)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.140500</td>\n",
              "      <td>0.412757</td>\n",
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              "      <td>0.064530</td>\n",
              "      <td>4.589344</td>\n",
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              "    <tr>\n",
              "      <th>598</th>\n",
              "      <td>(PARFUM, SABUN &amp; SAMPHOO, BISKUIT)</td>\n",
              "      <td>(KOSMETIK)</td>\n",
              "      <td>0.127694</td>\n",
              "      <td>0.412757</td>\n",
              "      <td>0.111316</td>\n",
              "      <td>0.871745</td>\n",
              "      <td>2.112006</td>\n",
              "      <td>0.058610</td>\n",
              "      <td>4.578729</td>\n",
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              "          const element = document.querySelector('#df-ac835c2b-28ed-4853-8df6-04b8ef2c5fb3');\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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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "If Antecedent Then Concequent"
      ],
      "metadata": {
        "id": "ccr4JuUsJm6G"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## 4. Interpretation"
      ],
      "metadata": {
        "id": "2YLORX0BbJEv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Produk-produk yang dibeli secara bersamaan oleh customer di daerah **JAWA TENGAH** terhadap rule asosiasi pada dataset dengan min_support 0.1 / 10%, min_threshold = 1, dan nilai lift sebesar lebih dari samadengan 1 serta tingkat confidence minimal yang diperhitungkan sebesar 0.8 (80%) adalah:\n",
        "- **sabun, shampoo, obat-obatan, parfum** dengan **kosmetik**.\n",
        "- **kosmetik, susu, obat-obatan** dengan **minuman**.\n",
        "- **kosmetik, alat rumah tangga, minuman** dengan **sabun dan samphoo**.\n",
        "<br>\n",
        "<br>\n",
        "\n",
        "**Additional Note:**\n",
        "Produk atau barang yang menjadi kombinasi produk pertama untuk frekuensi yang paling banyak adalah **kosmetik, minuman, sabun dan sampho**."
      ],
      "metadata": {
        "id": "DQUfAPeGbNuU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# check barang kedua untuk kombinasi barang pertama\n",
        "# pairing kombinasi dari pembelian produk pertama yang paling banyak untuk barang kedua adalah\n",
        "\n",
        "apr_result['consequents'].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1-EzLNj4bByj",
        "outputId": "0bb37cbc-2ce1-495f-9e87-3551e1082d99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(KOSMETIK)           31\n",
              "(MINUMAN)            22\n",
              "(SABUN & SAMPHOO)    11\n",
              "Name: consequents, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **2. Customer Life Time Value**"
      ],
      "metadata": {
        "id": "xXLRxF6oKTjD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import dataset\n",
        "df = pd.read_excel('/content/drive/MyDrive/tugas1/data_retail2.xlsx')\n",
        "\n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 490
        },
        "id": "TRSqqdJkKmkz",
        "outputId": "e86a0610-7558-42e4-abd7-a021f17beaa4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo         InvoiceDate  BRANCH_SPLR BRANCHNAME_SPLR  \\\n",
              "0    536365 2020-12-01 08:26:00           13       SAMARINDA   \n",
              "1    536365 2020-12-01 08:26:00           13       SAMARINDA   \n",
              "2    536365 2020-12-01 08:26:00           19      YOGYAKARTA   \n",
              "3    536365 2020-12-01 08:26:00           13       SAMARINDA   \n",
              "4    536365 2020-12-01 08:26:00           19      YOGYAKARTA   \n",
              "\n",
              "  warehouseProductsID      BARCODEID StockCode                  PRODUCT  \\\n",
              "0               10001  8992753282401    85123A  123 BENDERA COKLAT 300G   \n",
              "1               20020  8999909192034     71053             2.3.4 FILTER   \n",
              "2               20021  8999909007147    84406B              234 KERETEK   \n",
              "3               20018  8999909172234    84029G           234 KERETEK 12   \n",
              "4               20019  8999909005860    84029E           234 KERETEK 16   \n",
              "\n",
              "  PRODUCT_CATEGORY  Quantity  ...  CustomerID                 CUSTNAME  \\\n",
              "0             SUSU         6  ...     17850.0  DUA PUTRI SLAMET RIYADI   \n",
              "1            ROKOK         6  ...     17850.0                   SANURI   \n",
              "2            ROKOK         8  ...     17850.0                 EMI MBAK   \n",
              "3            ROKOK         6  ...     17850.0    RISKA CELL ADAM MALIK   \n",
              "4            ROKOK         6  ...     17850.0               ABADI MART   \n",
              "\n",
              "                              ADDRESS       KOTA          PROVINSI     NEGARA  \\\n",
              "0                   JL. SLAMET RIYADI  SAMARINDA  KALIMANTAN TIMUR  INDONESIA   \n",
              "1                         JL. M. SAID  SAMARINDA  KALIMANTAN TIMUR  INDONESIA   \n",
              "2                     PS. TALUN NO.63   MAGELANG      JAWA TENGAH   INDONESIA   \n",
              "3                      JL. ADAM MALIK  SAMARINDA  KALIMANTAN TIMUR  INDONESIA   \n",
              "4  JL.GROWONG - PUCUNG REJO MUNTILAN.   MAGELANG      JAWA TENGAH   INDONESIA   \n",
              "\n",
              "  CHANNELID_SPLR CHANNELNAME_SPLR SUBDISTID         SUBDIST_NAME  \n",
              "0             32   Toko Kelontong    130113        CV. EKA PUTRA  \n",
              "1             32   Toko Kelontong    130113        CV. EKA PUTRA  \n",
              "2             32   Toko Kelontong    190105  PT. KTRI DISTRIBUSI  \n",
              "3             32   Toko Kelontong    130113        CV. EKA PUTRA  \n",
              "4             42      Mini Market    190105  PT. KTRI DISTRIBUSI  \n",
              "\n",
              "[5 rows x 23 columns]"
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              "      <th>BARCODEID</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>PRODUCT</th>\n",
              "      <th>PRODUCT_CATEGORY</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>...</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>CUSTNAME</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>KOTA</th>\n",
              "      <th>PROVINSI</th>\n",
              "      <th>NEGARA</th>\n",
              "      <th>CHANNELID_SPLR</th>\n",
              "      <th>CHANNELNAME_SPLR</th>\n",
              "      <th>SUBDISTID</th>\n",
              "      <th>SUBDIST_NAME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>13</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>10001</td>\n",
              "      <td>8992753282401</td>\n",
              "      <td>85123A</td>\n",
              "      <td>123 BENDERA COKLAT 300G</td>\n",
              "      <td>SUSU</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>DUA PUTRI SLAMET RIYADI</td>\n",
              "      <td>JL. SLAMET RIYADI</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>13</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>20020</td>\n",
              "      <td>8999909192034</td>\n",
              "      <td>71053</td>\n",
              "      <td>2.3.4 FILTER</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>SANURI</td>\n",
              "      <td>JL. M. SAID</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>19</td>\n",
              "      <td>YOGYAKARTA</td>\n",
              "      <td>20021</td>\n",
              "      <td>8999909007147</td>\n",
              "      <td>84406B</td>\n",
              "      <td>234 KERETEK</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>8</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>EMI MBAK</td>\n",
              "      <td>PS. TALUN NO.63</td>\n",
              "      <td>MAGELANG</td>\n",
              "      <td>JAWA TENGAH</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>190105</td>\n",
              "      <td>PT. KTRI DISTRIBUSI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>13</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>20018</td>\n",
              "      <td>8999909172234</td>\n",
              "      <td>84029G</td>\n",
              "      <td>234 KERETEK 12</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>RISKA CELL ADAM MALIK</td>\n",
              "      <td>JL. ADAM MALIK</td>\n",
              "      <td>SAMARINDA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>130113</td>\n",
              "      <td>CV. EKA PUTRA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>2020-12-01 08:26:00</td>\n",
              "      <td>19</td>\n",
              "      <td>YOGYAKARTA</td>\n",
              "      <td>20019</td>\n",
              "      <td>8999909005860</td>\n",
              "      <td>84029E</td>\n",
              "      <td>234 KERETEK 16</td>\n",
              "      <td>ROKOK</td>\n",
              "      <td>6</td>\n",
              "      <td>...</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>ABADI MART</td>\n",
              "      <td>JL.GROWONG - PUCUNG REJO MUNTILAN.</td>\n",
              "      <td>MAGELANG</td>\n",
              "      <td>JAWA TENGAH</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>42</td>\n",
              "      <td>Mini Market</td>\n",
              "      <td>190105</td>\n",
              "      <td>PT. KTRI DISTRIBUSI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 23 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-feae1217-4413-4df5-9d8e-d52507bf480e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Buat dataset dengan label target 'BALIKPAPAN'\n",
        "df2 = df[df[\"BRANCHNAME_SPLR\"]==\"BALIKPAPAN\"]"
      ],
      "metadata": {
        "id": "zTKyEWNZFGQQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_bd = pd.DataFrame(df2)\n",
        "df_bd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "4p90cHT7FMs9",
        "outputId": "14f904da-fe20-4980-d2e2-e2ec44df0aa2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       InvoiceNo         InvoiceDate  BRANCH_SPLR BRANCHNAME_SPLR  \\\n",
              "25        536369 2020-12-01 08:35:00           35      BALIKPAPAN   \n",
              "105       536380 2020-12-01 09:41:00           35      BALIKPAPAN   \n",
              "106       536381 2020-12-01 09:41:00           35      BALIKPAPAN   \n",
              "156       536384 2020-12-01 09:53:00           35      BALIKPAPAN   \n",
              "213       536390 2020-12-01 10:19:00           35      BALIKPAPAN   \n",
              "...          ...                 ...          ...             ...   \n",
              "445379    574837 2021-11-07 11:33:00           35      BALIKPAPAN   \n",
              "445380    574837 2021-11-07 11:33:00           35      BALIKPAPAN   \n",
              "445381    574837 2021-11-07 11:33:00           35      BALIKPAPAN   \n",
              "445382    574837 2021-11-07 11:33:00           35      BALIKPAPAN   \n",
              "445383    574837 2021-11-07 11:33:00           35      BALIKPAPAN   \n",
              "\n",
              "       warehouseProductsID      BARCODEID StockCode  \\\n",
              "25                   A2546   711844120150     21756   \n",
              "105                  A2407   711844162426     22961   \n",
              "106                  A2611   711844140219     22139   \n",
              "156                  A2482  8993478101053     84755   \n",
              "213                  A1610  8992705042022     22961   \n",
              "...                    ...            ...       ...   \n",
              "445379               P0156  4972940140503     23583   \n",
              "445380               P0125  4902430041539     23207   \n",
              "445381               P0085  8999232011002     71459   \n",
              "445382               P0084  9556031072932     84755   \n",
              "445383               P0083  9556031072956     22950   \n",
              "\n",
              "                                    PRODUCT   PRODUCT_CATEGORY  Quantity  ...  \\\n",
              "25                     ABC AYAM GRENG 340ML    SABUN & SAMPHOO         3  ...   \n",
              "105                     ABC SARI ASAM 250ML            MINUMAN        24  ...   \n",
              "106               ABC SAUS ASAM MANIS 195ML               SUSU        23  ...   \n",
              "156     ACTIFED SYRUP 60ML COUGH SUPPRESSAN            MINUMAN        48  ...   \n",
              "213                AGAR SATELIT SUPER GREEN            MANISAN        12  ...   \n",
              "...                                     ...                ...       ...  ...   \n",
              "445379               PANCI SUSU  GG KAYU 16  ALAT RUMAH TANGGA        10  ...   \n",
              "445380               PAMPERS BABY DRY XL-34            PAMPERS        10  ...   \n",
              "445381               PALMA M.GORENG 1LT REF      MINYAK GORENG        96  ...   \n",
              "445382               PALM. SOFT MOIST 100GR    SABUN & SAMPHOO        32  ...   \n",
              "445383  PALM. SILKY SMOOTH 100GR MILK & HON    SABUN & SAMPHOO        12  ...   \n",
              "\n",
              "        CustomerID                  CUSTNAME          ADDRESS  \\\n",
              "25         13047.0                    ALTON        SEPINGGAN    \n",
              "105        17809.0                     TK 79           BDS II   \n",
              "106        15311.0                  TK DZAKI            KM 50   \n",
              "156        18074.0  TK CAHAYA UJUNG PANDANG          SENIPAH    \n",
              "213        17511.0      KOPERASI RS HERMINA   JL MT HARIYONO    \n",
              "...            ...                       ...              ...   \n",
              "445379     15785.0                 TK SOFIE        SEPINNGAN    \n",
              "445380     15785.0                  TK ABAH      ASRAMA BUKIT   \n",
              "445381     15785.0                   TK BUDI     JL.MANUNGGAL   \n",
              "445382     15785.0                   TK.ANA        SEPINGGAN    \n",
              "445383     15785.0                KANTIN BBC   RS.BHAYANGKARA   \n",
              "\n",
              "                     KOTA          PROVINSI     NEGARA CHANNELID_SPLR  \\\n",
              "25             Balikpapan  Kalimantan Timur  INDONESIA             32   \n",
              "105            BALIKPAPAN  KALIMANTAN TIMUR  INDONESIA             32   \n",
              "106     KUTAI KARTANEGARA  KALIMANTAN TIMUR  INDONESIA             32   \n",
              "156            Balikpapan  Kalimantan Timur  INDONESIA             32   \n",
              "213            Balikpapan  Kalimantan Timur  INDONESIA             45   \n",
              "...                   ...               ...        ...            ...   \n",
              "445379         Balikpapan  Kalimantan Timur  INDONESIA             32   \n",
              "445380  KUTAI KARTANEGARA  KALIMANTAN TIMUR  INDONESIA             32   \n",
              "445381         BALIKPAPAN  KALIMANTAN TIMUR  INDONESIA             32   \n",
              "445382         Balikpapan  Kalimantan Timur  INDONESIA             32   \n",
              "445383         BALIKPAPAN  KALIMANTAN TIMUR  INDONESIA             14   \n",
              "\n",
              "       CHANNELNAME_SPLR SUBDISTID              SUBDIST_NAME  \n",
              "25       Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "105      Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "106      Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "156      Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "213            Koperasi    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "...                 ...       ...                       ...  \n",
              "445379   Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "445380   Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "445381   Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "445382   Toko Kelontong    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "445383         Apotik I    300106  CV. ANUGRAH DEZA MANDIRI  \n",
              "\n",
              "[13064 rows x 23 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>BRANCH_SPLR</th>\n",
              "      <th>BRANCHNAME_SPLR</th>\n",
              "      <th>warehouseProductsID</th>\n",
              "      <th>BARCODEID</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>PRODUCT</th>\n",
              "      <th>PRODUCT_CATEGORY</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>...</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>CUSTNAME</th>\n",
              "      <th>ADDRESS</th>\n",
              "      <th>KOTA</th>\n",
              "      <th>PROVINSI</th>\n",
              "      <th>NEGARA</th>\n",
              "      <th>CHANNELID_SPLR</th>\n",
              "      <th>CHANNELNAME_SPLR</th>\n",
              "      <th>SUBDISTID</th>\n",
              "      <th>SUBDIST_NAME</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>536369</td>\n",
              "      <td>2020-12-01 08:35:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>A2546</td>\n",
              "      <td>711844120150</td>\n",
              "      <td>21756</td>\n",
              "      <td>ABC AYAM GRENG 340ML</td>\n",
              "      <td>SABUN &amp; SAMPHOO</td>\n",
              "      <td>3</td>\n",
              "      <td>...</td>\n",
              "      <td>13047.0</td>\n",
              "      <td>ALTON</td>\n",
              "      <td>SEPINGGAN</td>\n",
              "      <td>Balikpapan</td>\n",
              "      <td>Kalimantan Timur</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>105</th>\n",
              "      <td>536380</td>\n",
              "      <td>2020-12-01 09:41:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>A2407</td>\n",
              "      <td>711844162426</td>\n",
              "      <td>22961</td>\n",
              "      <td>ABC SARI ASAM 250ML</td>\n",
              "      <td>MINUMAN</td>\n",
              "      <td>24</td>\n",
              "      <td>...</td>\n",
              "      <td>17809.0</td>\n",
              "      <td>TK 79</td>\n",
              "      <td>BDS II</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>106</th>\n",
              "      <td>536381</td>\n",
              "      <td>2020-12-01 09:41:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>A2611</td>\n",
              "      <td>711844140219</td>\n",
              "      <td>22139</td>\n",
              "      <td>ABC SAUS ASAM MANIS 195ML</td>\n",
              "      <td>SUSU</td>\n",
              "      <td>23</td>\n",
              "      <td>...</td>\n",
              "      <td>15311.0</td>\n",
              "      <td>TK DZAKI</td>\n",
              "      <td>KM 50</td>\n",
              "      <td>KUTAI KARTANEGARA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>156</th>\n",
              "      <td>536384</td>\n",
              "      <td>2020-12-01 09:53:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>A2482</td>\n",
              "      <td>8993478101053</td>\n",
              "      <td>84755</td>\n",
              "      <td>ACTIFED SYRUP 60ML COUGH SUPPRESSAN</td>\n",
              "      <td>MINUMAN</td>\n",
              "      <td>48</td>\n",
              "      <td>...</td>\n",
              "      <td>18074.0</td>\n",
              "      <td>TK CAHAYA UJUNG PANDANG</td>\n",
              "      <td>SENIPAH</td>\n",
              "      <td>Balikpapan</td>\n",
              "      <td>Kalimantan Timur</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>213</th>\n",
              "      <td>536390</td>\n",
              "      <td>2020-12-01 10:19:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>A1610</td>\n",
              "      <td>8992705042022</td>\n",
              "      <td>22961</td>\n",
              "      <td>AGAR SATELIT SUPER GREEN</td>\n",
              "      <td>MANISAN</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>17511.0</td>\n",
              "      <td>KOPERASI RS HERMINA</td>\n",
              "      <td>JL MT HARIYONO</td>\n",
              "      <td>Balikpapan</td>\n",
              "      <td>Kalimantan Timur</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>45</td>\n",
              "      <td>Koperasi</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <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>445379</th>\n",
              "      <td>574837</td>\n",
              "      <td>2021-11-07 11:33:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>P0156</td>\n",
              "      <td>4972940140503</td>\n",
              "      <td>23583</td>\n",
              "      <td>PANCI SUSU  GG KAYU 16</td>\n",
              "      <td>ALAT RUMAH TANGGA</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>15785.0</td>\n",
              "      <td>TK SOFIE</td>\n",
              "      <td>SEPINNGAN</td>\n",
              "      <td>Balikpapan</td>\n",
              "      <td>Kalimantan Timur</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445380</th>\n",
              "      <td>574837</td>\n",
              "      <td>2021-11-07 11:33:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>P0125</td>\n",
              "      <td>4902430041539</td>\n",
              "      <td>23207</td>\n",
              "      <td>PAMPERS BABY DRY XL-34</td>\n",
              "      <td>PAMPERS</td>\n",
              "      <td>10</td>\n",
              "      <td>...</td>\n",
              "      <td>15785.0</td>\n",
              "      <td>TK ABAH</td>\n",
              "      <td>ASRAMA BUKIT</td>\n",
              "      <td>KUTAI KARTANEGARA</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
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              "    <tr>\n",
              "      <th>445381</th>\n",
              "      <td>574837</td>\n",
              "      <td>2021-11-07 11:33:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>P0085</td>\n",
              "      <td>8999232011002</td>\n",
              "      <td>71459</td>\n",
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              "      <td>MINYAK GORENG</td>\n",
              "      <td>96</td>\n",
              "      <td>...</td>\n",
              "      <td>15785.0</td>\n",
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              "      <td>JL.MANUNGGAL</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445382</th>\n",
              "      <td>574837</td>\n",
              "      <td>2021-11-07 11:33:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>P0084</td>\n",
              "      <td>9556031072932</td>\n",
              "      <td>84755</td>\n",
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              "      <td>32</td>\n",
              "      <td>...</td>\n",
              "      <td>15785.0</td>\n",
              "      <td>TK.ANA</td>\n",
              "      <td>SEPINGGAN</td>\n",
              "      <td>Balikpapan</td>\n",
              "      <td>Kalimantan Timur</td>\n",
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              "      <td>32</td>\n",
              "      <td>Toko Kelontong</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>445383</th>\n",
              "      <td>574837</td>\n",
              "      <td>2021-11-07 11:33:00</td>\n",
              "      <td>35</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>P0083</td>\n",
              "      <td>9556031072956</td>\n",
              "      <td>22950</td>\n",
              "      <td>PALM. SILKY SMOOTH 100GR MILK &amp; HON</td>\n",
              "      <td>SABUN &amp; SAMPHOO</td>\n",
              "      <td>12</td>\n",
              "      <td>...</td>\n",
              "      <td>15785.0</td>\n",
              "      <td>KANTIN BBC</td>\n",
              "      <td>RS.BHAYANGKARA</td>\n",
              "      <td>BALIKPAPAN</td>\n",
              "      <td>KALIMANTAN TIMUR</td>\n",
              "      <td>INDONESIA</td>\n",
              "      <td>14</td>\n",
              "      <td>Apotik I</td>\n",
              "      <td>300106</td>\n",
              "      <td>CV. ANUGRAH DEZA MANDIRI</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>13064 rows × 23 columns</p>\n",
              "</div>\n",
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "product_df = df.value_counts()\n",
        "product_df = pd.DataFrame(product_df)\n",
        "product_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 952
        },
        "id": "0GqE7Lh_K4ig",
        "outputId": "6c42eb0c-eb57-4ca1-b562-ff8c4fe49e31"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                                                                                                                                                                                                                                                                                                                                                                0\n",
              "InvoiceNo InvoiceDate         BRANCH_SPLR BRANCHNAME_SPLR warehouseProductsID BARCODEID     StockCode PRODUCT                       PRODUCT_CATEGORY  Quantity UnitPrice UnitPriceRupiah oldCUSTID CustomerID CUSTNAME                  ADDRESS                                  KOTA         PROVINSI         NEGARA    CHANNELID_SPLR CHANNELNAME_SPLR     SUBDISTID SUBDIST_NAME              \n",
              "565411    2021-09-04 11:22:00 6           SEMARANG        V0908               8997016370499 22895     VICTORIA ABU - ABU 60ML       PARFUM             1       3.25      46475.0         1611777   13772.0    COO CAFE (GEDUNG MERPATI) RSUD KARIADI JL.DR,SUTOMO                SEMARANG     JAWA TENGAH      INDONESIA 52             PBC (Pub, Bar, Cafe) 60313     PT. SABRINA ABADI FARMA  2\n",
              "536365    2020-12-01 08:26:00 13          SAMARINDA       10001               8992753282401 85123A    123 BENDERA COKLAT 300G       SUSU               6       2.55      36465.0         3012815   17850.0    DUA PUTRI SLAMET RIYADI   JL. SLAMET RIYADI                        SAMARINDA    KALIMANTAN TIMUR INDONESIA 32             Toko Kelontong       130113    CV. EKA PUTRA            1\n",
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              "                                                          P4545               8992802218016 22966     PRENAGEN ESENSIS CKLT 180G    MAKANAN BAYI       24      1.25      17875.0         4511979   14644.0    AL FARIZI (KD-KR MALANG)  KARANG MALANG RT 03/04                   KUDUS        JAWA TENGAH      INDONESIA 32             Toko Kelontong       450202    CV. CAHYO SELAMANYA      1\n",
              "...                                                                                                                                                                                                                                                                                                                                                                                            ..\n",
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              "                                                          L0723               8999979026352 22800     LS ICE TRAY                   ALAT RUMAH TANGGA  4       3.75      53625.0         3217725   15095.0    ENTIN, TK ENT0101         KARANG SALAM                             BANYUMAS     JAWA TENGAH      INDONESIA 32             Toko Kelontong       320302    CV. CITRA BERUSAHA       1\n",
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              "C581569   2021-12-09 11:58:00 22          MALANG          G0102               8888036009387 20979     G/L GEL 330ML BLUE            MINYAK RAMBUT     -5       1.25      17875.0         2212680   17315.0    TETAP JAYA TK             JLN GATOT SUBROTO NO 3 TALOK             MALANG       JAWA TIMUR       INDONESIA 32             Toko Kelontong       220109    PT. KOTAK AGRO SUBUR     1\n",
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              "      <th>36465.0</th>\n",
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              "      <th>17850.0</th>\n",
              "      <th>DUA PUTRI SLAMET RIYADI</th>\n",
              "      <th>JL. SLAMET RIYADI</th>\n",
              "      <th>SAMARINDA</th>\n",
              "      <th>KALIMANTAN TIMUR</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>130113</th>\n",
              "      <th>CV. EKA PUTRA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
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              "      <th rowspan=\"3\" valign=\"top\">569239</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">2021-10-03 08:30:00</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">45</th>\n",
              "      <th rowspan=\"3\" valign=\"top\">KUDUS</th>\n",
              "      <th>P4758</th>\n",
              "      <th>8999999013196</th>\n",
              "      <th>23247</th>\n",
              "      <th>PONS FF SMOOTH PORES 50G</th>\n",
              "      <th>OBATAN</th>\n",
              "      <th>36</th>\n",
              "      <th>2.49</th>\n",
              "      <th>35607.0</th>\n",
              "      <th>4512536</th>\n",
              "      <th>14644.0</th>\n",
              "      <th>HUFADZ (JP-BANGSRI)</th>\n",
              "      <th>JL RAYA BANGSRI RT 14 RW 03 JAMBU BARAT</th>\n",
              "      <th>JEPARA</th>\n",
              "      <th>JAWA TENGAH</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>450202</th>\n",
              "      <th>CV. CAHYO SELAMANYA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4642</th>\n",
              "      <th>8992802236034</th>\n",
              "      <th>23294</th>\n",
              "      <th>PRENAGEN ESENSIS VANILA 360G</th>\n",
              "      <th>KOSMETIK</th>\n",
              "      <th>8</th>\n",
              "      <th>0.83</th>\n",
              "      <th>11869.0</th>\n",
              "      <th>4511476</th>\n",
              "      <th>14644.0</th>\n",
              "      <th>RIMA (KD03-GEBOG)</th>\n",
              "      <th>DEPAN TK RADHA</th>\n",
              "      <th>KUDUS</th>\n",
              "      <th>JAWA TENGAH</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>450202</th>\n",
              "      <th>CV. CAHYO SELAMANYA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P4545</th>\n",
              "      <th>8992802218016</th>\n",
              "      <th>22966</th>\n",
              "      <th>PRENAGEN ESENSIS CKLT 180G</th>\n",
              "      <th>MAKANAN BAYI</th>\n",
              "      <th>24</th>\n",
              "      <th>1.25</th>\n",
              "      <th>17875.0</th>\n",
              "      <th>4511979</th>\n",
              "      <th>14644.0</th>\n",
              "      <th>AL FARIZI (KD-KR MALANG)</th>\n",
              "      <th>KARANG MALANG RT 03/04</th>\n",
              "      <th>KUDUS</th>\n",
              "      <th>JAWA TENGAH</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>450202</th>\n",
              "      <th>CV. CAHYO SELAMANYA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">554039</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">2021-05-20 14:27:00</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">36</th>\n",
              "      <th rowspan=\"4\" valign=\"top\">PURWOKERTO</th>\n",
              "      <th>L0863</th>\n",
              "      <th>8999979018128</th>\n",
              "      <th>22791</th>\n",
              "      <th>LS TS-16 SOFIE CUTLERY HOLDER</th>\n",
              "      <th>ALAT RUMAH TANGGA</th>\n",
              "      <th>24</th>\n",
              "      <th>1.25</th>\n",
              "      <th>17875.0</th>\n",
              "      <th>3216960</th>\n",
              "      <th>15095.0</th>\n",
              "      <th>ELANG, TK ELG0113</th>\n",
              "      <th>REMPOAH-BATURADEN</th>\n",
              "      <th>BANYUMAS</th>\n",
              "      <th>JAWA TENGAH</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>320302</th>\n",
              "      <th>CV. CITRA BERUSAHA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L0817</th>\n",
              "      <th>8999979010368</th>\n",
              "      <th>22792</th>\n",
              "      <th>LS SEMPROTAN HS-6 1000ML</th>\n",
              "      <th>ALAT RUMAH TANGGA</th>\n",
              "      <th>24</th>\n",
              "      <th>0.85</th>\n",
              "      <th>12155.0</th>\n",
              "      <th>3217701</th>\n",
              "      <th>15095.0</th>\n",
              "      <th>RIKO RKO0165</th>\n",
              "      <th>CINANGSI</th>\n",
              "      <th>CILACAP</th>\n",
              "      <th>JAWA TENGAH</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>320302</th>\n",
              "      <th>CV. CITRA BERUSAHA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L0723</th>\n",
              "      <th>8999979026352</th>\n",
              "      <th>22800</th>\n",
              "      <th>LS ICE TRAY</th>\n",
              "      <th>ALAT RUMAH TANGGA</th>\n",
              "      <th>4</th>\n",
              "      <th>3.75</th>\n",
              "      <th>53625.0</th>\n",
              "      <th>3217725</th>\n",
              "      <th>15095.0</th>\n",
              "      <th>ENTIN, TK ENT0101</th>\n",
              "      <th>KARANG SALAM</th>\n",
              "      <th>BANYUMAS</th>\n",
              "      <th>JAWA TENGAH</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>320302</th>\n",
              "      <th>CV. CITRA BERUSAHA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>L0584</th>\n",
              "      <th>8992222110594</th>\n",
              "      <th>23065</th>\n",
              "      <th>LOVILLEA G.COL 50ML MUSKY FLR</th>\n",
              "      <th>PARFUM</th>\n",
              "      <th>1</th>\n",
              "      <th>12.50</th>\n",
              "      <th>178750.0</th>\n",
              "      <th>3217408</th>\n",
              "      <th>15095.0</th>\n",
              "      <th>TAPEN, APOTEK TPN0159</th>\n",
              "      <th>JL. RAYA PEREMPATAN TIMUR TAPEN-WANADADI</th>\n",
              "      <th>BANJARNEGARA</th>\n",
              "      <th>JAWA TENGAH</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>320302</th>\n",
              "      <th>CV. CITRA BERUSAHA</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C581569</th>\n",
              "      <th>2021-12-09 11:58:00</th>\n",
              "      <th>22</th>\n",
              "      <th>MALANG</th>\n",
              "      <th>G0102</th>\n",
              "      <th>8888036009387</th>\n",
              "      <th>20979</th>\n",
              "      <th>G/L GEL 330ML BLUE</th>\n",
              "      <th>MINYAK RAMBUT</th>\n",
              "      <th>-5</th>\n",
              "      <th>1.25</th>\n",
              "      <th>17875.0</th>\n",
              "      <th>2212680</th>\n",
              "      <th>17315.0</th>\n",
              "      <th>TETAP JAYA TK</th>\n",
              "      <th>JLN GATOT SUBROTO NO 3 TALOK</th>\n",
              "      <th>MALANG</th>\n",
              "      <th>JAWA TIMUR</th>\n",
              "      <th>INDONESIA</th>\n",
              "      <th>32</th>\n",
              "      <th>Toko Kelontong</th>\n",
              "      <th>220109</th>\n",
              "      <th>PT. KOTAK AGRO SUBUR</th>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>382655 rows × 1 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xcn79l3TLLtx",
        "outputId": "16551d08-d913-4222-d1ef-8e7bf2f1d446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(541909, 23)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LY-NoQHBLNkY",
        "outputId": "475919b8-a7c4-4c98-953d-97d732e0ea68"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 541909 entries, 0 to 541908\n",
            "Data columns (total 23 columns):\n",
            " #   Column               Non-Null Count   Dtype         \n",
            "---  ------               --------------   -----         \n",
            " 0   InvoiceNo            541909 non-null  object        \n",
            " 1   InvoiceDate          541909 non-null  datetime64[ns]\n",
            " 2   BRANCH_SPLR          541909 non-null  int64         \n",
            " 3   BRANCHNAME_SPLR      541909 non-null  object        \n",
            " 4   warehouseProductsID  541909 non-null  object        \n",
            " 5   BARCODEID            541909 non-null  int64         \n",
            " 6   StockCode            541909 non-null  object        \n",
            " 7   PRODUCT              541909 non-null  object        \n",
            " 8   PRODUCT_CATEGORY     541909 non-null  object        \n",
            " 9   Quantity             541909 non-null  int64         \n",
            " 10  UnitPrice            541909 non-null  float64       \n",
            " 11  UnitPriceRupiah      541909 non-null  float64       \n",
            " 12  oldCUSTID            541909 non-null  object        \n",
            " 13  CustomerID           406829 non-null  float64       \n",
            " 14  CUSTNAME             541909 non-null  object        \n",
            " 15  ADDRESS              541737 non-null  object        \n",
            " 16  KOTA                 525237 non-null  object        \n",
            " 17  PROVINSI             527069 non-null  object        \n",
            " 18  NEGARA               541909 non-null  object        \n",
            " 19  CHANNELID_SPLR       541909 non-null  int64         \n",
            " 20  CHANNELNAME_SPLR     541909 non-null  object        \n",
            " 21  SUBDISTID            541909 non-null  int64         \n",
            " 22  SUBDIST_NAME         541909 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(3), int64(5), object(14)\n",
            "memory usage: 95.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Mengganti kolom PERIODE menjadi tipe data datetime\n",
        "df['InvoiceDate'] = pd.to_datetime(df['InvoiceDate'])"
      ],
      "metadata": {
        "id": "E_WfwLAxKi_2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# melihat range/rentang waktu dari data yang digunakan\n",
        "print('Date Range: %s to %s' % (df['InvoiceDate'].min(), df['InvoiceDate'].max()))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7G8gDLmILiW1",
        "outputId": "4e3f2ed0-eaa0-456b-ccc2-396415c5871a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date Range: 2020-12-01 08:26:00 to 2021-12-09 12:50:00\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sales']=df['Quantity']*df['UnitPriceRupiah']"
      ],
      "metadata": {
        "id": "WK7vZfoOM6Tw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df['Sales']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9oubM_lNJie",
        "outputId": "a9fc2b87-4b80-4a0b-cf9f-64eaea0728e6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         218790.0\n",
              "1         290862.0\n",
              "2         314600.0\n",
              "3         290862.0\n",
              "4         290862.0\n",
              "            ...   \n",
              "541904    145860.0\n",
              "541905    180180.0\n",
              "541906    237380.0\n",
              "541907    237380.0\n",
              "541908    212355.0\n",
              "Name: Sales, Length: 541909, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# mendapat order summary dataset yang menunjukkan total penjualan yang dilakukan per faktur pelanggan (customer invoice)\n",
        "orders_df = df.groupby(['CustomerID','InvoiceNo']).agg({'Sales': sum,'InvoiceDate': max})"
      ],
      "metadata": {
        "id": "8Kk0cnIGML5Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "orders_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "XKbh2fpgNSdo",
        "outputId": "6c18311d-782b-4cd5-bb19-fad8e450a316"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             Sales         InvoiceDate\n",
              "CustomerID InvoiceNo                                  \n",
              "12346.0    541431     1.103725e+09 2021-01-18 10:01:00\n",
              "           C541433   -1.103725e+09 2021-01-18 10:17:00\n",
              "12347.0    537626     1.017860e+07 2020-12-07 14:57:00\n",
              "           542237     6.798077e+06 2021-01-26 14:30:00\n",
              "           549222     9.098375e+06 2021-04-07 10:43:00\n",
              "...                            ...                 ...\n",
              "18283.0    579673     3.197623e+06 2021-11-30 12:59:00\n",
              "           580872     2.974400e+06 2021-12-06 12:02:00\n",
              "18287.0    554065     1.094350e+07 2021-05-22 10:39:00\n",
              "           570715     1.431888e+07 2021-10-12 10:23:00\n",
              "           573167     1.010724e+06 2021-10-28 09:29:00\n",
              "\n",
              "[22190 rows x 2 columns]"
            ],
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          "metadata": {},
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      "execution_count": null,
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          "name": "stdout",
          "text": [
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            "MultiIndex: 22190 entries, (12346.0, 541431) to (18287.0, 573167)\n",
            "Data columns (total 2 columns):\n",
            " #   Column       Non-Null Count  Dtype         \n",
            "---  ------       --------------  -----         \n",
            " 0   Sales        22190 non-null  float64       \n",
            " 1   InvoiceDate  22190 non-null  datetime64[ns]\n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 670.0+ KB\n"
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    {
      "cell_type": "code",
      "source": [
        "# membuat sebuah simple functions yang dapat kita gunakan untuk membuat our summary dataframe\n",
        "def groupby_mean(x):\n",
        "    return x.mean()\n",
        "\n",
        "def groupby_count(x):\n",
        "    return x.count()\n",
        "\n",
        "def purchase_duration(x):\n",
        "    return (x.max() - x.min()).days\n",
        "\n",
        "def avg_frequency(x):\n",
        "    '''returns the average days between sales'''\n",
        "    return (x.max() - x.min()).days/x.count()\n",
        "\n",
        "groupby_mean.__name__ = 'avg'\n",
        "groupby_count.__name__ = 'count'\n",
        "purchase_duration.__name__ = 'purchase_duration'\n",
        "avg_frequency.__name__ = 'purchase_frequency'\n",
        "\n",
        "summary_df = orders_df.reset_index().groupby('CustomerID').agg({'Sales': [min, max, sum, groupby_mean, groupby_count],\\\n",
        "                                                            'InvoiceDate': [min, max, purchase_duration, avg_frequency]})"
      ],
      "metadata": {
        "id": "JyuG3UWZNQx-"
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      "execution_count": null,
      "outputs": []
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      "source": [
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              "                   InvoiceDate                                        \\\n",
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              "CustomerID                                                             \n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5ec25ebb-64b4-433a-867a-6b8286d67b67');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Rename our columns by combinng it with the top row\n",
        "summary_df.columns = ['_'.join(col).lower() for col in summary_df.columns]"
      ],
      "metadata": {
        "id": "FqIdxenwNk19"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "summary_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "6hboywB3NqIe",
        "outputId": "d977a804-a5b7-4b96-bea9-0ac0c58b9123"
      },
      "execution_count": null,
      "outputs": [
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              "               sales_min     sales_max   sales_sum     sales_avg  sales_count  \\\n",
              "CustomerID                                                                      \n",
              "12346.0    -1.103725e+09  1.103725e+09         0.0  0.000000e+00            2   \n",
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              "\n",
              "               invoicedate_min     invoicedate_max  \\\n",
              "CustomerID                                           \n",
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              "CustomerID                                                                 \n",
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              "\n",
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              "        const buttonEl =\n",
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              "\n",
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              "          const element = document.querySelector('#df-b543e131-56a5-458e-a12b-30f73f1269fa');\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": 66
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Visualisasi"
      ],
      "metadata": {
        "id": "YaR8G2bPN30C"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's look at the first 20 sales counts and the average spend for that sale\n",
        "\n",
        "# We firstly group by Sales_Counts and then look at the sales_avg\n",
        "summary_df.groupby('sales_count').count()['sales_avg'][:20]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cReuTd6yNth2",
        "outputId": "36f18c4c-9984-4ece-e415-66bb813f94c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "sales_count\n",
              "1     1313\n",
              "2      817\n",
              "3      490\n",
              "4      377\n",
              "5      288\n",
              "6      196\n",
              "7      157\n",
              "8      117\n",
              "9       80\n",
              "10      78\n",
              "11      62\n",
              "12      51\n",
              "13      38\n",
              "14      41\n",
              "15      28\n",
              "16      26\n",
              "17      19\n",
              "18      22\n",
              "19      16\n",
              "20      14\n",
              "Name: sales_avg, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# summary_df = summary_df.loc[summary_df['invoicedate_purchase_duration'] > 0]\n",
        "summary_df = summary_df.loc[summary_df['invoicedate_purchase_duration'] > 0]\n",
        "summary_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "tEL02_hON53k",
        "outputId": "2fcee0fb-a6e1-4a88-9d82-eb469ced881b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            sales_min   sales_max   sales_sum     sales_avg  sales_count  \\\n",
              "CustomerID                                                                 \n",
              "12347.0     3214926.0  18508776.0  61633000.0  8.804714e+06            7   \n",
              "12348.0     3252392.0  12767040.0  25700532.0  6.425133e+06            4   \n",
              "12352.0    -6632340.0  12016290.0  22099363.0  2.009033e+06           11   \n",
              "12356.0      834405.0  32484166.0  40203449.0  1.340115e+07            3   \n",
              "12358.0     6933498.0   9769760.0  16703258.0  8.351629e+06            2   \n",
              "...               ...         ...         ...           ...          ...   \n",
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              "18277.0     -182325.0   1578434.0   1396109.0  6.980545e+05            2   \n",
              "18282.0      -20735.0   1433003.0   2525380.0  8.417933e+05            3   \n",
              "18283.0       27885.0   4485195.0  29956784.0  1.872299e+06           16   \n",
              "18287.0     1010724.0  14318876.0  26273104.0  8.757701e+06            3   \n",
              "\n",
              "               invoicedate_min     invoicedate_max  \\\n",
              "CustomerID                                           \n",
              "12347.0    2020-12-07 14:57:00 2021-12-07 15:52:00   \n",
              "12348.0    2020-12-16 19:09:00 2021-09-25 13:13:00   \n",
              "12352.0    2021-02-16 12:33:00 2021-11-03 14:37:00   \n",
              "12356.0    2021-01-18 09:50:00 2021-11-17 08:40:00   \n",
              "12358.0    2021-07-12 10:04:00 2021-12-08 10:26:00   \n",
              "...                        ...                 ...   \n",
              "18276.0    2021-10-27 10:54:00 2021-11-18 17:01:00   \n",
              "18277.0    2021-01-25 12:34:00 2021-10-12 15:22:00   \n",
              "18282.0    2021-08-05 13:35:00 2021-12-02 11:43:00   \n",
              "18283.0    2021-01-06 14:14:00 2021-12-06 12:02:00   \n",
              "18287.0    2021-05-22 10:39:00 2021-10-28 09:29:00   \n",
              "\n",
              "            invoicedate_purchase_duration  invoicedate_purchase_frequency  \n",
              "CustomerID                                                                 \n",
              "12347.0                               365                       52.142857  \n",
              "12348.0                               282                       70.500000  \n",
              "12352.0                               260                       23.636364  \n",
              "12356.0                               302                      100.666667  \n",
              "12358.0                               149                       74.500000  \n",
              "...                                   ...                             ...  \n",
              "18276.0                                22                        7.333333  \n",
              "18277.0                               260                      130.000000  \n",
              "18282.0                               118                       39.333333  \n",
              "18283.0                               333                       20.812500  \n",
              "18287.0                               158                       52.666667  \n",
              "\n",
              "[2980 rows x 9 columns]"
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              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12347.0</th>\n",
              "      <td>3214926.0</td>\n",
              "      <td>18508776.0</td>\n",
              "      <td>61633000.0</td>\n",
              "      <td>8.804714e+06</td>\n",
              "      <td>7</td>\n",
              "      <td>2020-12-07 14:57:00</td>\n",
              "      <td>2021-12-07 15:52:00</td>\n",
              "      <td>365</td>\n",
              "      <td>52.142857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12348.0</th>\n",
              "      <td>3252392.0</td>\n",
              "      <td>12767040.0</td>\n",
              "      <td>25700532.0</td>\n",
              "      <td>6.425133e+06</td>\n",
              "      <td>4</td>\n",
              "      <td>2020-12-16 19:09:00</td>\n",
              "      <td>2021-09-25 13:13:00</td>\n",
              "      <td>282</td>\n",
              "      <td>70.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12352.0</th>\n",
              "      <td>-6632340.0</td>\n",
              "      <td>12016290.0</td>\n",
              "      <td>22099363.0</td>\n",
              "      <td>2.009033e+06</td>\n",
              "      <td>11</td>\n",
              "      <td>2021-02-16 12:33:00</td>\n",
              "      <td>2021-11-03 14:37:00</td>\n",
              "      <td>260</td>\n",
              "      <td>23.636364</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12356.0</th>\n",
              "      <td>834405.0</td>\n",
              "      <td>32484166.0</td>\n",
              "      <td>40203449.0</td>\n",
              "      <td>1.340115e+07</td>\n",
              "      <td>3</td>\n",
              "      <td>2021-01-18 09:50:00</td>\n",
              "      <td>2021-11-17 08:40:00</td>\n",
              "      <td>302</td>\n",
              "      <td>100.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12358.0</th>\n",
              "      <td>6933498.0</td>\n",
              "      <td>9769760.0</td>\n",
              "      <td>16703258.0</td>\n",
              "      <td>8.351629e+06</td>\n",
              "      <td>2</td>\n",
              "      <td>2021-07-12 10:04:00</td>\n",
              "      <td>2021-12-08 10:26:00</td>\n",
              "      <td>149</td>\n",
              "      <td>74.500000</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18276.0</th>\n",
              "      <td>-89375.0</td>\n",
              "      <td>4802798.0</td>\n",
              "      <td>4624048.0</td>\n",
              "      <td>1.541349e+06</td>\n",
              "      <td>3</td>\n",
              "      <td>2021-10-27 10:54:00</td>\n",
              "      <td>2021-11-18 17:01:00</td>\n",
              "      <td>22</td>\n",
              "      <td>7.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18277.0</th>\n",
              "      <td>-182325.0</td>\n",
              "      <td>1578434.0</td>\n",
              "      <td>1396109.0</td>\n",
              "      <td>6.980545e+05</td>\n",
              "      <td>2</td>\n",
              "      <td>2021-01-25 12:34:00</td>\n",
              "      <td>2021-10-12 15:22:00</td>\n",
              "      <td>260</td>\n",
              "      <td>130.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18282.0</th>\n",
              "      <td>-20735.0</td>\n",
              "      <td>1433003.0</td>\n",
              "      <td>2525380.0</td>\n",
              "      <td>8.417933e+05</td>\n",
              "      <td>3</td>\n",
              "      <td>2021-08-05 13:35:00</td>\n",
              "      <td>2021-12-02 11:43:00</td>\n",
              "      <td>118</td>\n",
              "      <td>39.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18283.0</th>\n",
              "      <td>27885.0</td>\n",
              "      <td>4485195.0</td>\n",
              "      <td>29956784.0</td>\n",
              "      <td>1.872299e+06</td>\n",
              "      <td>16</td>\n",
              "      <td>2021-01-06 14:14:00</td>\n",
              "      <td>2021-12-06 12:02:00</td>\n",
              "      <td>333</td>\n",
              "      <td>20.812500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18287.0</th>\n",
              "      <td>1010724.0</td>\n",
              "      <td>14318876.0</td>\n",
              "      <td>26273104.0</td>\n",
              "      <td>8.757701e+06</td>\n",
              "      <td>3</td>\n",
              "      <td>2021-05-22 10:39:00</td>\n",
              "      <td>2021-10-28 09:29:00</td>\n",
              "      <td>158</td>\n",
              "      <td>52.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2980 rows × 9 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0229f3ca-609c-4bee-8232-4794f92a32b4')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0229f3ca-609c-4bee-8232-4794f92a32b4 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-0229f3ca-609c-4bee-8232-4794f92a32b4');\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": 69
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Karena kami mencoba untuk mendapatkan waktu antara penjualan (diplot di sel berikutnya). Kita perlu menghilangkan pembelian tunggal. Itulah yang dilakukan baris ini.\n",
        "\n",
        "summary_df = ringkasan_df.loc[summary_df['invoicedate_purchase_duration'] > 0]\n",
        "\n",
        "Durasi pembelian yang sama dengan 0 menunjukkan bahwa hanya pembelian yang pernah dilakukan."
      ],
      "metadata": {
        "id": "Vc5kSpL0OG65"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# We filter on invoicedate_purchase_duration greater than 0\n",
        "summary_df = summary_df.loc[summary_df['invoicedate_purchase_duration'] >= 0]\n",
        "\n",
        "ax = summary_df.groupby('sales_count').count()['sales_avg'][:20].plot(kind='bar', color='skyblue',figsize=(12,7), grid=True)\n",
        "\n",
        "ax.set_ylabel('count')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 623
        },
        "id": "3UFh-Nn4N3GY",
        "outputId": "e98f8298-6ceb-478c-acc5-d10ec222fe3f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we create a distribution for the average times between sales\n",
        "ax = summary_df['invoicedate_purchase_duration'].hist(bins=20, color='skyblue', rwidth=0.7, figsize=(12,7))\n",
        "\n",
        "ax.set_xlabel('avg. number of days between purchases')\n",
        "ax.set_ylabel('count')\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 619
        },
        "id": "3J94KEDwOMvs",
        "outputId": "62c46137-1637-439e-d197-19d9e185c258"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x700 with 1 Axes>"
            ],
            "image/png": 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0AwAAANsntAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJatrQBdB4Xf/yuw2y3isP6dgg6wUAAKhtrrQDAABAooR2AAAASJTb4wEAqBPV+apcftnm6BkRN81bFWX5tfO/pr4qBzQmrrQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiWra0AUAAI3T9S+/W2fLzi/bHD0j4qZ5q6Isv/z/zlx5SMc6Wy8A1DdX2gEAACBRQjsAAAAkqkahfeDAgbF69eptpq9duzYGDhz4WWsCAAAAooahfcaMGbFp06Ztpn/00Ufx5z//+TMXBQAAAOzgg+jmzZuX+/sbb7wRy5cvz73esmVLPProo7HHHnvUXnUAAACwC9uh0H7wwQdHXl5e5OXlVXgbfMuWLePWW2+tteIAAABgV7ZDt8cvXrw4Fi1aFFmWxfPPPx+LFy/O/fnHP/4Ra9eujfPOO6/ay3vqqafihBNOiOLi4sjLy4v777+/3Pxzzjkn90OCrX+GDRtWbsx7770XI0aMiMLCwmjXrl2cf/75sW7duh3ZLAAAAEjSDl1p7969e0RElJWV1crK169fH3379o3zzjsvTj755ArHDBs2LCZNmpR73bx583LzR4wYEcuWLYtp06ZFaWlpnHvuuTFy5MiYMmVKrdQIAAAADWWHQvsnLViwIJ588slYuXLlNiH+mmuuqdYyjjvuuDjuuOMqHdO8efMoKiqqcN6bb74Zjz76aLzwwgtx2GGHRUTErbfeGl/5ylfipz/9aRQXF1erDgBg13b9y+82yHqvPKRjg6wXgJ1HjUL7r371qxg1alR07NgxioqKIi8vLzcvLy+v2qG9OmbMmBGdO3eO3XffPQYOHBj/8R//ER06dIiIiFmzZkW7du1ygT0iYvDgwZGfnx+zZ8+Ok046qcJlbty4MTZu3Jh7vXbt2oiIKC0tjdLS0lqrfUdsXW9V688v21wf5WyjJvtlZ6q1rmpIoRY+G71sPPSy/tXlvwNbl13ROvyblY7q7NfKellTjX2/psp5tn7U1/nq08emc2vtq26deVmWZTu68O7du8e3v/3tuOKKK3a4sO0WkpcX9913XwwfPjw37e67745WrVpFjx49YtGiRfG9730v2rRpE7NmzYomTZrEj370o7jjjjti/vz55ZbVuXPnGD9+fIwaNarCdY0bNy7Gjx+/zfQpU6ZEq1atam2bAAAAoCIbNmyIM844I9asWROFhYXbHVejK+3vv/9+fPWrX61xcdV12mmn5f7eu3fv6NOnT+yzzz4xY8aMGDRoUI2Xe9VVV8XYsWNzr9euXRvdunWLIUOGVLqz6lJpaWlMmzYtjj322CgoKNjuuJvmrarHqv7pkj4ddvg9O1Otta26/SR9etl46GX9q8t/B/LLNsd+S+fEguJ+UZZf/n9n/JuVjurs18p6WVONfb+mynm2ftTX+erTx6Zza+3besd3VWp0ZvzqV78ajz32WHzrW9+qydtr7HOf+1x07NgxFi5cGIMGDYqioqJYuXJluTGbN2+O9957b7vfg4/4+Hvyn36gXUREQUFBg59gqqqhtv4x21E12S87U611JYXPFLVDLxsPvaw/9fHvQFl+023W49+sdOzIfq2olzXV2Pdr6pxn61Z9n6+2HpvOrbWvunXWaC/uu+++cfXVV8dzzz0XvXv33mZlF110UU0WW6W33347Vq1aFV27do2IiJKSkli9enXMmTMn+vXrFxERTzzxRJSVlUX//v3rpAYAAACoLzUK7b/85S+jTZs2MXPmzJg5c2a5eXl5edUO7evWrYuFCxfmXi9evDjmzp0b7du3j/bt28f48ePjlFNOiaKioli0aFFcfvnlse+++8bQoUMjIuKAAw6IYcOGxQUXXBC33357lJaWxpgxY+K0007z5HgAAAB2ejUK7YsXL66Vlb/44otxzDHH5F5v/Z752WefHbfddlvMmzcv7rjjjli9enUUFxfHkCFD4gc/+EG5W9vvuuuuGDNmTAwaNCjy8/PjlFNOiVtuuaVW6gMAAICG1DBfMvj/BgwYEJU9vP5Pf/pTlcto3759TJkypTbLAgAAgCTUKLSfd955lc7/9a9/XaNiAAAAgH+q8a98+6TS0tJ47bXXYvXq1TFw4MBaKQwAAAB2dTUK7ffdd98208rKymLUqFGxzz77fOaiAAAAgIj8WltQfn6MHTs2brrpptpaJAAAAOzSavVBdIsWLYrNmzfX5iIBAKDOXf/yuw2y3isP6dgg6wV2HjUK7Vt/NdtWWZbFsmXL4qGHHoqzzz67VgoDAACAXV2NQvvLL79c7nV+fn506tQpfvazn1X5ZHkAAACgemoU2p988snargMAAAD4lM/0nfZ33nkn5s+fHxERPXv2jE6dOtVKUQAAAEANQ/v69evjwgsvjDvvvDPKysoiIqJJkyZx1llnxa233hqtWrWq1SKhrtX2w2fyyzZHz4i4ad6qKMvf/mHm4TMAAEBlavQr38aOHRszZ86MBx98MFavXh2rV6+OBx54IGbOnBmXXnppbdcIAAAAu6QaXWn/wx/+EL///e9jwIABuWlf+cpXomXLlvG1r30tbrvtttqqDwAA2En5VXrw2dXoSvuGDRuiS5cu20zv3LlzbNiw4TMXBQAAANQwtJeUlMS1114bH330UW7ahx9+GOPHj4+SkpJaKw4AAAB2ZTW6Pf7mm2+OYcOGxZ577hl9+/aNiIhXXnklmjdvHo899litFggAAAC7qhqF9t69e8eCBQvirrvuir/85S8REXH66afHiBEjomXLlrVaIAAAQF3z/XtSVaPQPmHChOjSpUtccMEF5ab/+te/jnfeeSeuuOKKWikOAAAAdmU1+k77L37xi+jVq9c20z//+c/H7bff/pmLAgAAAGoY2pcvXx5du3bdZnqnTp1i2bJln7koAAAAoIahvVu3bvHMM89sM/2ZZ56J4uLiz1wUAAAAUMPvtF9wwQVx8cUXR2lpaQwcODAiIqZPnx6XX355XHrppbVaIAAAAOyqahTaL7vssli1alV8+9vfjk2bNkVERIsWLeKKK66Iq666qlYLBAAAgF1VjUJ7Xl5e/PjHP46rr7463nzzzWjZsmXst99+0bx589quDwAA+AS/mgx2LTUK7Vu1adMmDj/88NqqBQAAAPiEGj2IDgAAAKh7QjsAAAAkSmgHAACARAntAAAAkKjP9CA6AGgMPIkZAEiVK+0AAACQKFfaYSfjiiAAAOw6XGkHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQqKYNXQAAANV3/cvvNsh6rzykY4OsF2BX50o7AAAAJEpoBwAAgEQJ7QAAAJAo32kHiO1/RzS/bHP0jIib5q2KsvzaP2X6jigAAJVxpR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUU0bugAAoPquf/ndBlnvlYd0bJD1AsCuzpV2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABLl97QD7ET8jm4AgF2L0A7UGQFz16b/AACfndvjAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEtWgof2pp56KE044IYqLiyMvLy/uv//+cvOzLItrrrkmunbtGi1btozBgwfHggULyo157733YsSIEVFYWBjt2rWL888/P9atW1ePWwEAAAB1o0FD+/r166Nv374xceLECuffcMMNccstt8Ttt98es2fPjtatW8fQoUPjo48+yo0ZMWJEvP766zFt2rSYOnVqPPXUUzFy5Mj62gQAAACoMw36e9qPO+64OO644yqcl2VZ3HzzzfH9738/TjzxxIiIuPPOO6NLly5x//33x2mnnRZvvvlmPProo/HCCy/EYYcdFhERt956a3zlK1+Jn/70p1FcXFxv2wIAAAC1rUFDe2UWL14cy5cvj8GDB+em7bbbbtG/f/+YNWtWnHbaaTFr1qxo165dLrBHRAwePDjy8/Nj9uzZcdJJJ1W47I0bN8bGjRtzr9euXRsREaWlpVFaWlpHW1S5reutav35ZZvro5xt1GS/7Mq1bl1eVctNodbq2lVrrW4va2pHa20M+7Suba/Wys6zqdVaGbWWX3ZF60it1sqotW7Os/Zr3aiq1orOs6nWWpGdpdb6qvPTx2Zj3qcNpbp15mVZltVxLdWSl5cX9913XwwfPjwiIp599tn40pe+FEuXLo2uXbvmxn3ta1+LvLy8uOeee+JHP/pR3HHHHTF//vxyy+rcuXOMHz8+Ro0aVeG6xo0bF+PHj99m+pQpU6JVq1a1t1EAAABQgQ0bNsQZZ5wRa9asicLCwu2OS/ZKe1266qqrYuzYsbnXa9eujW7dusWQIUMq3Vl1qbS0NKZNmxbHHntsFBQUbHfcTfNW1WNV/3RJnw47/J5dudb8ss2x39I5saC4X5Tlb/8wS6HW6tpVa61uL2tqR2ttDPu0rm2v1srOs6nVWhm1fqyyYzO1Wiuj1ro5z9qvdaOqWis6z6Zaa0V2llrrq85PH5uNeZ82lK13fFcl2dBeVFQUERErVqwod6V9xYoVcfDBB+fGrFy5stz7Nm/eHO+9917u/RVp3rx5NG/efJvpBQUFlQbm+lBVDXURGqqjJvtFrR8vt7Jlp1RrVXb1WqvqZU3taK2NaZ/Wlapqreg8m2qtFVHrtuv49HpSrbUiai0/tra2zX6tG9Wt9ZPn2dRr/aSdpdb6rnPrsdmY92lDqW6dyf6e9h49ekRRUVFMnz49N23t2rUxe/bsKCkpiYiIkpKSWL16dcyZMyc35oknnoiysrLo379/vdcMAAAAtalBr7SvW7cuFi5cmHu9ePHimDt3brRv3z722muvuPjii+M//uM/Yr/99osePXrE1VdfHcXFxbnvvR9wwAExbNiwuOCCC+L222+P0tLSGDNmTJx22mmeHA8AAMBOr0FD+4svvhjHHHNM7vXW75mfffbZMXny5Lj88stj/fr1MXLkyFi9enV8+ctfjkcffTRatGiRe89dd90VY8aMiUGDBkV+fn6ccsopccstt9T7tgAAAEBta9DQPmDAgKjs4fV5eXlx3XXXxXXXXbfdMe3bt48pU6bURXkAAADQoJL9TjsAAADs6oR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIVNKhfdy4cZGXl1fuT69evXLzP/rooxg9enR06NAh2rRpE6ecckqsWLGiASsGAACA2pN0aI+I+PznPx/Lli3L/Xn66adz8y655JJ48MEH4957742ZM2fG0qVL4+STT27AagEAAKD2NG3oAqrStGnTKCoq2mb6mjVr4r//+79jypQpMXDgwIiImDRpUhxwwAHx3HPPxRe+8IX6LhUAAABqVfKhfcGCBVFcXBwtWrSIkpKSmDBhQuy1114xZ86cKC0tjcGDB+fG9urVK/baa6+YNWtWpaF948aNsXHjxtzrtWvXRkREaWlplJaW1t3GVGLreqtaf37Z5vooZxs12S+7cq1bl1fVclOotbp21Vqr28ua2tFaG8M+rWvbq7Wy82xqtVZGreWXXdE6Uqu1Mmqtm/Os/Vo3qqq1ovNsqrVWZGeptb7q/PSx2Zj3aUOpbp15WZZldVxLjT3yyCOxbt266NmzZyxbtizGjx8f//jHP+K1116LBx98MM4999xy4Tsi4ogjjohjjjkmfvzjH293uePGjYvx48dvM33KlCnRqlWrWt8OAAAA+KQNGzbEGWecEWvWrInCwsLtjks6tH/a6tWro3v37nHjjTdGy5YtaxzaK7rS3q1bt3j33Xcr3Vl1qbS0NKZNmxbHHntsFBQUbHfcTfNW1WNV/3RJnw47/J5dudb8ss2x39I5saC4X5Tlb/+GlhRqra5dtdbq9rKmdrTWxrBP69r2aq3sPJtarZVR68cqOzZTq7Uyaq2b86z9WjeqqrWi82yqtVZkZ6m1vur89LHZmPdpQ1m7dm107NixytCe/O3xn9SuXbvYf//9Y+HChXHsscfGpk2bYvXq1dGuXbvcmBUrVlT4HfhPat68eTRv3nyb6QUFBZUG5vpQVQ11ERqqoyb7Ra0fL7eyZadUa1V29Vqr6mVN7WitjWmf1pWqaq3oPJtqrRVR67br+PR6Uq21ImotP7a2ts1+rRvVrfWT59nUa/2knaXW+q5z67HZmPdpQ6lunck/Pf6T1q1bF4sWLYquXbtGv379oqCgIKZPn56bP3/+/FiyZEmUlJQ0YJUAAABQO5K+0v7d7343TjjhhOjevXssXbo0rr322mjSpEmcfvrpsdtuu8X5558fY8eOjfbt20dhYWFceOGFUVJS4snxAAAANApJh/a33347Tj/99Fi1alV06tQpvvzlL8dzzz0XnTp1ioiIm266KfLz8+OUU06JjRs3xtChQ+PnP/95A1cNAAAAtSPp0H733XdXOr9FixYxceLEmDhxYj1VBAAAAPVnp/pOOwAAAOxKhHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKKEdgAAAEiU0A4AAACJEtoBAAAgUUI7AAAAJEpoBwAAgEQJ7QAAAJAooR0AAAASJbQDAABAooR2AAAASJTQDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAcAAIBECe0AAACQKKEdAAAAEiW0AwAAQKIaTWifOHFi7L333tGiRYvo379/PP/88w1dEgAAAHwmjSK033PPPTF27Ni49tpr46WXXoq+ffvG0KFDY+XKlQ1dGgAAANRYowjtN954Y1xwwQVx7rnnxoEHHhi33357tGrVKn796183dGkAAABQYzt9aN+0aVPMmTMnBg8enJuWn58fgwcPjlmzZjVgZQAAAPDZNG3oAj6rd999N7Zs2RJdunQpN71Lly7xl7/8pcL3bNy4MTZu3Jh7vWbNmoiIeO+996K0tLTuiq1EaWlpbNiwIVatWhUFBQXbHbdp7fv1WNU/rVqVt8Pv2ZVrzS/bHBs2bIhNa9+PsvztH2Yp1Fpdu2qt1e1lTe1orY1hn9a17dVa2Xk2tVoro9aPVXZsplZrZdRaN+dZ+7VuVFVrRefZVGutyM5Sa33V+eljszHv04bywQcfRERElmWVjsvLqhqRuKVLl8Yee+wRzz77bJSUlOSmX3755TFz5syYPXv2Nu8ZN25cjB8/vj7LBAAAgG38/e9/jz333HO783f6K+0dO3aMJk2axIoVK8pNX7FiRRQVFVX4nquuuirGjh2be11WVhbvvfdedOjQIfLyGuanMmvXro1u3brF3//+9ygsLGyQGqg9+tl46GXjoZeNi342HnrZeOhl46KfdS/Lsvjggw+iuLi40nE7fWhv1qxZ9OvXL6ZPnx7Dhw+PiI9D+PTp02PMmDEVvqd58+bRvHnzctPatWtXx5VWT2FhoYOiEdHPxkMvGw+9bFz0s/HQy8ZDLxsX/axbu+22W5VjdvrQHhExduzYOPvss+Owww6LI444Im6++eZYv359nHvuuQ1dGgAAANRYowjtp556arzzzjtxzTXXxPLly+Pggw+ORx99dJuH0wEAAMDOpFGE9oiIMWPGbPd2+J1B8+bN49prr93mtn12TvrZeOhl46GXjYt+Nh562XjoZeOin+nY6Z8eDwAAAI1VfkMXAAAAAFRMaAcAAIBECe0AAACQKKEdAAAAEiW0J2LixImx9957R4sWLaJ///7x/PPPN3RJVGHcuHGRl5dX7k+vXr1y8z/66KMYPXp0dOjQIdq0aROnnHJKrFixogErZqunnnoqTjjhhCguLo68vLy4//77y83Psiyuueaa6Nq1a7Rs2TIGDx4cCxYsKDfmvffeixEjRkRhYWG0a9cuzj///Fi3bl09bgVbVdXPc845Z5tjddiwYeXG6GcaJkyYEIcffni0bds2OnfuHMOHD4/58+eXG1Odc+uSJUvi+OOPj1atWkXnzp3jsssui82bN9fnpuzyqtPLAQMGbHNsfutb3yo3Ri8b3m233RZ9+vSJwsLCKCwsjJKSknjkkUdy8x2TO5eq+um4TJPQnoB77rknxo4dG9dee2289NJL0bdv3xg6dGisXLmyoUujCp///Odj2bJluT9PP/10bt4ll1wSDz74YNx7770xc+bMWLp0aZx88skNWC1brV+/Pvr27RsTJ06scP4NN9wQt9xyS9x+++0xe/bsaN26dQwdOjQ++uij3JgRI0bE66+/HtOmTYupU6fGU089FSNHjqyvTeATqupnRMSwYcPKHau//e1vy83XzzTMnDkzRo8eHc8991xMmzYtSktLY8iQIbF+/frcmKrOrVu2bInjjz8+Nm3aFM8++2zccccdMXny5LjmmmsaYpN2WdXpZUTEBRdcUO7YvOGGG3Lz9DINe+65Z1x//fUxZ86cePHFF2PgwIFx4oknxuuvvx4RjsmdTVX9jHBcJimjwR1xxBHZ6NGjc6+3bNmSFRcXZxMmTGjAqqjKtddem/Xt27fCeatXr84KCgqye++9NzftzTffzCIimzVrVj1VSHVERHbfffflXpeVlWVFRUXZT37yk9y01atXZ82bN89++9vfZlmWZW+88UYWEdkLL7yQG/PII49keXl52T/+8Y96q51tfbqfWZZlZ599dnbiiSdu9z36ma6VK1dmEZHNnDkzy7LqnVsffvjhLD8/P1u+fHluzG233ZYVFhZmGzdurN8NIOfTvcyyLDv66KOz73znO9t9j16ma/fdd8/+67/+yzHZSGztZ5Y5LlPlSnsD27RpU8yZMycGDx6cm5afnx+DBw+OWbNmNWBlVMeCBQuiuLg4Pve5z8WIESNiyZIlERExZ86cKC0tLdfXXr16xV577aWviVu8eHEsX768XO9222236N+/f653s2bNinbt2sVhhx2WGzN48ODIz8+P2bNn13vNVG3GjBnRuXPn6NmzZ4waNSpWrVqVm6ef6VqzZk1ERLRv3z4iqndunTVrVvTu3Tu6dOmSGzN06NBYu3ZtuStJ1K9P93Kru+66Kzp27BgHHXRQXHXVVbFhw4bcPL1Mz5YtW+Luu++O9evXR0lJiWNyJ/fpfm7luExP04YuYFf37rvvxpYtW8p98CMiunTpEn/5y18aqCqqo3///jF58uTo2bNnLFu2LMaPHx9HHnlkvPbaa7F8+fJo1qxZtGvXrtx7unTpEsuXL2+YgqmWrf2p6JjcOm/58uXRuXPncvObNm0a7du3198EDRs2LE4++eTo0aNHLFq0KL73ve/FcccdF7NmzYomTZroZ6LKysri4osvji996Utx0EEHRURU69y6fPnyCo/frfOofxX1MiLijDPOiO7du0dxcXHMmzcvrrjiipg/f3788Y9/jAi9TMmrr74aJSUl8dFHH0WbNm3ivvvuiwMPPDDmzp3rmNwJba+fEY7LVAntUEPHHXdc7u99+vSJ/v37R/fu3eN3v/tdtGzZsgErAz7ptNNOy/29d+/e0adPn9hnn31ixowZMWjQoAasjMqMHj06XnvttXLPCmHntL1efvK5Eb17946uXbvGoEGDYtGiRbHPPvvUd5lUomfPnjF37txYs2ZN/P73v4+zzz47Zs6c2dBlUUPb6+eBBx7ouEyU2+MbWMeOHaNJkybbPGVzxYoVUVRU1EBVURPt2rWL/fffPxYuXBhFRUWxadOmWL16dbkx+pq+rf2p7JgsKira5kGRmzdvjvfee09/dwKf+9znomPHjrFw4cKI0M8UjRkzJqZOnRpPPvlk7Lnnnrnp1Tm3FhUVVXj8bp1H/dpeLyvSv3//iIhyx6ZepqFZs2ax7777Rr9+/WLChAnRt2/f+M///E/H5E5qe/2siOMyDUJ7A2vWrFn069cvpk+fnptWVlYW06dPL/fdEtK3bt26WLRoUXTt2jX69esXBQUF5fo6f/78WLJkib4mrkePHlFUVFSud2vXro3Zs2fneldSUhKrV6+OOXPm5MY88cQTUVZWlvvHjXS9/fbbsWrVqujatWtE6GdKsiyLMWPGxH333RdPPPFE9OjRo9z86pxbS0pK4tVXXy33g5hp06ZFYWFh7vZP6l5VvazI3LlzIyLKHZt6maaysrLYuHGjY7KR2NrPijguE9HQT8Ijy+6+++6sefPm2eTJk7M33ngjGzlyZNauXbtyT2UkPZdeemk2Y8aMbPHixdkzzzyTDR48OOvYsWO2cuXKLMuy7Fvf+la21157ZU888UT24osvZiUlJVlJSUkDV02WZdkHH3yQvfzyy9nLL7+cRUR24403Zi+//HL2t7/9LcuyLLv++uuzdu3aZQ888EA2b9687MQTT8x69OiRffjhh7llDBs2LDvkkEOy2bNnZ08//XS23377ZaeffnpDbdIurbJ+fvDBB9l3v/vdbNasWdnixYuzxx9/PDv00EOz/fbbL/voo49yy9DPNIwaNSrbbbfdshkzZmTLli3L/dmwYUNuTFXn1s2bN2cHHXRQNmTIkGzu3LnZo48+mnXq1Cm76qqrGmKTdllV9XLhwoXZddddl7344ovZ4sWLswceeCD73Oc+lx111FG5ZehlGq688sps5syZ2eLFi7N58+ZlV155ZZaXl5c99thjWZY5Jnc2lfXTcZkuoT0Rt956a7bXXntlzZo1y4444ojsueeea+iSqMKpp56ade3aNWvWrFm2xx57ZKeeemq2cOHC3PwPP/ww+/a3v53tvvvuWatWrbKTTjopW7ZsWQNWzFZPPvlkFhHb/Dn77LOzLPv4175dffXVWZcuXbLmzZtngwYNyubPn19uGatWrcpOP/30rE2bNllhYWF27rnnZh988EEDbA2V9XPDhg3ZkCFDsk6dOmUFBQVZ9+7dswsuuGCbH4rqZxoq6mNEZJMmTcqNqc659a233sqOO+64rGXLllnHjh2zSy+9NCstLa3nrdm1VdXLJUuWZEcddVTWvn37rHnz5tm+++6bXXbZZdmaNWvKLUcvG955552Xde/ePWvWrFnWqVOnbNCgQbnAnmWOyZ1NZf10XKYrL8uyrP6u6wMAAADV5TvtAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAiRLaAQAAIFFCOwAAACRKaAeAT5kxY0bk5eXF6tWrG7qUnGeeeSZ69+4dBQUFMXz48Gq/b++9946bb765zuqqyjnnnLND9VJ7xo0bFwcffHBDlwHAZyS0A8BOYOzYsXHwwQfH4sWLY/LkyQ1dTr1K8YcoAFBfhHYAqCebNm2q8XsXLVoUAwcOjD333DPatWtXe0WxU/osnyUAdi5COwDlPProo/HlL3852rVrFx06dIh/+Zd/iUWLFuXmf/GLX4wrrrii3HveeeedKCgoiKeeeioiIpYtWxbHH398tGzZMnr06BFTpkzZ4du0t15dnT59ehx22GHRqlWr+OIXvxjz58/Pjano1uuLL744BgwYkHs9YMCAuPDCC+Piiy+O3XffPbp06RK/+tWvYv369XHuuedG27ZtY999941HHnlkmxqeeeaZ6NOnT7Ro0SK+8IUvxGuvvVZu/tNPPx1HHnlktGzZMrp16xYXXXRRrF+/Pjd/7733jh/84Adx1llnRWFhYYwcObLCbd24cWNcdNFF0blz52jRokV8+ctfjhdeeCEiIt56663Iy8uLVatWxXnnnRd5eXnbvdK+cuXKOOGEE3L7/a677tpmzI033hi9e/eO1q1bR7du3eLb3/52rFu3LiIi1q9fH4WFhfH73/++3Hvuv//+aN26dXzwwQexadOmGDNmTHTt2jVatGgR3bt3jwkTJlRYzyeNHz8+OnXqFIWFhfGtb32rXOgsKyuLCRMmRI8ePaJly5bRt2/fXA1vvfVWHHPMMRERsfvuu0deXl6cc845MXXq1GjXrl1s2bIlIiLmzp0beXl5ceWVV+aW+41vfCO+/vWv515X1a+NGzfGd7/73dhjjz2idevW0b9//5gxY0Zu/uTJk6Ndu3bxpz/9KQ444IBo06ZNDBs2LJYtW7bd7d76OX7ooYe2+1mq6Db2m2++Ofbee+/c662f9R/+8IdRXFwcPXv2jIiIt99+O04//fRo3759tG7dOg477LCYPXt2uWX95je/ib333jt22223OO200+KDDz7IzavqeK+q36tXr45vfOMbud4OHDgwXnnlldz8V155JY455pho27ZtFBYWRr9+/eLFF1/c7v4CYFtCOwDlrF+/PsaOHRsvvvhiTJ8+PfLz8+Okk06KsrKyiIgYMWJE3H333ZFlWe4999xzTxQXF8eRRx4ZERFnnXVWLF26NGbMmBF/+MMf4pe//GWsXLmyRvX8+7//e/zsZz+LF198MZo2bRrnnXfeDi/jjjvuiI4dO8bzzz8fF154YYwaNSq++tWvxhe/+MV46aWXYsiQIXHmmWfGhg0byr3vsssui5/97GfxwgsvRKdOneKEE06I0tLSiPj4yvewYcPilFNOiXnz5sU999wTTz/9dIwZM6bcMn76059G37594+WXX46rr766wvouv/zy+MMf/hB33HFHvPTSS7HvvvvG0KFD47333otu3brFsmXLorCwMG6++eZYtmxZnHrqqRUu55xzzom///3v8eSTT8bvf//7+PnPf77Nfs/Pz49bbrklXn/99bjjjjviiSeeiMsvvzwiIlq3bh2nnXZaTJo0qdx7Jk2aFP/2b/8Wbdu2jVtuuSX+93//N373u9/F/Pnz46677ioXLisyffr0ePPNN2PGjBnx29/+Nv74xz/G+PHjc/MnTJgQd955Z9x+++3x+uuvxyWXXBJf//rXY+bMmdGtW7f4wx/+EBER8+fPj2XLlsV//ud/xpFHHhkffPBBvPzyyxERMXPmzOjYsWO5kD1z5szcD3Cq068xY8bErFmz4u6774558+bFV7/61Rg2bFgsWLAgN2bDhg3x05/+NH7zm9/EU089FUuWLInvfve7lW5/ROWfpeqaPn16zJ8/P6ZNmxZTp06NdevWxdFHHx3/+Mc/4n//93/jlVdeicsvvzx3rG7d7vvvvz+mTp0aU6dOjZkzZ8b111+fm1/V8V5Vv7/61a/GypUr45FHHok5c+bEoYceGoMGDYr33nsvIj4+X+y5557xwgsvxJw5c+LKK6+MgoKCHdpugF1eBgCVeOedd7KIyF599dUsy7Js5cqVWdOmTbOnnnoqN6akpCS74oorsizLsjfffDOLiOyFF17IzV+wYEEWEdlNN91U7fU++eSTWURkjz/+eG7aQw89lEVE9uGHH2ZZlmVnn312duKJJ5Z733e+853s6KOPzr0++uijsy9/+cu515s3b85at26dnXnmmblpy5YtyyIimzVrVrl133333bkxq1atylq2bJndc889WZZl2fnnn5+NHDmy3Lr//Oc/Z/n5+bn6unfvng0fPrzS7Vy3bl1WUFCQ3XXXXblpmzZtyoqLi7MbbrghN2233XbLJk2atN3lzJ8/P4uI7Pnnn89N29qLyvb7vffem3Xo0CH3evbs2VmTJk2ypUuXZlmWZStWrMiaNm2azZgxI8uyLLvwwguzgQMHZmVlZZVu11Znn3121r59+2z9+vW5abfddlvWpk2bbMuWLdlHH32UtWrVKnv22WfLve/888/PTj/99CzL/tmP999/v9yYQw89NPvJT36SZVmWDR8+PPvhD3+YNWvWLPvggw+yt99+O4uI7K9//WtueZX1629/+1vWpEmT7B//+Ee5MYMGDcquuuqqLMuybNKkSVlEZAsXLszNnzhxYtalS5ftbn91PkvXXntt1rdv33Lvu+mmm7Lu3buX249dunTJNm7cmJv2i1/8Imvbtm22atWqCtd97bXXZq1atcrWrl2bm3bZZZdl/fv33269nz7eK+v3n//856ywsDD76KOPyk3fZ599sl/84hdZlmVZ27Zts8mTJ293fQBUzZV2AMpZsGBBnH766fG5z30uCgsLc1fVlixZEhERnTp1iiFDhuRuvV68eHHMmjUrRowYEREfXw1t2rRpHHroobll7rvvvrH77rvXqJ4+ffrk/t61a9eIiB2+av/JZTRp0iQ6dOgQvXv3zk3r0qVLhcstKSnJ/b19+/bRs2fPePPNNyPi49t+J0+eHG3atMn9GTp0aJSVlcXixYtz7zvssMMqrW3RokVRWloaX/rSl3LTCgoK4ogjjsitqzrefPPNaNq0afTr1y83rVevXtt8//3xxx+PQYMGxR577BFt27aNM888M1atWpW7y+CII46Iz3/+83HHHXdERMT//M//RPfu3eOoo46KiI+v5s+dOzd69uwZF110UTz22GNV1ta3b99o1apV7nVJSUmsW7cu/v73v8fChQtjw4YNceyxx5bbl3feeWe527QrcvTRR8eMGTMiy7L485//HCeffHIccMAB8fTTT8fMmTOjuLg49ttvv4ioul+vvvpqbNmyJfbff/9yY2bOnFmujlatWsU+++yTe921a9dqfR4r+yxVV+/evaNZs2a513Pnzo1DDjkk2rdvv9337L333tG2bdvt1lvV8V5Zv1955ZVYt25ddOjQodw+W7x4cW6fjR07Nr7xjW/E4MGD4/rrr6+ypwBsq2lDFwBAWk444YTo3r17/OpXv4ri4uIoKyuLgw46qNx3kEeMGBEXXXRR3HrrrTFlypTo3bt3uRBcmz55K21eXl5ERO7W3fz8/HK36UdEhbccf/p23Ly8vEqXWx3r1q2Lb37zm3HRRRdtM2+vvfbK/b1169bVXmZde+utt+Jf/uVfYtSoUfHDH/4w2rdvH08//XScf/75sWnTplyw/sY3vhETJ06MK6+8MiZNmhTnnntubh8deuihsXjx4njkkUfi8ccfj6997WsxePDgbb4HX11bv0//0EMPxR577FFuXvPmzSt974ABA+LXv/51vPLKK1FQUBC9evWKAQMGxIwZM+L999+Po48+utx6KuvXvHnzokmTJjFnzpxo0qRJuflt2rTJ/b2iz9KnP4M7qrqf409/llq2bFnlsiuq95Of86qO98r6vW7duujatWu5ryRstfWHRePGjYszzjgjHnrooXjkkUfi2muvjbvvvjtOOumkKmsH4GNCOwA5q1ativnz58evfvWr3PfTn3766W3GnXjiiTFy5Mh49NFHY8qUKXHWWWfl5vXs2TM2b94cL7/8cu6q78KFC+P999+v9Xo7deq0zcPh5s6dW2vfmX3uuedyAfz999+Pv/71r3HAAQdExMdh5o033oh99933M61jn332iWbNmsUzzzwT3bt3j4iPA9sLL7wQF198cbWX06tXr9i8eXPMmTMnDj/88Ij4+K6HT/6atDlz5kRZWVn87Gc/i/z8j2+2+93vfrfNsr7+9a/H5ZdfHrfccku88cYbcfbZZ5ebX1hYGKeeemqceuqp8W//9m8xbNiweO+997Z7xfeVV16JDz/8MBcyn3vuuWjTpk1069Yt2rdvH82bN48lS5aUC9mftPXq8taHzm219XvtN910U+69AwYMiOuvvz7ef//9uPTSS3Njq+rXIYccElu2bImVK1fmPvu1qbLPUqdOnWL58uWRZVnuhyNz586tcpl9+vSJ//qv/6p031emusf79vp96KGHxvLly6Np06aVPtdg//33j/333z8uueSSOP3002PSpElCO8AOcHs8ADm77757dOjQIX75y1/GwoUL44knnoixY8duM65169YxfPjwuPrqq+PNN9+M008/PTevV69eMXjw4Bg5cmQ8//zz8fLLL8fIkSOjZcuWuUAS8fHD6q666qrPVO/AgQPjxRdfjDvvvDMWLFgQ11577TYh/rO47rrrYvr06fHaa6/FOeecEx07dsw9rf6KK66IZ599NsaMGRNz586NBQsWxAMPPLDNg+iq0rp16xg1alRcdtll8eijj8Ybb7wRF1xwQWzYsCHOP//8ai+nZ8+eMWzYsPjmN78Zs2fPjjlz5sQ3vvGNcldj99133ygtLY1bb701/u///i9+85vfxO23377Nsnbfffc4+eST47LLLoshQ4bEnnvumZt34403xm9/+9v4y1/+En/961/j3nvvjaKiokp/Dd2mTZvi/PPPjzfeeCMefvjhuPbaa2PMmDGRn58fbdu2je9+97txySWXxB133BGLFi2Kl156KW699dbcLfrdu3ePvLy8mDp1arzzzju5q/O777579OnTJ+66667cA+eOOuqoeOmll+Kvf/1ruR8CVNWv/fffP0aMGBFnnXVW/PGPf4zFixfH888/HxMmTIiHHnqo2n3Ynso+SwMGDIh33nknbrjhhli0aFFMnDixwt9m8Gmnn356FBUVxfDhw+OZZ56J//u//4s//OEPMWvWrGrVVJ3jvbJ+Dx48OEpKSmL48OHx2GOPxVtvvRXPPvts/Pu//3u8+OKL8eGHH8aYMWNixowZ8be//S2eeeaZeOGFF3I/rACgeoR2AHLy8/Pj7rvvjjlz5sRBBx0Ul1xySfzkJz+pcOyIESPilVdeiSOPPLLc7eAREXfeeWd06dIljjrqqDjppJPiggsuiLZt20aLFi1yY5YsWVLpr8qqjqFDh8bVV18dl19+eRx++OHxwQcflLvq/1ldf/318Z3vfCf69esXy5cvjwcffDB31bdPnz4xc+bM+Otf/xpHHnlkHHLIIXHNNddEcXFxjdZzyimnxJlnnhmHHnpoLFy4MP70pz/t8HMAJk2aFMXFxXH00UfHySefHCNHjozOnTvn5vft2zduvPHG+PGPfxwHHXRQ3HXXXdv9dW1bb5n/9NP627ZtGzfccEMcdthhcfjhh8dbb70VDz/8cO7KfUUGDRoU++23Xxx11FFx6qmnxr/+67/GuHHjcvN/8IMfxNVXXx0TJkyIAw44IIYNGxYPPfRQ9OjRIyIi9thjjxg/fnxceeWV0aVLl3I/GDn66KNjy5YtudDevn37OPDAA6OoqCj3a9EiqtevSZMmxVlnnRWXXnpp9OzZM4YPHx4vvPDCNp/vmqjss3TAAQfEz3/+85g4cWL07ds3nn/++Wo9kb5Zs2bx2GOPRefOneMrX/lK9O7dO66//vptbu/fnuoc75X1Oy8vLx5++OE46qij4txzz439998/TjvttPjb3/4WXbp0iSZNmsSqVavirLPOiv333z++9rWvxXHHHVfuNwcAULW87LN+EQsAqvD2229Ht27dcg9BI32/+c1v4pJLLomlS5eWe/gZO2bGjBlxzDHHxPvvv1/p3QgAsD2+0w5ArXviiSdi3bp10bt371i2bFlcfvnlsffee+eeQE66NmzYEMuWLYvrr78+vvnNbwrsANDA3B4PQK0rLS2N733ve/H5z38+TjrppOjUqVPMmDGj1h4QR9254YYbolevXlFUVPSZnzkAAHx2bo8HAACARLnSDgAAAIkS2gEAACBRQjsAAAAkSmgHAACARAntAAAAkCihHQAAABIltAMAAECihHYAAABIlNAOAAAAifp/LlBip+HZfZQAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Predicting the 3 month Customer Lifetime Value"
      ],
      "metadata": {
        "id": "rYdbmuDlOkig"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "orders_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 238
        },
        "id": "Pxzc1F-SOWTC",
        "outputId": "74ae2d2e-ddb3-414d-fbf0-bfdc20ff33da"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                             Sales         InvoiceDate\n",
              "CustomerID InvoiceNo                                  \n",
              "12346.0    541431     1.103725e+09 2021-01-18 10:01:00\n",
              "           C541433   -1.103725e+09 2021-01-18 10:17:00\n",
              "12347.0    537626     1.017860e+07 2020-12-07 14:57:00\n",
              "           542237     6.798077e+06 2021-01-26 14:30:00\n",
              "           549222     9.098375e+06 2021-04-07 10:43:00"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7b9ae5d-aefa-49e3-a9ed-591c738c9283\">\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></th>\n",
              "      <th>Sales</th>\n",
              "      <th>InvoiceDate</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th>InvoiceNo</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">12346.0</th>\n",
              "      <th>541431</th>\n",
              "      <td>1.103725e+09</td>\n",
              "      <td>2021-01-18 10:01:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>C541433</th>\n",
              "      <td>-1.103725e+09</td>\n",
              "      <td>2021-01-18 10:17:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">12347.0</th>\n",
              "      <th>537626</th>\n",
              "      <td>1.017860e+07</td>\n",
              "      <td>2020-12-07 14:57:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>542237</th>\n",
              "      <td>6.798077e+06</td>\n",
              "      <td>2021-01-26 14:30:00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>549222</th>\n",
              "      <td>9.098375e+06</td>\n",
              "      <td>2021-04-07 10:43:00</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7b9ae5d-aefa-49e3-a9ed-591c738c9283')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  "
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          "metadata": {},
          "execution_count": 74
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create summary statistics in 3 month intervals for customer Sales\n",
        "\n",
        "# Use 3M for 3 month\n",
        "clv_freq = '3M'\n",
        "\n",
        "# Groupby customer ID now, and group all invoices for 3 month cycles.\n",
        "# Aggregate on sum, mean and counts\n",
        "data_df = orders_df.reset_index().groupby(['CustomerID', pd.Grouper(key='InvoiceDate', freq=clv_freq)]).agg({\n",
        "    'Sales': [sum, groupby_mean, groupby_count],})\n",
        "\n",
        "data_df.columns = ['_'.join(col).lower() for col in data_df.columns]\n",
        "data_df = data_df.reset_index()\n",
        "data_df.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "id": "QdtStQAXOn8r",
        "outputId": "f229ea95-c4eb-4bf8-de02-51dff7ec0305"
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      "execution_count": null,
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          "output_type": "execute_result",
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            "text/plain": [
              "   CustomerID InvoiceDate   sales_sum   sales_avg  sales_count\n",
              "0     12346.0  2021-03-31         0.0         0.0            2\n",
              "1     12347.0  2020-12-31  10178597.0  10178597.0            1\n",
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              "3     12347.0  2021-06-30  14568411.0   7284205.5            2\n",
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              "                                                     [key], {});\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "# Adding Recency 'M'\n",
        "\n",
        "# Get the M\n",
        "date_month_map = {str(x)[:10]: 'M_%s' % (i+1) for i, x in enumerate(sorted(data_df.reset_index()['InvoiceDate'].unique(), reverse=True))}\n",
        "\n",
        "# Create our M Column\n",
        "data_df['M'] = data_df['InvoiceDate'].apply(lambda x: date_month_map[str(x)[:10]])\n",
        "data_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "FWKGqHKKOqYD",
        "outputId": "4fd7a023-4c30-48c0-9f3a-aa6809ffb222"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID InvoiceDate   sales_sum   sales_avg  sales_count    M\n",
              "0     12346.0  2021-03-31         0.0         0.0            2  M_4\n",
              "1     12347.0  2020-12-31  10178597.0  10178597.0            1  M_5\n",
              "2     12347.0  2021-03-31   6798077.0   6798077.0            1  M_4\n",
              "3     12347.0  2021-06-30  14568411.0   7284205.5            2  M_3\n",
              "4     12347.0  2021-09-30   8364213.0   8364213.0            1  M_2"
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              "      <td>8364213.0</td>\n",
              "      <td>8364213.0</td>\n",
              "      <td>1</td>\n",
              "      <td>M_2</td>\n",
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              "  </tbody>\n",
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              "  </style>\n",
              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-2d01d6c8-4a01-4620-8050-b212fe1b3600 button.colab-df-convert');\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_df['M'].unique()"
      ],
      "metadata": {
        "colab": {
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        },
        "id": "3SE3ow9SO4FB",
        "outputId": "841ac514-908b-4a3b-f2ef-0327b285b97d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['M_4', 'M_5', 'M_3', 'M_2', 'M_1'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**RFM**\n",
        "\n",
        "By definition, RFM represents:\n",
        "\n",
        "- R(ecency): how recently did customer purchase?\n",
        "- F(rquency): how often do customer purchase?\n",
        "- M(onetary Value): how much do they spend (each time on average)?"
      ],
      "metadata": {
        "id": "K5bep4T7PCrD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create our Features DF\n",
        "features_df = pd.pivot_table(data_df.loc[data_df['M'] != 'M_1'], values=['sales_sum', 'sales_avg', 'sales_count'], columns='M', index='CustomerID')\n",
        "\n",
        "features_df.columns = ['_'.join(col) for col in features_df.columns]\n",
        "\n",
        "features_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "id": "d-iwYSvqO-jS",
        "outputId": "eee49663-96bb-4c6a-d6b6-3c7693e9bc99"
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      "execution_count": null,
      "outputs": [
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          "data": {
            "text/plain": [
              "            sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
              "CustomerID                                                               \n",
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              "\n",
              "            sales_count_M_2  sales_count_M_3  sales_count_M_4  \\\n",
              "CustomerID                                                      \n",
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              "\n",
              "            sales_count_M_5  sales_sum_M_2  sales_sum_M_3  sales_sum_M_4  \\\n",
              "CustomerID                                                                 \n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create our Response DF\n",
        "response_df = data_df.loc[data_df['M'] == 'M_1', ['CustomerID', 'sales_sum']]\n",
        "\n",
        "response_df.columns = ['CustomerID', 'CLV_'+clv_freq]\n",
        "response_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "gLcV3m_EPFYd",
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      },
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    CustomerID      CLV_3M\n",
              "5      12347.0  21723702.0\n",
              "10     12349.0  25132965.0\n",
              "14     12352.0   4457739.0\n",
              "20     12356.0    834405.0\n",
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              "\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create our Sample Set by merging features with response_df\n",
        "sample_set_df = features_df.merge(\n",
        "    response_df,\n",
        "    left_index=True,\n",
        "    right_on='CustomerID',\n",
        "    how='left'\n",
        ")\n",
        "\n",
        "sample_set_df = sample_set_df.fillna(0)\n",
        "sample_set_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "RGXtChTBPO9w",
        "outputId": "8400ebaa-4c9d-4fac-b5c2-abace3b282cc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        sales_avg_M_2  sales_avg_M_3  sales_avg_M_4  sales_avg_M_5  \\\n",
              "NaN      0.000000e+00            0.0   0.000000e+00            0.0   \n",
              "5.0      8.364213e+06      7284205.5   6.798077e+06     10178597.0   \n",
              "NaN      4.433000e+06      5248100.0   3.252392e+06     12767040.0   \n",
              "NaN      0.000000e+00            0.0   4.781920e+06            0.0   \n",
              "14.0     4.522375e+06            0.0   1.074609e+06            0.0   \n",
              "...               ...            ...            ...            ...   \n",
              "NaN      0.000000e+00            0.0   2.582580e+06            0.0   \n",
              "NaN      0.000000e+00      1155726.0   0.000000e+00            0.0   \n",
              "9574.0   7.061340e+05            0.0   0.000000e+00            0.0   \n",
              "9578.0   1.325562e+06      1875731.0   1.515323e+06            0.0   \n",
              "9580.0   0.000000e+00     10943504.0   0.000000e+00            0.0   \n",
              "\n",
              "        sales_count_M_2  sales_count_M_3  sales_count_M_4  sales_count_M_5  \\\n",
              "NaN                 0.0              0.0              2.0              0.0   \n",
              "5.0                 1.0              2.0              1.0              1.0   \n",
              "NaN                 1.0              1.0              1.0              1.0   \n",
              "NaN                 0.0              0.0              1.0              0.0   \n",
              "14.0                2.0              0.0              8.0              0.0   \n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a4828c79-989d-4007-9c84-792f38d9538c')\"\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-a4828c79-989d-4007-9c84-792f38d9538c');\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",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sample_set_df.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NIJX4i8HSkZl",
        "outputId": "4a820c13-d43c-49b3-870a-98de8973b5a4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3658, 14)"
            ]
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Using Linear Regression"
      ],
      "metadata": {
        "id": "WgRGBKenPX-W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Linear regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "# Our Target Variable\n",
        "target_var = 'CLV_'+clv_freq\n",
        "\n",
        "# Our Features\n",
        "all_features = [x for x in sample_set_df.columns if x not in ['CUSTID', target_var]]\n",
        "\n",
        "# Split data in Train and Test\n",
        "x_train, x_test, y_train, y_test = train_test_split(sample_set_df[all_features], sample_set_df[target_var], test_size=0.3)\n",
        "\n",
        "# Fit Model\n",
        "reg_fit = LinearRegression()\n",
        "reg_fit.fit(x_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "_RymTg1UPZwa",
        "outputId": "2cd0b3ab-4357-4b08-a73f-824d6f724ed3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ],
            "text/html": [
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            ]
          },
          "metadata": {},
          "execution_count": 85
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Get our y intercept\n",
        "reg_fit.intercept_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vOW__BsDPSOY",
        "outputId": "40916112-df06-4556-c0a8-da93a1c348b7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3189707.4404336866"
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "1. Intersep (intercept) Intersep, definisi secara metematis adalah suatu titik perpotongan antara suatu garis dengan sumbu Y pada diagram/sumbu kartesius saat nilai X = 0. Sedangkan definisi secara statistika adalah nilai rata-ratapada variabel Y apabila nilai pada variabel X bernilai 0."
      ],
      "metadata": {
        "id": "qW7DbEzwTOMm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Get our coefficents\n",
        "reg_fit.coef_"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TKnHNmGLPdyi",
        "outputId": "ef489e12-36d2-4692-d4fc-d511fcf0b491"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.22535478e-01, -4.57256532e-01,  1.19873202e-01,  4.16216686e-02,\n",
              "        2.07283480e+06, -6.24752966e+05, -1.05165036e+06, -2.87977184e+05,\n",
              "        1.35627966e-01,  5.26305610e-01,  2.49765988e-01,  6.51105024e-01,\n",
              "       -1.94536020e+02])"
            ]
          },
          "metadata": {},
          "execution_count": 87
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Show them here per feature\n",
        "coef = pd.DataFrame(list(zip(all_features, reg_fit.coef_)))\n",
        "coef.columns = ['feature', 'coef']\n",
        "\n",
        "coef"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 457
        },
        "id": "OZOQ4JJMPfWz",
        "outputId": "ef895d75-4de6-4c95-d645-8f13578584ee"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            feature          coef\n",
              "0     sales_avg_M_2  2.225355e-01\n",
              "1     sales_avg_M_3 -4.572565e-01\n",
              "2     sales_avg_M_4  1.198732e-01\n",
              "3     sales_avg_M_5  4.162167e-02\n",
              "4   sales_count_M_2  2.072835e+06\n",
              "5   sales_count_M_3 -6.247530e+05\n",
              "6   sales_count_M_4 -1.051650e+06\n",
              "7   sales_count_M_5 -2.879772e+05\n",
              "8     sales_sum_M_2  1.356280e-01\n",
              "9     sales_sum_M_3  5.263056e-01\n",
              "10    sales_sum_M_4  2.497660e-01\n",
              "11    sales_sum_M_5  6.511050e-01\n",
              "12       CustomerID -1.945360e+02"
            ],
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              "\n",
              "  <div id=\"df-38f1c6df-707f-4c88-979c-ff5fb590d965\">\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",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>feature</th>\n",
              "      <th>coef</th>\n",
              "    </tr>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>sales_avg_M_2</td>\n",
              "      <td>2.225355e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>sales_avg_M_3</td>\n",
              "      <td>-4.572565e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>sales_avg_M_4</td>\n",
              "      <td>1.198732e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>sales_avg_M_5</td>\n",
              "      <td>4.162167e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>sales_count_M_2</td>\n",
              "      <td>2.072835e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>sales_count_M_3</td>\n",
              "      <td>-6.247530e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>sales_count_M_4</td>\n",
              "      <td>-1.051650e+06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>sales_count_M_5</td>\n",
              "      <td>-2.879772e+05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>sales_sum_M_2</td>\n",
              "      <td>1.356280e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>sales_sum_M_3</td>\n",
              "      <td>5.263056e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>sales_sum_M_4</td>\n",
              "      <td>2.497660e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>sales_sum_M_5</td>\n",
              "      <td>6.511050e-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>CustomerID</td>\n",
              "      <td>-1.945360e+02</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
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              "\n",
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              "      width: 32px;\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
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              "      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-38f1c6df-707f-4c88-979c-ff5fb590d965 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-38f1c6df-707f-4c88-979c-ff5fb590d965');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Run our input data into our model predictors\n",
        "train_preds = reg_fit.predict(x_train)\n",
        "test_preds = reg_fit.predict(x_test)"
      ],
      "metadata": {
        "id": "4n9QcDMNPhwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Assess performance Training Data\n",
        "from sklearn.metrics import r2_score, median_absolute_error\n",
        "\n",
        "print(r2_score(y_true=y_train, y_pred=train_preds))\n",
        "print(median_absolute_error(y_true=y_train, y_pred=train_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhQ6Tuu6PkyE",
        "outputId": "4778718d-429b-4547-a8ca-417d37eb8b01"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.6688446864429648\n",
            "3283773.8007978923\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare R-Squared Performance on Training and Test Datasets\n",
        "print('In-Sample R-Squared: %0.4f' % r2_score(y_true = y_train, y_pred = train_preds))\n",
        "print('Out-of-Sample R-Squared: %0.4f' % r2_score(y_true = y_test, y_pred = test_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5ezdB-2gPmZM",
        "outputId": "0558a84a-bcf8-4bb2-e172-265d2f2746e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In-Sample R-Squared: 0.6688\n",
            "Out-of-Sample R-Squared: 0.8941\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare MSE on Training and Test Datasets\n",
        "print('In-Sample MSE: %0.4f' % median_absolute_error(y_true = y_train, y_pred = train_preds))\n",
        "print('Out-of-Sample MSE: %0.4f' % median_absolute_error(y_true = y_test, y_pred = test_preds))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4dhAF3WKPrKo",
        "outputId": "ae58fb8a-541a-492a-9a8c-c10fe6a8a9ad"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "In-Sample MSE: 3283773.8008\n",
            "Out-of-Sample MSE: 3365947.9441\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plot the predicted vs actual values using our Test Dataset\n",
        "plt.scatter(y_test, test_preds)\n",
        "plt.plot([0, max(y_test)], [0, max(test_preds)], color='gray', lw=1, linestyle='--')\n",
        "\n",
        "plt.xlabel('actual')\n",
        "plt.ylabel('predicted')\n",
        "plt.title('Out-of-Sample Actual vs. Predicted')\n",
        "plt.grid()\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "yF8BYA3GPoSH",
        "outputId": "c92bff29-21c7-4e4d-c595-506ca3979793"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "f5VLhkgUPtn9"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}