Otniel_Abiezer.ipynb

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  "metadata": {
    "colab": {
      "name": "Rakamin_Homework.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "VRrYn2ZNb2iQ",
        "e2n_Kmh2upsl",
        "DBb4JqWwO0JF"
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      "display_name": "Python 3",
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    "language_info": {
      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Obt0kqSJXbKS"
      },
      "source": [
        "# Homework "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ebf52fe7"
      },
      "source": [
        "## Load Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k7WubxwY1H_N"
      },
      "source": [
        "Import Library"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sc3D46Dm_3np"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ],
      "execution_count": 235,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hja6wfLL1hNT"
      },
      "source": [
        "Import Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6c4b0183"
      },
      "source": [
        "#Direktori Dataset disesuaikan\n",
        "\n",
        "url = 'https://drive.google.com/file/d/1r0qXP_unNdthoUNSrh1z8CfBwWFko_w6/view?usp=sharing'\n",
        "url2= 'https://drive.google.com/uc?id=' + url.split('/')[-2]\n",
        "\n",
        "df_dataset = pd.read_csv(url2)"
      ],
      "execution_count": 236,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h5LNXYON1n-U"
      },
      "source": [
        "Mengambil 10 data acak"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "Umw_hNvpB9Jf",
        "outputId": "91fe4ef3-49b1-4fd7-df46-7c259b2f953c"
      },
      "source": [
        "df_dataset.sample(10)"
      ],
      "execution_count": 237,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\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>Invoice</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>Price</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>434350</th>\n",
              "      <td>530855</td>\n",
              "      <td>84946</td>\n",
              "      <td>ANTIQUE SILVER TEA GLASS ETCHED</td>\n",
              "      <td>12</td>\n",
              "      <td>2010-11-04 14:55:00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>13303.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>980512</th>\n",
              "      <td>575607</td>\n",
              "      <td>22549</td>\n",
              "      <td>PICTURE DOMINOES</td>\n",
              "      <td>5</td>\n",
              "      <td>2011-11-10 12:37:00</td>\n",
              "      <td>3.29</td>\n",
              "      <td>14096.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>988575</th>\n",
              "      <td>576053</td>\n",
              "      <td>23365</td>\n",
              "      <td>SET 12 COLOUR PENCILS LOVE LONDON</td>\n",
              "      <td>8</td>\n",
              "      <td>2011-11-13 14:53:00</td>\n",
              "      <td>0.65</td>\n",
              "      <td>16726.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>760609</th>\n",
              "      <td>557626</td>\n",
              "      <td>23174</td>\n",
              "      <td>REGENCY SUGAR BOWL GREEN</td>\n",
              "      <td>6</td>\n",
              "      <td>2011-06-21 14:29:00</td>\n",
              "      <td>4.15</td>\n",
              "      <td>14175.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29749</th>\n",
              "      <td>491950</td>\n",
              "      <td>22114</td>\n",
              "      <td>HOT WATER BOTTLE TEA AND SYMPATHY</td>\n",
              "      <td>4</td>\n",
              "      <td>2009-12-14 15:49:00</td>\n",
              "      <td>3.95</td>\n",
              "      <td>16280.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>46398</th>\n",
              "      <td>493696</td>\n",
              "      <td>21213</td>\n",
              "      <td>PACK OF 72 SKULL CAKE CASES</td>\n",
              "      <td>120</td>\n",
              "      <td>2010-01-05 13:32:00</td>\n",
              "      <td>0.42</td>\n",
              "      <td>16779.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>110387</th>\n",
              "      <td>499977</td>\n",
              "      <td>84270</td>\n",
              "      <td>ASSTD DESIGN BUBBLE GUM RING</td>\n",
              "      <td>30</td>\n",
              "      <td>2010-03-03 15:13:00</td>\n",
              "      <td>0.21</td>\n",
              "      <td>14142.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>525326</th>\n",
              "      <td>538167</td>\n",
              "      <td>22306</td>\n",
              "      <td>SILVER MUG BONE CHINA TREE OF LIFE</td>\n",
              "      <td>4</td>\n",
              "      <td>2010-12-09 18:58:00</td>\n",
              "      <td>1.95</td>\n",
              "      <td>14713.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>683430</th>\n",
              "      <td>550215</td>\n",
              "      <td>85099C</td>\n",
              "      <td>JUMBO  BAG BAROQUE BLACK WHITE</td>\n",
              "      <td>5</td>\n",
              "      <td>2011-04-15 10:43:00</td>\n",
              "      <td>4.13</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150765</th>\n",
              "      <td>503774</td>\n",
              "      <td>22423</td>\n",
              "      <td>REGENCY CAKESTAND 3 TIER</td>\n",
              "      <td>1</td>\n",
              "      <td>2010-04-07 11:04:00</td>\n",
              "      <td>12.75</td>\n",
              "      <td>14606.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Invoice StockCode  ... Customer ID         Country\n",
              "434350  530855     84946  ...     13303.0  United Kingdom\n",
              "980512  575607     22549  ...     14096.0  United Kingdom\n",
              "988575  576053     23365  ...     16726.0  United Kingdom\n",
              "760609  557626     23174  ...     14175.0  United Kingdom\n",
              "29749   491950     22114  ...     16280.0  United Kingdom\n",
              "46398   493696     21213  ...     16779.0  United Kingdom\n",
              "110387  499977     84270  ...     14142.0  United Kingdom\n",
              "525326  538167     22306  ...     14713.0  United Kingdom\n",
              "683430  550215    85099C  ...         NaN  United Kingdom\n",
              "150765  503774     22423  ...     14606.0  United Kingdom\n",
              "\n",
              "[10 rows x 8 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 237
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UNjqwQ8E1wAF"
      },
      "source": [
        "Melihat ukuran dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l2dP_x-q1Cpg",
        "outputId": "6c4df0d3-25fe-41ef-f12e-8a0e2264b420"
      },
      "source": [
        "df_dataset.shape"
      ],
      "execution_count": 238,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1067371, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 238
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1o_X_3-53B2T"
      },
      "source": [
        "Mencari info setiap kolom (yang non null dan tipe data)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8ihcho1c2N3H",
        "outputId": "fd838a3f-e6a1-40c2-da75-760e8a9cdc7e"
      },
      "source": [
        "df_dataset.info()"
      ],
      "execution_count": 239,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1067371 entries, 0 to 1067370\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count    Dtype  \n",
            "---  ------       --------------    -----  \n",
            " 0   Invoice      1067371 non-null  object \n",
            " 1   StockCode    1067371 non-null  object \n",
            " 2   Description  1062989 non-null  object \n",
            " 3   Quantity     1067371 non-null  int64  \n",
            " 4   InvoiceDate  1067371 non-null  object \n",
            " 5   Price        1067371 non-null  float64\n",
            " 6   Customer ID  824364 non-null   float64\n",
            " 7   Country      1067371 non-null  object \n",
            "dtypes: float64(2), int64(1), object(5)\n",
            "memory usage: 65.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UxBVd3BVyRTH"
      },
      "source": [
        "## Section 1"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qr4-JQSuyrzE"
      },
      "source": [
        "### 1.1. Create New Feature: Year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vzIA8n4f6Y6R"
      },
      "source": [
        "Cek Info awal"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FXJF-9Re2s-b",
        "outputId": "894018f5-7dcb-4497-cb23-c9cd038505dd"
      },
      "source": [
        "df_dataset.info()"
      ],
      "execution_count": 240,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1067371 entries, 0 to 1067370\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count    Dtype  \n",
            "---  ------       --------------    -----  \n",
            " 0   Invoice      1067371 non-null  object \n",
            " 1   StockCode    1067371 non-null  object \n",
            " 2   Description  1062989 non-null  object \n",
            " 3   Quantity     1067371 non-null  int64  \n",
            " 4   InvoiceDate  1067371 non-null  object \n",
            " 5   Price        1067371 non-null  float64\n",
            " 6   Customer ID  824364 non-null   float64\n",
            " 7   Country      1067371 non-null  object \n",
            "dtypes: float64(2), int64(1), object(5)\n",
            "memory usage: 65.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2xPFJGKB0FVG"
      },
      "source": [
        "Mengganti InvoiceDate menjadi datetime"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T0S9antnyt3N",
        "outputId": "69cf6a40-f18d-42b6-bae2-4165fc2dd1fe"
      },
      "source": [
        "df_dataset['InvoiceDate'] = pd.to_datetime(df_dataset['InvoiceDate'])\n",
        "df_dataset.info()"
      ],
      "execution_count": 241,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1067371 entries, 0 to 1067370\n",
            "Data columns (total 8 columns):\n",
            " #   Column       Non-Null Count    Dtype         \n",
            "---  ------       --------------    -----         \n",
            " 0   Invoice      1067371 non-null  object        \n",
            " 1   StockCode    1067371 non-null  object        \n",
            " 2   Description  1062989 non-null  object        \n",
            " 3   Quantity     1067371 non-null  int64         \n",
            " 4   InvoiceDate  1067371 non-null  datetime64[ns]\n",
            " 5   Price        1067371 non-null  float64       \n",
            " 6   Customer ID  824364 non-null   float64       \n",
            " 7   Country      1067371 non-null  object        \n",
            "dtypes: datetime64[ns](1), float64(2), int64(1), object(4)\n",
            "memory usage: 65.1+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-j7DlGLZ3N-_"
      },
      "source": [
        "Mendapatkan Tahun"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dV5_DGRx5Xvc",
        "outputId": "2927608d-0ba6-432d-8910-f6e621743ad6"
      },
      "source": [
        "year = pd.DatetimeIndex(df_dataset['InvoiceDate']).year\n",
        "year"
      ],
      "execution_count": 242,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Int64Index([2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009, 2009,\n",
              "            ...\n",
              "            2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011, 2011],\n",
              "           dtype='int64', name='InvoiceDate', length=1067371)"
            ]
          },
          "metadata": {},
          "execution_count": 242
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CTayltko5pAl"
      },
      "source": [
        "Menambahkan Kolom Year ke Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3jmn9mBx6Bmp",
        "outputId": "48159cf6-2898-4ba9-c372-ed18ac303a7f"
      },
      "source": [
        "df_dataset['Year'] = year\n",
        "df_dataset.info()"
      ],
      "execution_count": 263,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1067371 entries, 0 to 1067370\n",
            "Data columns (total 9 columns):\n",
            " #   Column       Non-Null Count    Dtype         \n",
            "---  ------       --------------    -----         \n",
            " 0   Invoice      1067371 non-null  object        \n",
            " 1   StockCode    1067371 non-null  object        \n",
            " 2   Description  1062989 non-null  object        \n",
            " 3   Quantity     1067371 non-null  int64         \n",
            " 4   InvoiceDate  1067371 non-null  datetime64[ns]\n",
            " 5   Price        1067371 non-null  float64       \n",
            " 6   Customer ID  824364 non-null   float64       \n",
            " 7   Country      1067371 non-null  object        \n",
            " 8   Year         1067371 non-null  int64         \n",
            "dtypes: datetime64[ns](1), float64(2), int64(2), object(4)\n",
            "memory usage: 73.3+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gy8YhbFC9UI0"
      },
      "source": [
        "### 1.2. Filtering Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "scaSHgZW-mvA"
      },
      "source": [
        "Mendapatkan deskripsi Quantity"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qc7Nu4ro9mjo",
        "outputId": "b29fe36f-123a-4ff1-d8d2-32684a9a03ce"
      },
      "source": [
        "df_dataset['Quantity'].describe()"
      ],
      "execution_count": 244,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    1.067371e+06\n",
              "mean     9.938898e+00\n",
              "std      1.727058e+02\n",
              "min     -8.099500e+04\n",
              "25%      1.000000e+00\n",
              "50%      3.000000e+00\n",
              "75%      1.000000e+01\n",
              "max      8.099500e+04\n",
              "Name: Quantity, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 244
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ov0BsGYH_ysk"
      },
      "source": [
        "Quantity di atas atau sama dengan 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "eBY3pKfw_rDs",
        "outputId": "22b13235-4d69-4e26-f544-0dd5db65c705"
      },
      "source": [
        "quantity_valid = df_dataset[df_dataset['Quantity'] >= 1]\n",
        "quantity_valid.head()"
      ],
      "execution_count": 245,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Invoice</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>Price</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Country</th>\n",
              "      <th>Year</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>489434</td>\n",
              "      <td>85048</td>\n",
              "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.95</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323P</td>\n",
              "      <td>PINK CHERRY LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323W</td>\n",
              "      <td>WHITE CHERRY LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>489434</td>\n",
              "      <td>22041</td>\n",
              "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
              "      <td>48</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>2.10</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>489434</td>\n",
              "      <td>21232</td>\n",
              "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
              "      <td>24</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Invoice StockCode  ...         Country  Year\n",
              "0  489434     85048  ...  United Kingdom  2009\n",
              "1  489434    79323P  ...  United Kingdom  2009\n",
              "2  489434    79323W  ...  United Kingdom  2009\n",
              "3  489434     22041  ...  United Kingdom  2009\n",
              "4  489434     21232  ...  United Kingdom  2009\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 245
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BadAL70yADPC"
      },
      "source": [
        "Banyaknya Quantity di atas atau sama dengan 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yc6eY3q1AG5y",
        "outputId": "e6f610fb-7abb-474e-950c-ae75a582b13c"
      },
      "source": [
        "quantity_valid.shape"
      ],
      "execution_count": 246,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1044421, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 246
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UwoASSM6B7R1"
      },
      "source": [
        "Invoice Tanpa C"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sWpU6SP5CDc7",
        "outputId": "6ece06ad-4b91-4bed-b4e5-8d524fe805d0"
      },
      "source": [
        "quantity_valid = quantity_valid[~quantity_valid['Invoice'].str.contains(\"C\")]\n",
        "quantity_valid.shape"
      ],
      "execution_count": 247,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1044420, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 247
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B54qJgsfFGai"
      },
      "source": [
        "### 1.3. Create New Feature: Revenue"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AEa6zXjcFZUr"
      },
      "source": [
        "Buat list Revenue berisi `Quantity * Price`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kGPSyAxOFc7u",
        "outputId": "71874ed5-453a-4cac-bce0-921e44f70755"
      },
      "source": [
        "revenue = quantity_valid['Quantity'] * quantity_valid['Price']\n",
        "revenue"
      ],
      "execution_count": 248,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0           83.40\n",
              "1           81.00\n",
              "2           81.00\n",
              "3          100.80\n",
              "4           30.00\n",
              "            ...  \n",
              "1067366     12.60\n",
              "1067367     16.60\n",
              "1067368     16.60\n",
              "1067369     14.85\n",
              "1067370     18.00\n",
              "Length: 1044420, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 248
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XyAbDLY2GAD4"
      },
      "source": [
        "Masukan ke dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "uN2Xo8uMGHPD",
        "outputId": "ed622440-d468-48de-a279-01de5395d1f7"
      },
      "source": [
        "quantity_valid['Revenue'] = revenue\n",
        "quantity_valid.head()"
      ],
      "execution_count": 249,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Invoice</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>Price</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Country</th>\n",
              "      <th>Year</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>489434</td>\n",
              "      <td>85048</td>\n",
              "      <td>15CM CHRISTMAS GLASS BALL 20 LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.95</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "      <td>83.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323P</td>\n",
              "      <td>PINK CHERRY LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>489434</td>\n",
              "      <td>79323W</td>\n",
              "      <td>WHITE CHERRY LIGHTS</td>\n",
              "      <td>12</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>6.75</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "      <td>81.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>489434</td>\n",
              "      <td>22041</td>\n",
              "      <td>RECORD FRAME 7\" SINGLE SIZE</td>\n",
              "      <td>48</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>2.10</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "      <td>100.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>489434</td>\n",
              "      <td>21232</td>\n",
              "      <td>STRAWBERRY CERAMIC TRINKET BOX</td>\n",
              "      <td>24</td>\n",
              "      <td>2009-12-01 07:45:00</td>\n",
              "      <td>1.25</td>\n",
              "      <td>13085.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2009</td>\n",
              "      <td>30.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Invoice StockCode  ...  Year  Revenue\n",
              "0  489434     85048  ...  2009     83.4\n",
              "1  489434    79323P  ...  2009     81.0\n",
              "2  489434    79323W  ...  2009     81.0\n",
              "3  489434     22041  ...  2009    100.8\n",
              "4  489434     21232  ...  2009     30.0\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 249
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1qdKLi8LHrB1"
      },
      "source": [
        "### 1.4. Average of Revenue per Year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E6NPhrbTJWkH"
      },
      "source": [
        "Rata-rata per Tahun"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "i-gCx4bTH6ww",
        "outputId": "ae254906-847f-43e5-e725-e7ff916ebc16"
      },
      "source": [
        "bakal_plot = quantity_valid.groupby('Year')['Revenue'].mean().reset_index()\n",
        "bakal_plot.rename(columns={'Revenue':'Mean'}, inplace=True)\n",
        "bakal_plot['Year'] = pd.to_datetime(bakal_plot['Year'], format='%Y')\n",
        "bakal_plot"
      ],
      "execution_count": 250,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Year</th>\n",
              "      <th>Mean</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009-01-01</td>\n",
              "      <td>18.713273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-01-01</td>\n",
              "      <td>19.908987</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011-01-01</td>\n",
              "      <td>20.058767</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        Year       Mean\n",
              "0 2009-01-01  18.713273\n",
              "1 2010-01-01  19.908987\n",
              "2 2011-01-01  20.058767"
            ]
          },
          "metadata": {},
          "execution_count": 250
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1oa8RLmeTTZi",
        "outputId": "f0c3d950-04a7-450e-84c9-b780cf6d1800"
      },
      "source": [
        "bakal_plot.info()"
      ],
      "execution_count": 251,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3 entries, 0 to 2\n",
            "Data columns (total 2 columns):\n",
            " #   Column  Non-Null Count  Dtype         \n",
            "---  ------  --------------  -----         \n",
            " 0   Year    3 non-null      datetime64[ns]\n",
            " 1   Mean    3 non-null      float64       \n",
            "dtypes: datetime64[ns](1), float64(1)\n",
            "memory usage: 176.0 bytes\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CVTycXD-vO3p"
      },
      "source": [
        "Matplotlib Styles"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TfoarYs-vRTh",
        "outputId": "8c813fc3-87e8-44c1-be8e-15c8759c7c84"
      },
      "source": [
        "print(plt.style.available)\n",
        "mpl.style.use(['ggplot'])"
      ],
      "execution_count": 252,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Solarize_Light2', '_classic_test_patch', 'bmh', 'classic', 'dark_background', 'fast', 'fivethirtyeight', 'ggplot', 'grayscale', 'seaborn', 'seaborn-bright', 'seaborn-colorblind', 'seaborn-dark', 'seaborn-dark-palette', 'seaborn-darkgrid', 'seaborn-deep', 'seaborn-muted', 'seaborn-notebook', 'seaborn-paper', 'seaborn-pastel', 'seaborn-poster', 'seaborn-talk', 'seaborn-ticks', 'seaborn-white', 'seaborn-whitegrid', 'tableau-colorblind10']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BReS9IihKPAO"
      },
      "source": [
        "Visualisasi\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cx5EaLA6KTUL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "3c5a7172-2b9b-486f-f5d8-c5f273f4587e"
      },
      "source": [
        "mpl.style.use(['fast'])\n",
        "\n",
        "bakal_plot.plot(x='Year', y='Mean', kind='line',\n",
        "                figsize=(10,5), color='orange')\n",
        "plt.title('Rata-rata Revenue Tiap Tahun')\n",
        "plt.ylabel('Mean')\n",
        "plt.xlabel('Year')\n",
        "plt.show()"
      ],
      "execution_count": 253,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "slFBEe6kaZxK"
      },
      "source": [
        "### 1.5. Interpretation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_mp-pyeIahes"
      },
      "source": [
        "Dari Output di atas, dapat dilihat bahwa tiap tahun, rata-rata penjualan (dalam poundsterling) mengalami peningkatan. Pada tahun 2009-2010 mengalami peningkatan yang tajam, sementara pada tahun 2010-2011 hanya mengalami peningkatan yang sedikit saja, tidak seperti pada tahun sebelumnya."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmHCCAthOTMK"
      },
      "source": [
        "## Section 2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VcFpwao8OxlO"
      },
      "source": [
        "### 2.1. dan 2.2. Filtering Data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DBb4JqWwO0JF"
      },
      "source": [
        "#### 2.1. Customers who finished their purchases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KNO89gypO8IU",
        "outputId": "3399ea19-57a9-400f-852e-006eb1e65819"
      },
      "source": [
        "finished = quantity_valid[~quantity_valid['Customer ID'].isnull()]\n",
        "finished.shape[0]"
      ],
      "execution_count": 254,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "805620"
            ]
          },
          "metadata": {},
          "execution_count": 254
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwXDjmt5SObe"
      },
      "source": [
        "#### 2.2. Customers who canceled their purchases"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vA8DciV4STUp",
        "outputId": "001c3238-b4ee-4c1b-9811-f12e53614cc8"
      },
      "source": [
        "cancel = df_dataset[df_dataset['Invoice'].str.contains(\"C\")]\n",
        "cancel.shape[0]"
      ],
      "execution_count": 255,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19494"
            ]
          },
          "metadata": {},
          "execution_count": 255
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S2JRyBlFTQ5m"
      },
      "source": [
        "### 2.3. Number of Finished and Canceled Transactions Each Year"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gReHDRG4sy_1"
      },
      "source": [
        "Banyaknya Transaksi berhasil setiap tahun"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "7jMcrjstTWAg",
        "outputId": "03572430-414a-42e7-abc3-9fbec14e56c6"
      },
      "source": [
        "finished_vis = finished.groupby('Year')['Invoice'].count().reset_index()\n",
        "finished_vis.rename(columns={'Invoice' : 'Finished'}, inplace=True)\n",
        "finished_vis"
      ],
      "execution_count": 256,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Year</th>\n",
              "      <th>Finished</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009</td>\n",
              "      <td>30761</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>403094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>371765</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Finished\n",
              "0  2009     30761\n",
              "1  2010    403094\n",
              "2  2011    371765"
            ]
          },
          "metadata": {},
          "execution_count": 256
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aiE3zN-es1-r"
      },
      "source": [
        "Banyaknya Transaksi gagal setiap tahun"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "k69YXmT6VXEh",
        "outputId": "39abd560-de14-4594-bf02-9cf5dfb5accb"
      },
      "source": [
        "cancel_vis = cancel.groupby('Year')['Invoice'].count().reset_index()\n",
        "cancel_vis.rename(columns={'Invoice' : 'Cancel'}, inplace=True)\n",
        "cancel_vis"
      ],
      "execution_count": 257,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Year</th>\n",
              "      <th>Cancel</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009</td>\n",
              "      <td>1015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>9919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>8560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Cancel\n",
              "0  2009    1015\n",
              "1  2010    9919\n",
              "2  2011    8560"
            ]
          },
          "metadata": {},
          "execution_count": 257
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tB4V7rZTs7gS"
      },
      "source": [
        "Menggabungkan kedua tabel"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "0pwAuNTWV2Qc",
        "outputId": "b67414ef-f729-4ed5-915b-b1d30846486c"
      },
      "source": [
        "cancel_temp = cancel_vis['Cancel']\n",
        "vis = finished_vis\n",
        "vis['Cancel'] = cancel_temp\n",
        "vis"
      ],
      "execution_count": 258,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Year</th>\n",
              "      <th>Finished</th>\n",
              "      <th>Cancel</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2009</td>\n",
              "      <td>30761</td>\n",
              "      <td>1015</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010</td>\n",
              "      <td>403094</td>\n",
              "      <td>9919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2011</td>\n",
              "      <td>371765</td>\n",
              "      <td>8560</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Year  Finished  Cancel\n",
              "0  2009     30761    1015\n",
              "1  2010    403094    9919\n",
              "2  2011    371765    8560"
            ]
          },
          "metadata": {},
          "execution_count": 258
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qdh89GjGs_Wz"
      },
      "source": [
        "Visualisasi"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 517
        },
        "id": "de1lzbBraB8J",
        "outputId": "dc83067c-bd78-43ff-9c9c-b6c9d3d97817"
      },
      "source": [
        "vis['Year'] = pd.to_datetime(vis['Year'], format='%Y')\n",
        "\n",
        "vis.plot(x='Year',\n",
        "          kind='area', \n",
        "             stacked=True,\n",
        "             figsize=(15, 8),\n",
        "         alpha = 0.5\n",
        "             )\n",
        "\n",
        "plt.title('Finished vs Cancel Transaction')\n",
        "plt.ylabel('Number of Transaction')\n",
        "plt.xlabel('Years')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 259,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3URKOj8ynf3r"
      },
      "source": [
        "### 2.4. Cancellation Rate"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqC2On5_tB-5"
      },
      "source": [
        "Menghitung Total"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ysbrfkt8njLM",
        "outputId": "b043bfa5-51f5-4de0-9b30-02b1f473f705"
      },
      "source": [
        "total = vis['Finished'] + vis['Cancel']\n",
        "total"
      ],
      "execution_count": 260,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0     31776\n",
              "1    413013\n",
              "2    380325\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 260
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U1-FVWcbtGMy"
      },
      "source": [
        "Cancellation rate per tahun"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KPHtggzQoXC4",
        "outputId": "05fda024-ec6c-484e-ee71-a56b17d7bed6"
      },
      "source": [
        "cancel_rate = vis['Cancel'] / total * 100\n",
        "cancel_rate.values.tolist()\n",
        "for i in range(len(cancel_rate)):\n",
        "  print(2009 + i, \" memiliki cancellation rate \", cancel_rate[i], \"%\")"
      ],
      "execution_count": 261,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2009  memiliki cancellation rate  3.194234642497482 %\n",
            "2010  memiliki cancellation rate  2.4016193194887325 %\n",
            "2011  memiliki cancellation rate  2.2507066324853744 %\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "e1xGUlmZsHjW"
      },
      "source": [
        "### 2.5. Interpretation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "da1WOPG8sV7A"
      },
      "source": [
        "Dari hasil nomor 2.3 dapat dilihat visualisasi antara transaksi yang berhasil dan cancel, sementara pada nomor 2.4 dapat dilihat cancellation rate. Tiap tahun, cancellation rate mengalami penurunan sehingga merupakan kemajuan bagi ritel online karena semakin banyak pelanggan yang tidak melakukan cancel. "
      ]
    }
  ]
}