eda-rfm-clv-cohort-analysis .ipynb

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    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:300%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>CUSTOMER RELATIONSHIP MANAGEMENT</b></p>\n",
    "\n",
    "<div style=\"float:center;margin-left:190px;max-width:70%;\">   \n",
    "<img src=\"https://cdn.dribbble.com/users/508588/screenshots/16899443/media/51e8632fd2e3d8c19b884034d92dc455.jpg\"></div>\n",
    "\n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:30px;font-family:Georgia;text-align:center;\">What are CRM Benefits?</strong> </div>        \n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:18px;font-family:Georgia;\">**  Target—and build relationships with—new customers.</strong> </div>\n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:18px;font-family:Georgia;\">**  Strengthen relationships with your current customers.</strong> </div>\n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:18px;font-family:Georgia;\">**  View your audience holistically.</strong> </div>\n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:18px;font-family:Georgia;\">**  Show appreciation to your best customers (and grow your business).</strong> </div>\n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:18px;font-family:Georgia;\">**  Track—and improve—your marketing performance.</strong> </div>\n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:18px;font-family:Georgia;\">**  Save time with automation.</strong> </div>\n",
    "<div <h1 <strong style=\"color:#3F497F;font-size:18px;font-family:Georgia;\">**  Gain insights to better understand your business.</strong> </div>\n",
    "\n",
    "<div class=\"inner_cell\">\n",
    "<div class=\"text_cell_render border-box-sizing rendered_html\">\n",
    "<p></p><div class=\"list-group\" id=\"list-tab\" role=\"tablist\">\n",
    "  <h3 class=\"list-group-item list-group-item-action active\" data-toggle=\"list\" role=\"tab\" aria-controls=\"home\" style = \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:200%;text-align:LEFT;border-radius:20px 40px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\">...Table of Contents...</h3>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#1\" role=\"tab\" aria-controls=\"home\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">Data Understanding<span class=\"badge badge-primary badge-pill\">1</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#2\" role=\"tab\" aria-controls=\"messages\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">Data Preparation<span class=\"badge badge-primary badge-pill\">2</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#3\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">CALCULATING RFM METRICS<span class=\"badge badge-primary badge-pill\">3</span></a>\n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#4\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">RFM SCORES<span class=\"badge badge-primary badge-pill\">4</span></a> \n",
    "  <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#5\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">RFM SEGMENTS<span class=\"badge badge-primary badge-pill\">5</span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#6\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">EVALUATION & VISUALIZATION<span class=\"badge badge-primary badge-pill\">6</span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#7\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">Cohort Analysis<span class=\"badge badge-primary badge-pill\">7</span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#8\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">CUSTOMER LIFETIME VALUE<span class=\"badge badge-primary badge-pill\">8</span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#9\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">CLTV Prediction with BG-NBD and Gamma-Gamma<span class=\"badge badge-primary badge-pill\">9</span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"#10\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">Calculation of CLTV<span class=\"badge badge-primary badge-pill\">10</span></a>\n",
    "    <a class=\"list-group-item list-group-item-action\" data-toggle=\"list\" href=\"11\" role=\"tab\" aria-controls=\"settings\" target=\"_self\" style = \"color:#000000; font-family:Charter;font-size:120%;\">Segmentation With CLV<span class=\"badge badge-primary badge-pill\">11</span></a>\n",
    "</div>\n",
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    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>Packages-Libraries-Settings 📜</b></p>"
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      "Collecting lifetimes\r\n",
      "  Downloading Lifetimes-0.11.3-py3-none-any.whl (584 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m584.2/584.2 kB\u001b[0m \u001b[31m18.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hCollecting autograd>=1.2.0\r\n",
      "  Downloading autograd-1.5-py3-none-any.whl (48 kB)\r\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m48.9/48.9 kB\u001b[0m \u001b[31m4.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\r\n",
      "\u001b[?25hRequirement already satisfied: dill>=0.2.6 in /opt/conda/lib/python3.7/site-packages (from lifetimes) (0.3.6)\r\n",
      "Requirement already satisfied: numpy>=1.10.0 in /opt/conda/lib/python3.7/site-packages (from lifetimes) (1.21.6)\r\n",
      "Requirement already satisfied: pandas>=0.24.0 in /opt/conda/lib/python3.7/site-packages (from lifetimes) (1.3.5)\r\n",
      "Requirement already satisfied: scipy>=1.0.0 in /opt/conda/lib/python3.7/site-packages (from lifetimes) (1.7.3)\r\n",
      "Requirement already satisfied: future>=0.15.2 in /opt/conda/lib/python3.7/site-packages (from autograd>=1.2.0->lifetimes) (0.18.3)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24.0->lifetimes) (2022.7.1)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas>=0.24.0->lifetimes) (2.8.2)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas>=0.24.0->lifetimes) (1.16.0)\r\n",
      "Installing collected packages: autograd, lifetimes\r\n",
      "Successfully installed autograd-1.5 lifetimes-0.11.3\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\u001b[33m\r\n",
      "\u001b[0m"
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   "source": [
    "!pip install lifetimes\n",
    "\n",
    "# Temel kütüphanelerimiz.\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import warnings\n",
    "import datetime as dt\n",
    "from lifetimes import BetaGeoFitter\n",
    "from lifetimes import GammaGammaFitter\n",
    "\n",
    "# Beklenti hesaplamak için kullanacağımız kütüphaneler.\n",
    "\n",
    "\n",
    "# Görselleştirme için kullandığımız kütüphaneler.\n",
    "from lifetimes.plotting import plot_period_transactions\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from operator import attrgetter\n",
    "import matplotlib.colors as mcolors\n",
    "import plotly.graph_objs as go\n",
    "from plotly.offline import iplot\n",
    "import plotly.express as px\n",
    "from plotly.tools import FigureFactory as ff\n",
    "\n",
    "sns.set_style('whitegrid')\n",
    "palette = 'Set2'\n",
    "\n",
    "# Birtakım ayarlar.\n",
    "warnings.filterwarnings('ignore')\n",
    "pd.set_option('display.max_columns', None)\n",
    "pd.set_option('display.max_rows', None)\n",
    "pd.set_option('display.width', 160)\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)"
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    "<a id = \"1\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>Data Understanding</b></p>"
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       "            Plotly.purge(gd);\n",
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       "var notebookContainer = gd.closest('#notebook-container');\n",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = pd.read_csv('/kaggle/input/customer-segmentationrfmclvchurn/customer_relationship.csv')\n",
    "df_ = df.copy()\n",
    "a = df.head()\n",
    "\n",
    "\n",
    "table = ff.create_table(a,colorscale=\"blackbody\")\n",
    "for i in range(len(table.layout.annotations)):\n",
    "    table.layout.annotations[i].font.size = 13\n",
    "iplot(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fc9506ab",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:15:59.697030Z",
     "iopub.status.busy": "2023-03-27T00:15:59.696619Z",
     "iopub.status.idle": "2023-03-27T00:15:59.766684Z",
     "shell.execute_reply": "2023-03-27T00:15:59.764906Z"
    },
    "papermill": {
     "duration": 0.088626,
     "end_time": "2023-03-27T00:15:59.769525",
     "exception": false,
     "start_time": "2023-03-27T00:15:59.680899",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ SHAPE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "Rows: 25953\n",
      "Columns: 6\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ TYPES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "invoiceID        object\n",
      "invoice_date     object\n",
      "customerID      float64\n",
      "country          object\n",
      "quantity          int64\n",
      "amount           object\n",
      "dtype: object\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ MISSING VALUES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "invoiceID          0\n",
      "invoice_date       0\n",
      "customerID      3724\n",
      "country            0\n",
      "quantity           0\n",
      "amount             0\n",
      "dtype: int64\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DUPLICATED VALUES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "10\n",
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DESCRIBE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "              count     mean     std       min      25%      50%      75%      max\n",
      "customerID 22229.00 15238.29 1732.98  12346.00 13755.00 15136.00 16746.00 18287.00\n",
      "quantity   25953.00   199.47 1107.08 -80995.00     6.00   100.00   240.00 80995.00\n"
     ]
    }
   ],
   "source": [
    "def check_df(dataframe, head=5):\n",
    "    print(\" SHAPE \".center(90,'~'))\n",
    "    print('Rows: {}'.format(dataframe.shape[0]))\n",
    "    print('Columns: {}'.format(dataframe.shape[1]))\n",
    "    print(\" TYPES \".center(90,'~'))\n",
    "    print(dataframe.dtypes)\n",
    "    print(\" MISSING VALUES \".center(90,'~'))\n",
    "    print(dataframe.isnull().sum())\n",
    "    print(\" DUPLICATED VALUES \".center(90,'~'))\n",
    "    print(dataframe.duplicated().sum())\n",
    "    print(\" DESCRIBE \".center(90,'~'))\n",
    "    print(dataframe.describe().T)\n",
    "    \n",
    "check_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "5a585eb3",
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     "iopub.execute_input": "2023-03-27T00:15:59.800523Z",
     "iopub.status.busy": "2023-03-27T00:15:59.799542Z",
     "iopub.status.idle": "2023-03-27T00:16:02.723812Z",
     "shell.execute_reply": "2023-03-27T00:16:02.722520Z"
    },
    "papermill": {
     "duration": 2.942575,
     "end_time": "2023-03-27T00:16:02.726386",
     "exception": false,
     "start_time": "2023-03-27T00:15:59.783811",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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     "metadata": {},
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   ],
   "source": [
    "df['invoice_date'] = df['invoice_date'].astype('datetime64')\n",
    "\n",
    "Sales_weekly = df.resample('w', on='invoice_date').size()\n",
    "fig = px.line(df, x=Sales_weekly.index, y=Sales_weekly,\n",
    "              labels={'y': 'Number of Sales',\n",
    "                     'x': 'Date'})\n",
    "fig.update_layout(title_text='Number of Sales Weekly',\n",
    "                  title_x=0.5, title_font=dict(size=18)) \n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "1738b388",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:02.757977Z",
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     "start_time": "2023-03-27T00:16:02.741060",
     "status": "completed"
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    "tags": []
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   "outputs": [
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     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "df['amount']= [col.replace(',', '.') for col in df['amount']]\n",
    "df['amount'] = df['amount'].astype(float)\n",
    "\n",
    "\n",
    "month = df.groupby(df['invoice_date'].dt.strftime('%m'))['amount'].sum()\n",
    "fig = px.bar(month, x=month.index, y=month, color=month,\n",
    "             labels={'y': 'Number of Sales',\n",
    "                     'x': 'Months'})\n",
    "fig.update_layout(title_text='Number of Sales per Discrete Months',\n",
    "                  title_x=0.5, title_font=dict(size=18))\n",
    "fig.update_traces(marker=dict(line=dict(color='black', width=1)))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "25078f4d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:03.238036Z",
     "iopub.status.busy": "2023-03-27T00:16:03.237345Z",
     "iopub.status.idle": "2023-03-27T00:16:03.413937Z",
     "shell.execute_reply": "2023-03-27T00:16:03.412664Z"
    },
    "papermill": {
     "duration": 0.195993,
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     "exception": false,
     "start_time": "2023-03-27T00:16:03.220819",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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of Orders by Countries\",\"x\":0.5,\"xanchor\":\"center\",\"y\":0.9,\"yanchor\":\"top\"},\"width\":1100},                        {\"responsive\": true}                    ).then(function(){\n",
       "                            \n",
       "var gd = document.getElementById('cbeae4a9-1242-4c49-be27-3daee93e3db4');\n",
       "var x = new MutationObserver(function (mutations, observer) {{\n",
       "        var display = window.getComputedStyle(gd).display;\n",
       "        if (!display || display === 'none') {{\n",
       "            console.log([gd, 'removed!']);\n",
       "            Plotly.purge(gd);\n",
       "            observer.disconnect();\n",
       "        }}\n",
       "}});\n",
       "\n",
       "// Listen for the removal of the full notebook cells\n",
       "var notebookContainer = gd.closest('#notebook-container');\n",
       "if (notebookContainer) {{\n",
       "    x.observe(notebookContainer, {childList: true});\n",
       "}}\n",
       "\n",
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       "var outputEl = gd.closest('.output');\n",
       "if (outputEl) {{\n",
       "    x.observe(outputEl, {childList: true});\n",
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       "\n",
       "                        })                };                });            </script>        </div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "world_map = df[['customerID', 'invoiceID', 'country']\n",
    "              ].groupby(['customerID', 'invoiceID', 'country']\n",
    "                       ).count().reset_index(drop = False)\n",
    "countries = world_map['country'].value_counts()\n",
    "data = dict(type='choropleth',\n",
    "            locations = countries.index,\n",
    "            locationmode = 'country names',\n",
    "            z = countries,\n",
    "            text = countries.index,\n",
    "            colorbar = {'title':'Orders'},\n",
    "            colorscale='inferno',\n",
    "            reversescale = False)\n",
    "\n",
    "layout = dict(title={'text': \"Number of Orders by Countries\",\n",
    "                     'y':0.9,\n",
    "                     'x':0.5,\n",
    "                     'xanchor': 'center',\n",
    "                     'yanchor': 'top'},\n",
    "              geo = dict(resolution = 50,\n",
    "                         showocean = True,\n",
    "                         oceancolor = \"skyblue\",\n",
    "                         showland = True,\n",
    "                         landcolor = \"goldenrod\",\n",
    "                         showframe = True),\n",
    "             template = 'simple_white',\n",
    "             height = 700,\n",
    "             width = 1100)\n",
    "\n",
    "choromap = go.Figure(data = [data], layout = layout)\n",
    "iplot(choromap, validate = False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4008bafa",
   "metadata": {
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   "source": [
    "<a id = \"2\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>Data Preparation</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e904d2c",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:03.484954Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 22229 entries, 0 to 25951\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype         \n",
      "---  ------        --------------  -----         \n",
      " 0   invoiceID     22229 non-null  object        \n",
      " 1   invoice_date  22229 non-null  datetime64[ns]\n",
      " 2   customerID    22229 non-null  float64       \n",
      " 3   country       22229 non-null  object        \n",
      " 4   quantity      22229 non-null  int64         \n",
      " 5   amount        22229 non-null  float64       \n",
      "dtypes: datetime64[ns](1), float64(2), int64(1), object(2)\n",
      "memory usage: 1.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.dropna(inplace=True)\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e3f11f49",
   "metadata": {
    "execution": {
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     "shell.execute_reply": "2023-03-27T00:16:03.577018Z"
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     "exception": false,
     "start_time": "2023-03-27T00:16:03.531999",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['548370', '575767', '549106', ..., '553060', '577049', '550122'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[~df['invoiceID'].astype(str).str.contains('C', na=False)]\n",
    "df['invoiceID'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "d175f762",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:03.613792Z",
     "iopub.status.busy": "2023-03-27T00:16:03.612962Z",
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     "shell.execute_reply": "2023-03-27T00:16:03.632975Z"
    },
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     "duration": 0.041404,
     "end_time": "2023-03-27T00:16:03.637090",
     "exception": false,
     "start_time": "2023-03-27T00:16:03.595686",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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_date</th>\n",
       "      <th>customerID</th>\n",
       "      <th>quantity</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2021-03-30 16:14:00</td>\n",
       "      <td>15528.00</td>\n",
       "      <td>123</td>\n",
       "      <td>229.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2021-11-11 11:11:00</td>\n",
       "      <td>17348.00</td>\n",
       "      <td>163</td>\n",
       "      <td>209.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2021-04-06 12:08:00</td>\n",
       "      <td>17045.00</td>\n",
       "      <td>1</td>\n",
       "      <td>39.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2021-10-27 15:33:00</td>\n",
       "      <td>16416.00</td>\n",
       "      <td>357</td>\n",
       "      <td>344.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2021-11-16 08:38:00</td>\n",
       "      <td>13816.00</td>\n",
       "      <td>91</td>\n",
       "      <td>199.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         invoice_date  customerID  quantity  amount\n",
       "0 2021-03-30 16:14:00    15528.00       123  229.33\n",
       "1 2021-11-11 11:11:00    17348.00       163  209.73\n",
       "3 2021-04-06 12:08:00    17045.00         1   39.95\n",
       "4 2021-10-27 15:33:00    16416.00       357  344.83\n",
       "5 2021-11-16 08:38:00    13816.00        91  199.98"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.select_dtypes(exclude=['object']).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "13b4153b",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ DESCRIBE ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n",
      "              count     mean     std      min      25%      50%      75%      max\n",
      "customerID 18570.00 15265.97 1734.02 12346.00 13772.50 15173.00 16779.00 18287.00\n",
      "quantity   18570.00   220.96  209.78     1.00    74.00   155.00   290.75   848.50\n",
      "amount     18570.00   379.19  321.71     0.00   157.69   302.87   471.26  1352.07\n"
     ]
    }
   ],
   "source": [
    "def outlier_thresholds(dataframe, variable):\n",
    "    quartile1 = dataframe[variable].quantile(0.01)\n",
    "    quartile3 = dataframe[variable].quantile(0.80)\n",
    "    interquantile_range = quartile3 - quartile1\n",
    "    up_limit = quartile3 + 1.5 * interquantile_range\n",
    "    low_limit = quartile1 - 1.5 * interquantile_range\n",
    "    return low_limit, up_limit\n",
    "\n",
    "\n",
    "def replace_with_thresholds(dataframe, variable):\n",
    "    low_limit, up_limit = outlier_thresholds(dataframe, variable)\n",
    "    dataframe.loc[(dataframe[variable] < low_limit), variable] = low_limit\n",
    "    dataframe.loc[(dataframe[variable] > up_limit), variable] = up_limit\n",
    "    \n",
    "replace_with_thresholds(df, \"quantity\")\n",
    "replace_with_thresholds(df, \"amount\")\n",
    "\n",
    "print(\" DESCRIBE \".center(90,'~'))\n",
    "print(df.describe().T)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "394c665c",
   "metadata": {
    "execution": {
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     "exception": false,
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Timestamp('2021-12-09 12:50:00')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['invoice_date'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "ca7fd6fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:03.783644Z",
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2021, 12, 12, 0, 0)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today_date = dt.datetime(2021, 12, 12)\n",
    "today_date"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "b430fd39",
   "metadata": {
    "execution": {
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     "status": "completed"
    },
    "tags": []
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   "outputs": [
    {
     "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>invoiceID</th>\n",
       "      <th>invoice_date</th>\n",
       "      <th>customerID</th>\n",
       "      <th>country</th>\n",
       "      <th>quantity</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>548370</td>\n",
       "      <td>2021-03-30 16:14:00</td>\n",
       "      <td>15528.00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>123.00</td>\n",
       "      <td>229.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>575767</td>\n",
       "      <td>2021-11-11 11:11:00</td>\n",
       "      <td>17348.00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>163.00</td>\n",
       "      <td>209.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>549106</td>\n",
       "      <td>2021-04-06 12:08:00</td>\n",
       "      <td>17045.00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>1.00</td>\n",
       "      <td>39.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>573112</td>\n",
       "      <td>2021-10-27 15:33:00</td>\n",
       "      <td>16416.00</td>\n",
       "      <td>United Kingdom</td>\n",
       "      <td>357.00</td>\n",
       "      <td>344.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>576630</td>\n",
       "      <td>2021-11-16 08:38:00</td>\n",
       "      <td>13816.00</td>\n",
       "      <td>Germany</td>\n",
       "      <td>91.00</td>\n",
       "      <td>199.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  invoiceID        invoice_date  customerID         country  quantity  amount\n",
       "0    548370 2021-03-30 16:14:00    15528.00  United Kingdom    123.00  229.33\n",
       "1    575767 2021-11-11 11:11:00    17348.00  United Kingdom    163.00  209.73\n",
       "3    549106 2021-04-06 12:08:00    17045.00  United Kingdom      1.00   39.95\n",
       "4    573112 2021-10-27 15:33:00    16416.00  United Kingdom    357.00  344.83\n",
       "5    576630 2021-11-16 08:38:00    13816.00         Germany     91.00  199.98"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "477f034d",
   "metadata": {
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     "duration": 0.016072,
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     "status": "completed"
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   "source": [
    "<a id = \"3\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>CALCULATING RFM METRICS</b></p>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0e3f3061",
   "metadata": {
    "papermill": {
     "duration": 0.015705,
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     "status": "completed"
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    "tags": []
   },
   "source": [
    "<div style=\"float:center;margin-left:100px;max-width:70%;\">   \n",
    "<img src=\"https://www.moengage.com/wp-content/uploads/2020/07/predictive-segments-using-rfm-moengage-2.jpg\"></div>\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "26edeb5d",
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     "metadata": {},
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   ],
   "source": [
    "# Let's calculate the RFM metrics..\n",
    "# Recency : The time elapsed since the customer's last purchase.\n",
    "# Frequency: The total number of purchases by the customer.\n",
    "# Monetary: The total money left by the customer in the whole process.\n",
    "\n",
    "\n",
    "rfm = df.groupby('customerID').agg({'invoice_date': lambda x: (today_date - x.max()).days,\n",
    "                                     'invoiceID': lambda x: x.nunique(),\n",
    "                                     'amount': lambda x: x.sum()})\n",
    "\n",
    "rfm.columns = ['recency', 'frequency', 'monetary']\n",
    "# İadelerden doğabilecek negatif değerleri ve 0 değerleri kaldıralım.\n",
    "rfm = rfm[rfm[\"monetary\"] > 0]\n",
    "\n",
    "b=rfm.head()\n",
    "\n",
    "table = ff.create_table(b,colorscale=\"blackbody\")\n",
    "for i in range(len(table.layout.annotations)):\n",
    "    table.layout.annotations[i].font.size = 13\n",
    "iplot(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "41d1e4ee",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:05.159053Z",
     "iopub.status.busy": "2023-03-27T00:16:05.158617Z",
     "iopub.status.idle": "2023-03-27T00:16:05.317733Z",
     "shell.execute_reply": "2023-03-27T00:16:05.316463Z"
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    "papermill": {
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     "end_time": "2023-03-27T00:16:05.320370",
     "exception": false,
     "start_time": "2023-03-27T00:16:05.140726",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = px.histogram(rfm, x='recency', opacity=0.85, marginal='box')\n",
    "fig.update_traces(marker=dict(line=dict(color='skyblue', width=1)))\n",
    "fig.update_layout(title_text='Visit Frequency Distribution of the Customers',\n",
    "                  title_x=0.5, title_font=dict(size=20))\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b813aabc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:05.358118Z",
     "iopub.status.busy": "2023-03-27T00:16:05.357382Z",
     "iopub.status.idle": "2023-03-27T00:16:05.452183Z",
     "shell.execute_reply": "2023-03-27T00:16:05.450907Z"
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    "papermill": {
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     "end_time": "2023-03-27T00:16:05.455343",
     "exception": false,
     "start_time": "2023-03-27T00:16:05.337981",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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   "source": [
    "fig = px.histogram(rfm, x='monetary', opacity=0.85, marginal='box')\n",
    "fig.update_traces(marker=dict(line=dict(color='skyblue', width=1)))\n",
    "fig.update_layout(title_text='Monetary Distribution of the Customers',\n",
    "                  title_x=0.5, title_font=dict(size=20))\n",
    "fig.show()"
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   "execution_count": 17,
   "id": "8d547640",
   "metadata": {
    "execution": {
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     "status": "completed"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "recency        int64\n",
       "frequency      int64\n",
       "monetary     float64\n",
       "dtype: object"
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     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "rfm.dtypes"
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   "source": [
    "<a id = \"4\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>RFM SCORES</b></p>"
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   "execution_count": 18,
   "id": "7ab23dbc",
   "metadata": {
    "execution": {
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "      <th>frequency_score</th>\n",
       "      <th>monetary_score</th>\n",
       "      <th>RFM_SCORE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.00</th>\n",
       "      <td>327</td>\n",
       "      <td>1</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>12347.00</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
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       "    <tr>\n",
       "      <th>12348.00</th>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
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       "      <td>24</td>\n",
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       "    <tr>\n",
       "      <th>12349.00</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.00</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "            recency  frequency  monetary recency_score frequency_score monetary_score RFM_SCORE\n",
       "customerID                                                                                     \n",
       "12346.00        327          1   1352.07             1               1              4        11\n",
       "12347.00          4          7   4310.00             5               5              5        55\n",
       "12348.00         77          4   1797.24             2               4              4        24\n",
       "12349.00         20          1   1352.07             4               1              4        41\n",
       "12350.00        312          1    334.40             1               1              2        11"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's determine the RFM scores with the function we will create.\n",
    "\n",
    "def get_rfm_scores(dataframe):\n",
    "\n",
    "    dataframe[\"recency_score\"] = pd.qcut(dataframe[\"recency\"], 5, labels=[5, 4, 3, 2, 1])\n",
    "    dataframe[\"frequency_score\"] = pd.qcut(dataframe[\"frequency\"].rank(method='first'), 5, labels=[1, 2, 3, 4, 5])\n",
    "    dataframe[\"monetary_score\"] = pd.qcut(dataframe[\"monetary\"], 5, labels=[1, 2, 3, 4, 5])\n",
    "    dataframe[\"RFM_SCORE\"] = dataframe[\"recency_score\"].astype(str) + dataframe[\"frequency_score\"].astype(str)\n",
    "\n",
    "    return dataframe\n",
    "\n",
    "get_rfm_scores(rfm)\n",
    "rfm.reset_index()\n",
    "rfm.head()\n"
   ]
  },
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   "source": [
    "<a id = \"5\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>RFM SEGMENTS</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "90d4bd74",
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    "execution": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>recency_score</th>\n",
       "      <th>frequency_score</th>\n",
       "      <th>monetary_score</th>\n",
       "      <th>RFM_SCORE</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12346.00</th>\n",
       "      <td>327</td>\n",
       "      <td>1</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12347.00</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>55</td>\n",
       "      <td>champions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.00</th>\n",
       "      <td>77</td>\n",
       "      <td>4</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>24</td>\n",
       "      <td>at_Risk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.00</th>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>41</td>\n",
       "      <td>promising</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.00</th>\n",
       "      <td>312</td>\n",
       "      <td>1</td>\n",
       "      <td>334.40</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>hibernating</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency  frequency  monetary recency_score frequency_score monetary_score RFM_SCORE      segment\n",
       "customerID                                                                                                  \n",
       "12346.00        327          1   1352.07             1               1              4        11  hibernating\n",
       "12347.00          4          7   4310.00             5               5              5        55    champions\n",
       "12348.00         77          4   1797.24             2               4              4        24      at_Risk\n",
       "12349.00         20          1   1352.07             4               1              4        41    promising\n",
       "12350.00        312          1    334.40             1               1              2        11  hibernating"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "seg_map = {\n",
    "    r'[1-2][1-2]': 'hibernating',\n",
    "    r'[1-2][3-4]': 'at_Risk',\n",
    "    r'[1-2]5': 'cant_loose',\n",
    "    r'3[1-2]': 'about_to_sleep',\n",
    "    r'33': 'need_attention',\n",
    "    r'[3-4][4-5]': 'loyal_customers',\n",
    "    r'41': 'promising',\n",
    "    r'51': 'new_customers',\n",
    "    r'[4-5][2-3]': 'potential_loyalists',\n",
    "    r'5[4-5]': 'champions'\n",
    "}\n",
    "\n",
    "rfm['segment'] = rfm['RFM_SCORE'].replace(seg_map, regex = True)\n",
    "\n",
    "rfm.head()"
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  {
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    "<a id = \"6\"></a>\n",
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   "source": [
    "x = rfm.segment.value_counts()\n",
    "fig = px.treemap(x, path=[x.index], values=x)\n",
    "fig.update_layout(title_text='Distribution of the RFM Segments', title_x=0.5,\n",
    "                  title_font=dict(size=20))\n",
    "fig.update_traces(textinfo=\"label+value+percent root\")\n",
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  {
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   "source": [
    "1. The hibernate customer segment represents a quarter of our customer base, and targeted re-engagement efforts may be necessary to win them back. We could consider personalized email campaigns, social media re-targeting, or special offers to incentivize them to make a purchase. Additionally, conducting surveys or feedback sessions with these customers may provide valuable insights into why they stopped engaging with our brand and how we can win them back.\n",
    "\n",
    "2. Our loyal customers represent a significant portion of our customer base, and it is important to maintain their loyalty through personalized communication and incentives. This could include exclusive offers, early access to new products or services, or personalized communications that make them feel valued.\n",
    " \n",
    "3. The champions segment represents highly engaged and loyal customers, and we should continue to engage with this segment to maintain their loyalty. We could consider special promotions or discounts for repeat purchases, or personalized communications that show appreciation for their loyalty.\n",
    " \n",
    "4. For potential loyals and at-risk customers, we need to take different approaches. For potential loyals, targeted marketing efforts may be necessary to convert them into loyal customers. This could include personalized recommendations based on their purchase history or follow-up emails to encourage them to make another purchase. For at-risk customers, incentives may be necessary to bring them back, such as special offers or discounts.\n",
    " \n",
    "5. About-to-sleep customers represent a significant portion of our customer base, and we need to prevent them from becoming hibernating customers. This could be achieved through personalized communications that highlight new products or services, or special offers to incentivize them to make a purchase.\n",
    " \n",
    "6. For customers in the needs-attention and promising segments, targeted efforts are required to increase their engagement and loyalty. This could include personalized communications that highlight products or services that may be of interest to them, or special offers to incentivize them to make a purchase.\n",
    " \n",
    "7. Can't-lose customers are highly engaged and valuable, and it is important to continue to engage with this segment to maintain their loyalty. This could include personalized communications that highlight new products or services, or exclusive offers that show appreciation for their loyalty.\n",
    " \n",
    "8. For new customers, targeted campaigns may be necessary to increase their engagement and loyalty. This could include personalized communications that highlight products or services that may be of interest to them, or special offers to incentivize them to make a purchase. It is important to make a good first impression on new customers to increase their chances of becoming loyal customers in the future.\n",
    "\n",
    "Overall, the RFM analysis suggests that there are significant opportunities to increase engagement and loyalty among various segments of the customer base. Targeted marketing and re-engagement efforts may be necessary to increase engagement and loyalty among different segments of the customer base. It is also important to continue to engage with and maintain loyalty among valuable customer segments, such as loyal customers and champions.\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8fdbc11d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:06.037994Z",
     "iopub.status.busy": "2023-03-27T00:16:06.037594Z",
     "iopub.status.idle": "2023-03-27T00:16:06.061200Z",
     "shell.execute_reply": "2023-03-27T00:16:06.060035Z"
    },
    "papermill": {
     "duration": 0.047343,
     "end_time": "2023-03-27T00:16:06.063847",
     "exception": false,
     "start_time": "2023-03-27T00:16:06.016504",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">frequency</th>\n",
       "      <th colspan=\"2\" halign=\"left\">monetary</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>about_to_sleep</th>\n",
       "      <td>54.31</td>\n",
       "      <td>352</td>\n",
       "      <td>1.16</td>\n",
       "      <td>352</td>\n",
       "      <td>443.19</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>at_Risk</th>\n",
       "      <td>154.79</td>\n",
       "      <td>593</td>\n",
       "      <td>2.88</td>\n",
       "      <td>593</td>\n",
       "      <td>917.40</td>\n",
       "      <td>593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cant_loose</th>\n",
       "      <td>133.97</td>\n",
       "      <td>63</td>\n",
       "      <td>8.38</td>\n",
       "      <td>63</td>\n",
       "      <td>2713.31</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>champions</th>\n",
       "      <td>7.36</td>\n",
       "      <td>633</td>\n",
       "      <td>12.42</td>\n",
       "      <td>633</td>\n",
       "      <td>5081.49</td>\n",
       "      <td>633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hibernating</th>\n",
       "      <td>218.61</td>\n",
       "      <td>1071</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1071</td>\n",
       "      <td>379.44</td>\n",
       "      <td>1071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loyal_customers</th>\n",
       "      <td>34.61</td>\n",
       "      <td>819</td>\n",
       "      <td>6.48</td>\n",
       "      <td>819</td>\n",
       "      <td>2476.91</td>\n",
       "      <td>819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>need_attention</th>\n",
       "      <td>53.43</td>\n",
       "      <td>187</td>\n",
       "      <td>2.33</td>\n",
       "      <td>187</td>\n",
       "      <td>830.83</td>\n",
       "      <td>187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>new_customers</th>\n",
       "      <td>8.43</td>\n",
       "      <td>42</td>\n",
       "      <td>1.00</td>\n",
       "      <td>42</td>\n",
       "      <td>328.48</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>potential_loyalists</th>\n",
       "      <td>18.40</td>\n",
       "      <td>484</td>\n",
       "      <td>2.01</td>\n",
       "      <td>484</td>\n",
       "      <td>666.66</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>promising</th>\n",
       "      <td>24.51</td>\n",
       "      <td>94</td>\n",
       "      <td>1.00</td>\n",
       "      <td>94</td>\n",
       "      <td>289.69</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    recency       frequency       monetary      \n",
       "                       mean count      mean count     mean count\n",
       "segment                                                         \n",
       "about_to_sleep        54.31   352      1.16   352   443.19   352\n",
       "at_Risk              154.79   593      2.88   593   917.40   593\n",
       "cant_loose           133.97    63      8.38    63  2713.31    63\n",
       "champions              7.36   633     12.42   633  5081.49   633\n",
       "hibernating          218.61  1071      1.10  1071   379.44  1071\n",
       "loyal_customers       34.61   819      6.48   819  2476.91   819\n",
       "need_attention        53.43   187      2.33   187   830.83   187\n",
       "new_customers          8.43    42      1.00    42   328.48    42\n",
       "potential_loyalists   18.40   484      2.01   484   666.66   484\n",
       "promising             24.51    94      1.00    94   289.69    94"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to make more effective comments, we can apply some operations to our variables in the segments breakdown.\n",
    "rfm[[\"segment\", \"recency\", \"frequency\", \"monetary\"]].groupby(\"segment\").agg([\"mean\", \"count\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "01c30029",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:06.105322Z",
     "iopub.status.busy": "2023-03-27T00:16:06.104879Z",
     "iopub.status.idle": "2023-03-27T00:16:06.634160Z",
     "shell.execute_reply": "2023-03-27T00:16:06.632834Z"
    },
    "papermill": {
     "duration": 0.55342,
     "end_time": "2023-03-27T00:16:06.637102",
     "exception": false,
     "start_time": "2023-03-27T00:16:06.083682",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 2000x1500 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can make sense of our RFM segments much more easily on a graph.\n",
    "\n",
    "background = \"#313332\"\n",
    "backgroundr = \"#6D6D6D\"\n",
    "text_color = \"w\"\n",
    "title_font=\"Alegreya Sans\"\n",
    "\n",
    "plt.figure(figsize=(20,15))\n",
    "fig, ax = plt.subplots(figsize=(20,10))\n",
    "ax.patch.set_facecolor(backgroundr)\n",
    "fig.set_facecolor(backgroundr)\n",
    "fig.facecolor=\"white\"\n",
    "fig.edgecolor=\"white\"\n",
    "\n",
    "ax.grid(ls=\"dotted\",lw=0.5,color=\"lightgrey\",zorder=1)\n",
    "bar_ap = sns.countplot(data = rfm,\n",
    "                   x = 'segment')\n",
    "total = len(rfm.segment)\n",
    "for patch in ax.patches:\n",
    "    percentage = '{:.1f}%'.format(100 * patch.get_height()/total)\n",
    "    x = patch.get_x() + patch.get_width() / 2 - 0.17\n",
    "    y = patch.get_y() + patch.get_height() * 1.005\n",
    "    ax.annotate(percentage, (x, y), size = 14)\n",
    "bar_ap.set_xticklabels(bar_ap.get_xticklabels(), rotation=90)\n",
    "plt.title('Number of Customers by Segments', size = 16, color=text_color)\n",
    "plt.ylabel('Count', size = 18,color=\"white\" )\n",
    "plt.xlabel(\"SEGMENT\",fontsize = 18,color=\"white\" )\n",
    "plt.xticks(size = 15,color=\"white\" )\n",
    "plt.yticks(size = 15,color=\"white\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1cd796e4",
   "metadata": {
    "papermill": {
     "duration": 0.021351,
     "end_time": "2023-03-27T00:16:06.681725",
     "exception": false,
     "start_time": "2023-03-27T00:16:06.660374",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id = \"7\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>Cohort Analysis</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb85a930",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:06.726631Z",
     "iopub.status.busy": "2023-03-27T00:16:06.726202Z",
     "iopub.status.idle": "2023-03-27T00:16:08.031370Z",
     "shell.execute_reply": "2023-03-27T00:16:08.030441Z"
    },
    "papermill": {
     "duration": 1.331547,
     "end_time": "2023-03-27T00:16:08.034728",
     "exception": false,
     "start_time": "2023-03-27T00:16:06.703181",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# In Cohort Analysis, we track groups of users over time who find some common patterns or behaviors.\n",
    "def CohortAnalysis(dataframe):\n",
    "    dataframe = dataframe[[\"customerID\", \"invoiceID\", \"invoice_date\"]].drop_duplicates()\n",
    "    dataframe[\"order_month\"] = dataframe[\"invoice_date\"].dt.to_period(\"M\")\n",
    "    dataframe[\"cohort\"] = (dataframe.groupby(\"customerID\")[\"invoice_date\"].transform(\"min\").dt.to_period(\"M\"))\n",
    "    cohort_data = (dataframe.groupby([\"cohort\", \"order_month\"]).agg(n_customers=(\"customerID\", \"nunique\")).reset_index(drop=False))\n",
    "    cohort_data[\"period_number\"] = (cohort_data.order_month - cohort_data.cohort).apply(attrgetter(\"n\"))\n",
    "    cohort_pivot = cohort_data.pivot_table(index=\"cohort\", columns=\"period_number\", values=\"n_customers\")\n",
    "    cohort_size = cohort_pivot.iloc[:, 0]\n",
    "    retention_matrix = cohort_pivot.divide(cohort_size, axis=0)\n",
    "    with sns.axes_style(\"white\"):\n",
    "        fig, ax = plt.subplots(1, 2, figsize=(12, 8), sharey=True, gridspec_kw={\"width_ratios\": [1, 11]})\n",
    "        sns.heatmap(retention_matrix,mask=retention_matrix.isnull(), annot=True, cbar=False, fmt=\".0%\", cmap=\"inferno\", ax=ax[1],)\n",
    "        ax[1].set_title(\"Monthly Cohorts: User Retention\", fontsize=14)\n",
    "        ax[1].set(xlabel=\"# of periods\", ylabel=\"\")\n",
    "        white_cmap = mcolors.ListedColormap([\"white\"])\n",
    "        sns.heatmap(pd.DataFrame(cohort_size).rename(columns={0: \"cohort_size\"}),annot=True,cbar=False,fmt=\"g\",cmap=white_cmap,ax=ax[0],)\n",
    "        fig.tight_layout()\n",
    "    \n",
    "CohortAnalysis(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "68691f17",
   "metadata": {
    "papermill": {
     "duration": 0.023934,
     "end_time": "2023-03-27T00:16:08.084453",
     "exception": false,
     "start_time": "2023-03-27T00:16:08.060519",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id = \"8\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>CUSTOMER LIFETIME VALUE</b></p>\n",
    "\n",
    "* average_order_value = total_price / total_transaction)\n",
    "* Purchase Frequency (total_transaction / total_number_of_customers)\n",
    "* Repeat Rate & Churn Rate\n",
    "* profit_margin =  total_price * 0.10\n",
    "* customer_value = average_order_value * purchase_frequency)\n",
    "* CLTV = (customer_value / churn_rate) x profit_margin)\n",
    "* Segmentation\n",
    "* CLTV = Expected Number of Transaction(BG/NBD) * Expected Average Profit(gamma gamma)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9e7aa048",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:08.137603Z",
     "iopub.status.busy": "2023-03-27T00:16:08.136780Z",
     "iopub.status.idle": "2023-03-27T00:16:09.025016Z",
     "shell.execute_reply": "2023-03-27T00:16:09.023806Z"
    },
    "papermill": {
     "duration": 0.91795,
     "end_time": "2023-03-27T00:16:09.027812",
     "exception": false,
     "start_time": "2023-03-27T00:16:08.109862",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 'total_transaction' : Number of unique invoices per customer\n",
    "# 'total_unit'        : Total number of products purchased specifically for the customer\n",
    "# 'total_price'        :  Total amount spent per customer\n",
    "\n",
    "clv = df.groupby('customerID').agg({'invoiceID': lambda x: x.nunique(),\n",
    "                                        'quantity': lambda x: x.sum(),\n",
    "                                        'amount': lambda x: x.sum()})\n",
    "\n",
    "clv.columns = ['total_transaction', 'total_unit', 'total_price']\n",
    "\n",
    "clv['total_transaction'] = clv['total_transaction'].astype(float)\n",
    "clv.reset_index()\n",
    "d=clv.head()\n",
    "\n",
    "table = ff.create_table(d,colorscale=\"blackbody\")\n",
    "for i in range(len(table.layout.annotations)):\n",
    "    table.layout.annotations[i].font.size = 13\n",
    "iplot(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b212b422",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:09.082454Z",
     "iopub.status.busy": "2023-03-27T00:16:09.082004Z",
     "iopub.status.idle": "2023-03-27T00:16:09.103986Z",
     "shell.execute_reply": "2023-03-27T00:16:09.102598Z"
    },
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     "exception": false,
     "start_time": "2023-03-27T00:16:09.054743",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_transaction</th>\n",
       "      <th>total_unit</th>\n",
       "      <th>total_price</th>\n",
       "      <th>average_order_value</th>\n",
       "      <th>purchase_frequency</th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>customer_value</th>\n",
       "      <th>clv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>12346.00</th>\n",
       "      <td>1.00</td>\n",
       "      <td>848.50</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>446.18</td>\n",
       "      <td>0.31</td>\n",
       "      <td>403.80</td>\n",
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       "    <tr>\n",
       "      <th>12347.00</th>\n",
       "      <td>7.00</td>\n",
       "      <td>2458.00</td>\n",
       "      <td>4310.00</td>\n",
       "      <td>615.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1422.30</td>\n",
       "      <td>0.99</td>\n",
       "      <td>4103.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.00</th>\n",
       "      <td>4.00</td>\n",
       "      <td>1935.50</td>\n",
       "      <td>1797.24</td>\n",
       "      <td>449.31</td>\n",
       "      <td>0.00</td>\n",
       "      <td>593.09</td>\n",
       "      <td>0.41</td>\n",
       "      <td>713.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12349.00</th>\n",
       "      <td>1.00</td>\n",
       "      <td>631.00</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>1352.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>446.18</td>\n",
       "      <td>0.31</td>\n",
       "      <td>403.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12350.00</th>\n",
       "      <td>1.00</td>\n",
       "      <td>197.00</td>\n",
       "      <td>334.40</td>\n",
       "      <td>334.40</td>\n",
       "      <td>0.00</td>\n",
       "      <td>110.35</td>\n",
       "      <td>0.08</td>\n",
       "      <td>24.70</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "            total_transaction  total_unit  total_price  average_order_value  purchase_frequency  profit_margin  customer_value     clv\n",
       "customerID                                                                                                                            \n",
       "12346.00                 1.00      848.50      1352.07              1352.07                0.00         446.18            0.31  403.80\n",
       "12347.00                 7.00     2458.00      4310.00               615.71                0.00        1422.30            0.99 4103.15\n",
       "12348.00                 4.00     1935.50      1797.24               449.31                0.00         593.09            0.41  713.47\n",
       "12349.00                 1.00      631.00      1352.07              1352.07                0.00         446.18            0.31  403.80\n",
       "12350.00                 1.00      197.00       334.40               334.40                0.00         110.35            0.08   24.70"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculating CLV metrics\n",
    "clv[\"average_order_value\"] = clv[\"total_price\"] / clv[\"total_transaction\"]\n",
    "clv[\"purchase_frequency\"] = clv[\"total_transaction\"] / clv.shape[0]\n",
    "repeat_rate = clv[clv[\"total_transaction\"] > 1].shape[0] / clv.shape[0]\n",
    "churn_rate = 1 - repeat_rate\n",
    "clv['profit_margin'] = clv['total_price'] * 0.33\n",
    "clv['customer_value'] = clv['average_order_value'] * clv[\"purchase_frequency\"]\n",
    "\n",
    "clv[\"clv\"] = (clv[\"customer_value\"] / churn_rate) * clv[\"profit_margin\"]\n",
    "clv.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "a5706aac",
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    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_transaction</th>\n",
       "      <th>total_unit</th>\n",
       "      <th>total_price</th>\n",
       "      <th>average_order_value</th>\n",
       "      <th>purchase_frequency</th>\n",
       "      <th>profit_margin</th>\n",
       "      <th>customer_value</th>\n",
       "      <th>clv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerID</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>14911.00</th>\n",
       "      <td>201.00</td>\n",
       "      <td>68617.50</td>\n",
       "      <td>129556.23</td>\n",
       "      <td>644.56</td>\n",
       "      <td>0.05</td>\n",
       "      <td>42753.56</td>\n",
       "      <td>29.86</td>\n",
       "      <td>3707489.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14646.00</th>\n",
       "      <td>74.00</td>\n",
       "      <td>43317.00</td>\n",
       "      <td>68841.14</td>\n",
       "      <td>930.29</td>\n",
       "      <td>0.02</td>\n",
       "      <td>22717.58</td>\n",
       "      <td>15.87</td>\n",
       "      <td>1046789.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18102.00</th>\n",
       "      <td>60.00</td>\n",
       "      <td>32178.00</td>\n",
       "      <td>63356.61</td>\n",
       "      <td>1055.94</td>\n",
       "      <td>0.01</td>\n",
       "      <td>20907.68</td>\n",
       "      <td>14.60</td>\n",
       "      <td>886639.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13089.00</th>\n",
       "      <td>97.00</td>\n",
       "      <td>29109.00</td>\n",
       "      <td>53349.70</td>\n",
       "      <td>550.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>17605.40</td>\n",
       "      <td>12.30</td>\n",
       "      <td>628676.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15311.00</th>\n",
       "      <td>91.00</td>\n",
       "      <td>32005.50</td>\n",
       "      <td>52302.46</td>\n",
       "      <td>574.75</td>\n",
       "      <td>0.02</td>\n",
       "      <td>17259.81</td>\n",
       "      <td>12.05</td>\n",
       "      <td>604237.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            total_transaction  total_unit  total_price  average_order_value  purchase_frequency  profit_margin  customer_value        clv\n",
       "customerID                                                                                                                               \n",
       "14911.00               201.00    68617.50    129556.23               644.56                0.05       42753.56           29.86 3707489.93\n",
       "14646.00                74.00    43317.00     68841.14               930.29                0.02       22717.58           15.87 1046789.72\n",
       "18102.00                60.00    32178.00     63356.61              1055.94                0.01       20907.68           14.60  886639.77\n",
       "13089.00                97.00    29109.00     53349.70               550.00                0.02       17605.40           12.30  628676.61\n",
       "15311.00                91.00    32005.50     52302.46               574.75                0.02       17259.81           12.05  604237.48"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sıralı verimize bir göz atalım.\n",
    "clv.sort_values(by=\"clv\", ascending=False).head()"
   ]
  },
  {
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       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_transaction</th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_unit</th>\n",
       "      <th colspan=\"3\" halign=\"left\">total_price</th>\n",
       "      <th colspan=\"3\" halign=\"left\">average_order_value</th>\n",
       "      <th colspan=\"3\" halign=\"left\">purchase_frequency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">profit_margin</th>\n",
       "      <th colspan=\"3\" halign=\"left\">customer_value</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>1085</td>\n",
       "      <td>1373.00</td>\n",
       "      <td>1.27</td>\n",
       "      <td>1085</td>\n",
       "      <td>128134.50</td>\n",
       "      <td>118.10</td>\n",
       "      <td>1085</td>\n",
       "      <td>194130.09</td>\n",
       "      <td>178.92</td>\n",
       "      <td>1085</td>\n",
       "      <td>168062.43</td>\n",
       "      <td>154.90</td>\n",
       "      <td>1085</td>\n",
       "      <td>0.32</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1085</td>\n",
       "      <td>64062.93</td>\n",
       "      <td>59.04</td>\n",
       "      <td>1085</td>\n",
       "      <td>44.74</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1085</td>\n",
       "      <td>8940.06</td>\n",
       "      <td>8.24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>1085</td>\n",
       "      <td>2035.00</td>\n",
       "      <td>1.88</td>\n",
       "      <td>1085</td>\n",
       "      <td>303923.00</td>\n",
       "      <td>280.11</td>\n",
       "      <td>1085</td>\n",
       "      <td>503836.85</td>\n",
       "      <td>464.37</td>\n",
       "      <td>1085</td>\n",
       "      <td>329604.95</td>\n",
       "      <td>303.78</td>\n",
       "      <td>1085</td>\n",
       "      <td>0.47</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1085</td>\n",
       "      <td>166266.16</td>\n",
       "      <td>153.24</td>\n",
       "      <td>1085</td>\n",
       "      <td>116.12</td>\n",
       "      <td>0.11</td>\n",
       "      <td>1085</td>\n",
       "      <td>54587.64</td>\n",
       "      <td>50.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>1084</td>\n",
       "      <td>3682.00</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1084</td>\n",
       "      <td>690558.00</td>\n",
       "      <td>637.05</td>\n",
       "      <td>1084</td>\n",
       "      <td>1150928.52</td>\n",
       "      <td>1061.74</td>\n",
       "      <td>1084</td>\n",
       "      <td>460034.98</td>\n",
       "      <td>424.39</td>\n",
       "      <td>1084</td>\n",
       "      <td>0.85</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1084</td>\n",
       "      <td>379806.41</td>\n",
       "      <td>350.37</td>\n",
       "      <td>1084</td>\n",
       "      <td>265.25</td>\n",
       "      <td>0.24</td>\n",
       "      <td>1084</td>\n",
       "      <td>286901.99</td>\n",
       "      <td>264.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1085</td>\n",
       "      <td>11446.00</td>\n",
       "      <td>10.55</td>\n",
       "      <td>1085</td>\n",
       "      <td>2980573.00</td>\n",
       "      <td>2747.07</td>\n",
       "      <td>1085</td>\n",
       "      <td>5192676.06</td>\n",
       "      <td>4785.88</td>\n",
       "      <td>1085</td>\n",
       "      <td>545611.41</td>\n",
       "      <td>502.87</td>\n",
       "      <td>1085</td>\n",
       "      <td>2.64</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1085</td>\n",
       "      <td>1713583.10</td>\n",
       "      <td>1579.34</td>\n",
       "      <td>1085</td>\n",
       "      <td>1196.74</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1085</td>\n",
       "      <td>17741673.11</td>\n",
       "      <td>16351.77</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_transaction                total_unit                    total_price                    average_order_value                  purchase_frequency  \\\n",
       "                    count      sum  mean      count        sum    mean       count        sum    mean               count       sum   mean              count   \n",
       "segment                                                                                                                                                         \n",
       "D                    1085  1373.00  1.27       1085  128134.50  118.10        1085  194130.09  178.92                1085 168062.43 154.90               1085   \n",
       "C                    1085  2035.00  1.88       1085  303923.00  280.11        1085  503836.85  464.37                1085 329604.95 303.78               1085   \n",
       "B                    1084  3682.00  3.40       1084  690558.00  637.05        1084 1150928.52 1061.74                1084 460034.98 424.39               1084   \n",
       "A                    1085 11446.00 10.55       1085 2980573.00 2747.07        1085 5192676.06 4785.88                1085 545611.41 502.87               1085   \n",
       "\n",
       "                  profit_margin                    customer_value                clv                       \n",
       "         sum mean         count        sum    mean          count     sum mean count         sum     mean  \n",
       "segment                                                                                                    \n",
       "D       0.32 0.00          1085   64062.93   59.04           1085   44.74 0.04  1085     8940.06     8.24  \n",
       "C       0.47 0.00          1085  166266.16  153.24           1085  116.12 0.11  1085    54587.64    50.31  \n",
       "B       0.85 0.00          1084  379806.41  350.37           1084  265.25 0.24  1084   286901.99   264.67  \n",
       "A       2.64 0.00          1085 1713583.10 1579.34           1085 1196.74 1.10  1085 17741673.11 16351.77  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CLV segmentlerinin belirlenmesi\n",
    "clv[\"segment\"] = pd.qcut(clv[\"clv\"], 4, labels=[\"D\", \"C\", \"B\", \"A\"])\n",
    "\n",
    "# Segmentlere göre ayrıntılı analizler yapabiliriz.\n",
    "\n",
    "clv.groupby(\"segment\").agg({\"count\", \"mean\", \"sum\"})"
   ]
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    "<a id = \"9\"></a>\n",
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   ],
   "source": [
    "# Before setting up our prediction models, let's calculate the variables that you will use in our model. These:\n",
    "# 'recency'  : Time since customer's last purchase (weekly)\n",
    "#     'T'    :  Customer's age (time since first purchase)\n",
    "# 'frequency' : Number of purchases by the customer\n",
    "#  'monetary'  : The total amount of purchases made by the customer.\n",
    "\n",
    "clv_predict = df.groupby('customerID').agg({'invoice_date': [lambda InvoiceDate: (InvoiceDate.max() - InvoiceDate.min()).days,\n",
    "                                                         lambda InvoiceDate: (today_date - InvoiceDate.min()).days],\n",
    "                                         'invoiceID': lambda Invoice: Invoice.nunique(),\n",
    "                                         'amount': lambda TotalPrice: TotalPrice.sum()})\n",
    "\n",
    "clv_predict.columns = ['recency', 'T', 'frequency', 'monetary']\n",
    "\n",
    "clv_predict[\"monetary\"] = clv_predict[\"monetary\"] / clv_predict[\"frequency\"]\n",
    "\n",
    "clv_predict = clv_predict[(clv_predict['frequency'] > 1)]\n",
    "clv_predict[\"recency\"] = clv_predict[\"recency\"] / 7\n",
    "clv_predict[\"T\"] = clv_predict[\"T\"] / 7\n",
    "clv_predict.reset_index()\n",
    "d=clv_predict.head()\n",
    "\n",
    "table = ff.create_table(d,colorscale=\"blackbody\")\n",
    "for i in range(len(table.layout.annotations)):\n",
    "    table.layout.annotations[i].font.size = 13\n",
    "iplot(table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f9c99090",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:11.318168Z",
     "iopub.status.busy": "2023-03-27T00:16:11.317749Z",
     "iopub.status.idle": "2023-03-27T00:16:11.463932Z",
     "shell.execute_reply": "2023-03-27T00:16:11.462502Z"
    },
    "papermill": {
     "duration": 0.178445,
     "end_time": "2023-03-27T00:16:11.467774",
     "exception": false,
     "start_time": "2023-03-27T00:16:11.289329",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.BetaGeoFitter: fitted with 2845 subjects, a: 0.12, alpha: 11.47, b: 2.51, r: 2.19>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#BGF Modelimizi kuralım\n",
    "\n",
    "bgf = BetaGeoFitter(penalizer_coef=0.001)\n",
    "\n",
    "bgf.fit(clv_predict['frequency'],\n",
    "        clv_predict['recency'],\n",
    "        clv_predict['T'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "9a218cd1",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:11.561665Z",
     "iopub.status.busy": "2023-03-27T00:16:11.561047Z",
     "iopub.status.idle": "2023-03-27T00:16:11.586370Z",
     "shell.execute_reply": "2023-03-27T00:16:11.585311Z"
    },
    "papermill": {
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     "exception": false,
     "start_time": "2023-03-27T00:16:11.514010",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
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       "      <th>monetary</th>\n",
       "      <th>expected_purc_1_month</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356.00</th>\n",
       "      <td>43.14</td>\n",
       "      <td>46.71</td>\n",
       "      <td>3</td>\n",
       "      <td>630.63</td>\n",
       "      <td>0.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12358.00</th>\n",
       "      <td>21.29</td>\n",
       "      <td>21.71</td>\n",
       "      <td>2</td>\n",
       "      <td>584.03</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency     T  frequency  monetary  expected_purc_1_month\n",
       "customerID                                                           \n",
       "12347.00      52.14 52.71          7    615.71                   0.56\n",
       "12348.00      40.29 51.43          4    449.31                   0.36\n",
       "12352.00      37.14 42.57          8    313.25                   0.72\n",
       "12356.00      43.14 46.71          3    630.63                   0.34\n",
       "12358.00      21.29 21.71          2    584.03                   0.48"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Customers most likely to make a purchase in the 1-month period.\n",
    "clv_predict[\"expected_purc_1_month\"] = bgf.conditional_expected_number_of_purchases_up_to_time(4,\n",
    "                                                        clv_predict['frequency'],\n",
    "                                                        clv_predict['recency'],\n",
    "                                                        clv_predict['T'])\n",
    "clv_predict.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "37845143",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:11.646141Z",
     "iopub.status.busy": "2023-03-27T00:16:11.645320Z",
     "iopub.status.idle": "2023-03-27T00:16:11.667154Z",
     "shell.execute_reply": "2023-03-27T00:16:11.665833Z"
    },
    "papermill": {
     "duration": 0.053342,
     "end_time": "2023-03-27T00:16:11.669957",
     "exception": false,
     "start_time": "2023-03-27T00:16:11.616615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>expected_purc_1_month</th>\n",
       "      <th>expected_purc_3_month</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>customerID</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12347.00</th>\n",
       "      <td>52.14</td>\n",
       "      <td>52.71</td>\n",
       "      <td>7</td>\n",
       "      <td>615.71</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12348.00</th>\n",
       "      <td>40.29</td>\n",
       "      <td>51.43</td>\n",
       "      <td>4</td>\n",
       "      <td>449.31</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12352.00</th>\n",
       "      <td>37.14</td>\n",
       "      <td>42.57</td>\n",
       "      <td>8</td>\n",
       "      <td>313.25</td>\n",
       "      <td>0.72</td>\n",
       "      <td>2.15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12356.00</th>\n",
       "      <td>43.14</td>\n",
       "      <td>46.71</td>\n",
       "      <td>3</td>\n",
       "      <td>630.63</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12358.00</th>\n",
       "      <td>21.29</td>\n",
       "      <td>21.71</td>\n",
       "      <td>2</td>\n",
       "      <td>584.03</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12359.00</th>\n",
       "      <td>39.14</td>\n",
       "      <td>47.57</td>\n",
       "      <td>4</td>\n",
       "      <td>1090.24</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12360.00</th>\n",
       "      <td>21.14</td>\n",
       "      <td>28.86</td>\n",
       "      <td>3</td>\n",
       "      <td>887.35</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12362.00</th>\n",
       "      <td>41.71</td>\n",
       "      <td>42.43</td>\n",
       "      <td>10</td>\n",
       "      <td>522.62</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12363.00</th>\n",
       "      <td>18.86</td>\n",
       "      <td>34.86</td>\n",
       "      <td>2</td>\n",
       "      <td>276.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12364.00</th>\n",
       "      <td>14.86</td>\n",
       "      <td>16.29</td>\n",
       "      <td>4</td>\n",
       "      <td>328.27</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            recency     T  frequency  monetary  expected_purc_1_month  expected_purc_3_month\n",
       "customerID                                                                                  \n",
       "12347.00      52.14 52.71          7    615.71                   0.56                   1.67\n",
       "12348.00      40.29 51.43          4    449.31                   0.36                   1.09\n",
       "12352.00      37.14 42.57          8    313.25                   0.72                   2.15\n",
       "12356.00      43.14 46.71          3    630.63                   0.34                   1.02\n",
       "12358.00      21.29 21.71          2    584.03                   0.48                   1.43\n",
       "12359.00      39.14 47.57          4   1090.24                   0.39                   1.17\n",
       "12360.00      21.14 28.86          3    887.35                   0.47                   1.40\n",
       "12362.00      41.71 42.43         10    522.62                   0.89                   2.65\n",
       "12363.00      18.86 34.86          2    276.00                   0.30                   0.89\n",
       "12364.00      14.86 16.29          4    328.27                   0.86                   2.53"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Customers who are most likely to make a purchase in the 3-month period.\n",
    "\n",
    "clv_predict[\"expected_purc_3_month\"] = bgf.predict(4 * 3,\n",
    "                                               clv_predict['frequency'],\n",
    "                                               clv_predict['recency'],\n",
    "                                               clv_predict['T'])\n",
    "clv_predict.head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a0bcefe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:11.727021Z",
     "iopub.status.busy": "2023-03-27T00:16:11.726615Z",
     "iopub.status.idle": "2023-03-27T00:16:12.588831Z",
     "shell.execute_reply": "2023-03-27T00:16:12.587531Z"
    },
    "papermill": {
     "duration": 0.894209,
     "end_time": "2023-03-27T00:16:12.591715",
     "exception": false,
     "start_time": "2023-03-27T00:16:11.697506",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_period_transactions(bgf)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27a90bf2",
   "metadata": {
    "papermill": {
     "duration": 0.027557,
     "end_time": "2023-03-27T00:16:12.647266",
     "exception": false,
     "start_time": "2023-03-27T00:16:12.619709",
     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<a id = \"10\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>Calculation of CLTV</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "8b02282e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:12.705530Z",
     "iopub.status.busy": "2023-03-27T00:16:12.704717Z",
     "iopub.status.idle": "2023-03-27T00:16:12.710201Z",
     "shell.execute_reply": "2023-03-27T00:16:12.709229Z"
    },
    "papermill": {
     "duration": 0.03774,
     "end_time": "2023-03-27T00:16:12.712721",
     "exception": false,
     "start_time": "2023-03-27T00:16:12.674981",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Gamma gamma model\n",
    "ggf = GammaGammaFitter(penalizer_coef=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d5cd834d",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:12.772191Z",
     "iopub.status.busy": "2023-03-27T00:16:12.771315Z",
     "iopub.status.idle": "2023-03-27T00:16:12.830788Z",
     "shell.execute_reply": "2023-03-27T00:16:12.829331Z"
    },
    "papermill": {
     "duration": 0.093289,
     "end_time": "2023-03-27T00:16:12.834094",
     "exception": false,
     "start_time": "2023-03-27T00:16:12.740805",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifetimes.GammaGammaFitter: fitted with 2845 subjects, p: 3.80, q: 0.34, v: 3.74>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# setup of our gamma gamma model \n",
    "ggf.fit(clv_predict['frequency'], clv_predict['monetary'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "2eb717b9",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:12.929835Z",
     "iopub.status.busy": "2023-03-27T00:16:12.929274Z",
     "iopub.status.idle": "2023-03-27T00:16:12.944183Z",
     "shell.execute_reply": "2023-03-27T00:16:12.942838Z"
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID\n",
       "12406.00   1482.05\n",
       "16253.00   1482.05\n",
       "12435.00   1482.05\n",
       "16240.00   1482.05\n",
       "16000.00   1436.08\n",
       "12798.00   1436.08\n",
       "15749.00   1436.08\n",
       "16532.00   1436.08\n",
       "12536.00   1436.08\n",
       "16984.00   1429.04\n",
       "dtype: float64"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Calculation of expected average profit.\n",
    "\n",
    "ggf.conditional_expected_average_profit(clv_predict['frequency'],\n",
    "                                        clv_predict['monetary']).sort_values(ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "c29b7758",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:13.005693Z",
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       "      <td>0.34</td>\n",
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       "      <td>0.48</td>\n",
       "      <td>1.43</td>\n",
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      "text/plain": [
       "            recency     T  frequency  monetary  expected_purc_1_month  expected_purc_3_month  expected_average_profit\n",
       "customerID                                                                                                           \n",
       "12347.00      52.14 52.71          7    615.71                   0.56                   1.67                   631.85\n",
       "12348.00      40.29 51.43          4    449.31                   0.36                   1.09                   470.58\n",
       "12352.00      37.14 42.57          8    313.25                   0.72                   2.15                   320.65\n",
       "12356.00      43.14 46.71          3    630.63                   0.34                   1.02                   670.51\n",
       "12358.00      21.29 21.71          2    584.03                   0.48                   1.43                   641.34"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clv_predict[\"expected_average_profit\"] = ggf.conditional_expected_average_profit(clv_predict['frequency'],\n",
    "                                                                             clv_predict['monetary'])\n",
    "clv_predict.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "79c053c6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:13.083201Z",
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       "      <th></th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
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       "      <th>monetary</th>\n",
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       "      <th>customerID</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "    <tr>\n",
       "      <th>12406.00</th>\n",
       "      <td>23.00</td>\n",
       "      <td>26.43</td>\n",
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       "      <td>1.24</td>\n",
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       "      <th>16253.00</th>\n",
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       "      <td>0.11</td>\n",
       "      <td>0.32</td>\n",
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      "text/plain": [
       "            recency     T  frequency  monetary  expected_purc_1_month  expected_purc_3_month  expected_average_profit\n",
       "customerID                                                                                                           \n",
       "12406.00      23.00 26.43          2   1352.07                   0.42                   1.24                  1482.05\n",
       "16253.00      26.86 36.71          2   1352.07                   0.32                   0.94                  1482.05\n",
       "12435.00      26.86 38.43          2   1352.07                   0.30                   0.90                  1482.05\n",
       "16240.00       7.57 11.29          2   1352.07                   0.68                   2.00                  1482.05\n",
       "16000.00       0.00  0.57          3   1352.07                   1.63                   4.75                  1436.08\n",
       "12798.00      22.86 24.57          3   1352.07                   0.55                   1.64                  1436.08\n",
       "15749.00      13.86 47.71          3   1352.07                   0.11                   0.32                  1436.08\n",
       "16532.00      45.71 48.57          3   1352.07                   0.33                   0.99                  1436.08\n",
       "12536.00       2.14  8.71          3   1352.07                   0.84                   2.47                  1436.08\n",
       "16984.00       5.86 18.86          2   1303.65                   0.40                   1.19                  1429.04"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clv_predict.sort_values(\"expected_average_profit\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "aa895c92",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:13.163813Z",
     "iopub.status.busy": "2023-03-27T00:16:13.163353Z",
     "iopub.status.idle": "2023-03-27T00:16:13.222649Z",
     "shell.execute_reply": "2023-03-27T00:16:13.221216Z"
    },
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID\n",
       "12347.00   1124.29\n",
       "12358.00    974.46\n",
       "12352.00    733.91\n",
       "12356.00    726.14\n",
       "12348.00    543.92\n",
       "Name: clv, dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# calculating CLTV(Customer Lifetime Value)\n",
    "\n",
    "clv_result = ggf.customer_lifetime_value(bgf,\n",
    "                                   clv_predict['frequency'],\n",
    "                                   clv_predict['recency'],\n",
    "                                   clv_predict['T'],\n",
    "                                   clv_predict['monetary'],\n",
    "                                   time=3,  # 3 aylık\n",
    "                                   freq=\"W\",  # T'nin frekans bilgisi.\n",
    "                                   discount_rate=0.01)\n",
    "clv_result.head().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b2c20d38",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:13.287473Z",
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     "status": "completed"
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "customerID\n",
       "12347.00   1124.29\n",
       "12348.00    543.92\n",
       "12352.00    733.91\n",
       "12356.00    726.14\n",
       "12358.00    974.46\n",
       "Name: clv, dtype: float64"
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     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "clv_result.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9554bfa",
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    "execution": {
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       "      <td>641.34</td>\n",
       "      <td>974.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12359.00</td>\n",
       "      <td>39.14</td>\n",
       "      <td>47.57</td>\n",
       "      <td>4</td>\n",
       "      <td>1090.24</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1140.47</td>\n",
       "      <td>1425.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12360.00</td>\n",
       "      <td>21.14</td>\n",
       "      <td>28.86</td>\n",
       "      <td>3</td>\n",
       "      <td>887.35</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.40</td>\n",
       "      <td>942.94</td>\n",
       "      <td>1406.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12362.00</td>\n",
       "      <td>41.71</td>\n",
       "      <td>42.43</td>\n",
       "      <td>10</td>\n",
       "      <td>522.62</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.65</td>\n",
       "      <td>532.20</td>\n",
       "      <td>1497.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12363.00</td>\n",
       "      <td>18.86</td>\n",
       "      <td>34.86</td>\n",
       "      <td>2</td>\n",
       "      <td>276.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.89</td>\n",
       "      <td>304.16</td>\n",
       "      <td>286.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12364.00</td>\n",
       "      <td>14.86</td>\n",
       "      <td>16.29</td>\n",
       "      <td>4</td>\n",
       "      <td>328.27</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.53</td>\n",
       "      <td>344.08</td>\n",
       "      <td>926.08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  recency     T  frequency  monetary  expected_purc_1_month  expected_purc_3_month  expected_average_profit     clv\n",
       "0    12347.00    52.14 52.71          7    615.71                   0.56                   1.67                   631.85 1124.29\n",
       "1    12348.00    40.29 51.43          4    449.31                   0.36                   1.09                   470.58  543.92\n",
       "2    12352.00    37.14 42.57          8    313.25                   0.72                   2.15                   320.65  733.91\n",
       "3    12356.00    43.14 46.71          3    630.63                   0.34                   1.02                   670.51  726.14\n",
       "4    12358.00    21.29 21.71          2    584.03                   0.48                   1.43                   641.34  974.46\n",
       "5    12359.00    39.14 47.57          4   1090.24                   0.39                   1.17                  1140.47 1425.16\n",
       "6    12360.00    21.14 28.86          3    887.35                   0.47                   1.40                   942.94 1406.09\n",
       "7    12362.00    41.71 42.43         10    522.62                   0.89                   2.65                   532.20 1497.55\n",
       "8    12363.00    18.86 34.86          2    276.00                   0.30                   0.89                   304.16  286.68\n",
       "9    12364.00    14.86 16.29          4    328.27                   0.86                   2.53                   344.08  926.08"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's add the CLTV value we created to the dataframe.\n",
    "clv_result = clv_result.reset_index()\n",
    "clv_final = clv_predict.merge(clv_result, on=\"customerID\", how=\"left\")\n",
    "\n",
    "clv_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "3df20d69",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:13.446283Z",
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     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>customerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>expected_purc_1_month</th>\n",
       "      <th>expected_purc_3_month</th>\n",
       "      <th>expected_average_profit</th>\n",
       "      <th>clv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1257</th>\n",
       "      <td>14911.00</td>\n",
       "      <td>53.14</td>\n",
       "      <td>53.57</td>\n",
       "      <td>201</td>\n",
       "      <td>644.56</td>\n",
       "      <td>12.42</td>\n",
       "      <td>37.01</td>\n",
       "      <td>645.13</td>\n",
       "      <td>25403.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1122</th>\n",
       "      <td>14646.00</td>\n",
       "      <td>50.43</td>\n",
       "      <td>50.86</td>\n",
       "      <td>74</td>\n",
       "      <td>930.29</td>\n",
       "      <td>4.86</td>\n",
       "      <td>14.47</td>\n",
       "      <td>932.51</td>\n",
       "      <td>14354.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2761</th>\n",
       "      <td>18102.00</td>\n",
       "      <td>52.29</td>\n",
       "      <td>52.71</td>\n",
       "      <td>60</td>\n",
       "      <td>1055.94</td>\n",
       "      <td>3.85</td>\n",
       "      <td>11.47</td>\n",
       "      <td>1059.06</td>\n",
       "      <td>12922.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2458</th>\n",
       "      <td>17450.00</td>\n",
       "      <td>51.29</td>\n",
       "      <td>52.71</td>\n",
       "      <td>46</td>\n",
       "      <td>1129.33</td>\n",
       "      <td>2.97</td>\n",
       "      <td>8.85</td>\n",
       "      <td>1133.67</td>\n",
       "      <td>10669.55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>373</th>\n",
       "      <td>13089.00</td>\n",
       "      <td>52.29</td>\n",
       "      <td>53.00</td>\n",
       "      <td>97</td>\n",
       "      <td>550.00</td>\n",
       "      <td>6.11</td>\n",
       "      <td>18.20</td>\n",
       "      <td>551.02</td>\n",
       "      <td>10669.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1441</th>\n",
       "      <td>15311.00</td>\n",
       "      <td>53.29</td>\n",
       "      <td>53.57</td>\n",
       "      <td>91</td>\n",
       "      <td>574.75</td>\n",
       "      <td>5.70</td>\n",
       "      <td>16.98</td>\n",
       "      <td>575.89</td>\n",
       "      <td>10403.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>874</th>\n",
       "      <td>14156.00</td>\n",
       "      <td>51.57</td>\n",
       "      <td>53.29</td>\n",
       "      <td>55</td>\n",
       "      <td>883.68</td>\n",
       "      <td>3.48</td>\n",
       "      <td>10.38</td>\n",
       "      <td>886.54</td>\n",
       "      <td>9792.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>650</th>\n",
       "      <td>13694.00</td>\n",
       "      <td>52.71</td>\n",
       "      <td>53.57</td>\n",
       "      <td>50</td>\n",
       "      <td>950.56</td>\n",
       "      <td>3.18</td>\n",
       "      <td>9.48</td>\n",
       "      <td>953.94</td>\n",
       "      <td>9623.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>843</th>\n",
       "      <td>14096.00</td>\n",
       "      <td>13.86</td>\n",
       "      <td>14.71</td>\n",
       "      <td>17</td>\n",
       "      <td>1015.96</td>\n",
       "      <td>2.87</td>\n",
       "      <td>8.47</td>\n",
       "      <td>1026.62</td>\n",
       "      <td>9245.51</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2652</th>\n",
       "      <td>17841.00</td>\n",
       "      <td>53.00</td>\n",
       "      <td>53.57</td>\n",
       "      <td>124</td>\n",
       "      <td>330.58</td>\n",
       "      <td>7.71</td>\n",
       "      <td>22.97</td>\n",
       "      <td>331.07</td>\n",
       "      <td>8090.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      customerID  recency     T  frequency  monetary  expected_purc_1_month  expected_purc_3_month  expected_average_profit      clv\n",
       "1257    14911.00    53.14 53.57        201    644.56                  12.42                  37.01                   645.13 25403.66\n",
       "1122    14646.00    50.43 50.86         74    930.29                   4.86                  14.47                   932.51 14354.67\n",
       "2761    18102.00    52.29 52.71         60   1055.94                   3.85                  11.47                  1059.06 12922.23\n",
       "2458    17450.00    51.29 52.71         46   1129.33                   2.97                   8.85                  1133.67 10669.55\n",
       "373     13089.00    52.29 53.00         97    550.00                   6.11                  18.20                   551.02 10669.18\n",
       "1441    15311.00    53.29 53.57         91    574.75                   5.70                  16.98                   575.89 10403.22\n",
       "874     14156.00    51.57 53.29         55    883.68                   3.48                  10.38                   886.54  9792.36\n",
       "650     13694.00    52.71 53.57         50    950.56                   3.18                   9.48                   953.94  9623.84\n",
       "843     14096.00    13.86 14.71         17   1015.96                   2.87                   8.47                  1026.62  9245.51\n",
       "2652    17841.00    53.00 53.57        124    330.58                   7.71                  22.97                   331.07  8090.87"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clv_final.sort_values(by=\"clv\", ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a8fb4be",
   "metadata": {
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   "source": [
    "<a id = \"11\"></a>\n",
    "<p style= \"background-color:#3F497F;font-family:Georgia;color:#FEFBE9;font-size:180%;text-align:center;border-radius:20px 60px;overflow:hidden;border-style:dotted;border-width:1.8px;border-color:#FEFBE9;\"><b>Segmentation With CLV</b></p>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "8063b05e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2023-03-27T00:16:13.586774Z",
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     "shell.execute_reply": "2023-03-27T00:16:13.605313Z"
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     "start_time": "2023-03-27T00:16:13.554821",
     "status": "completed"
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   },
   "outputs": [
    {
     "data": {
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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>customerID</th>\n",
       "      <th>recency</th>\n",
       "      <th>T</th>\n",
       "      <th>frequency</th>\n",
       "      <th>monetary</th>\n",
       "      <th>expected_purc_1_month</th>\n",
       "      <th>expected_purc_3_month</th>\n",
       "      <th>expected_average_profit</th>\n",
       "      <th>clv</th>\n",
       "      <th>segment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12347.00</td>\n",
       "      <td>52.14</td>\n",
       "      <td>52.71</td>\n",
       "      <td>7</td>\n",
       "      <td>615.71</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.67</td>\n",
       "      <td>631.85</td>\n",
       "      <td>1124.29</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>12348.00</td>\n",
       "      <td>40.29</td>\n",
       "      <td>51.43</td>\n",
       "      <td>4</td>\n",
       "      <td>449.31</td>\n",
       "      <td>0.36</td>\n",
       "      <td>1.09</td>\n",
       "      <td>470.58</td>\n",
       "      <td>543.92</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12352.00</td>\n",
       "      <td>37.14</td>\n",
       "      <td>42.57</td>\n",
       "      <td>8</td>\n",
       "      <td>313.25</td>\n",
       "      <td>0.72</td>\n",
       "      <td>2.15</td>\n",
       "      <td>320.65</td>\n",
       "      <td>733.91</td>\n",
       "      <td>B</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12356.00</td>\n",
       "      <td>43.14</td>\n",
       "      <td>46.71</td>\n",
       "      <td>3</td>\n",
       "      <td>630.63</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.02</td>\n",
       "      <td>670.51</td>\n",
       "      <td>726.14</td>\n",
       "      <td>B</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12358.00</td>\n",
       "      <td>21.29</td>\n",
       "      <td>21.71</td>\n",
       "      <td>2</td>\n",
       "      <td>584.03</td>\n",
       "      <td>0.48</td>\n",
       "      <td>1.43</td>\n",
       "      <td>641.34</td>\n",
       "      <td>974.46</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12359.00</td>\n",
       "      <td>39.14</td>\n",
       "      <td>47.57</td>\n",
       "      <td>4</td>\n",
       "      <td>1090.24</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.17</td>\n",
       "      <td>1140.47</td>\n",
       "      <td>1425.16</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12360.00</td>\n",
       "      <td>21.14</td>\n",
       "      <td>28.86</td>\n",
       "      <td>3</td>\n",
       "      <td>887.35</td>\n",
       "      <td>0.47</td>\n",
       "      <td>1.40</td>\n",
       "      <td>942.94</td>\n",
       "      <td>1406.09</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12362.00</td>\n",
       "      <td>41.71</td>\n",
       "      <td>42.43</td>\n",
       "      <td>10</td>\n",
       "      <td>522.62</td>\n",
       "      <td>0.89</td>\n",
       "      <td>2.65</td>\n",
       "      <td>532.20</td>\n",
       "      <td>1497.55</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12363.00</td>\n",
       "      <td>18.86</td>\n",
       "      <td>34.86</td>\n",
       "      <td>2</td>\n",
       "      <td>276.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.89</td>\n",
       "      <td>304.16</td>\n",
       "      <td>286.68</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12364.00</td>\n",
       "      <td>14.86</td>\n",
       "      <td>16.29</td>\n",
       "      <td>4</td>\n",
       "      <td>328.27</td>\n",
       "      <td>0.86</td>\n",
       "      <td>2.53</td>\n",
       "      <td>344.08</td>\n",
       "      <td>926.08</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   customerID  recency     T  frequency  monetary  expected_purc_1_month  expected_purc_3_month  expected_average_profit     clv segment\n",
       "0    12347.00    52.14 52.71          7    615.71                   0.56                   1.67                   631.85 1124.29       A\n",
       "1    12348.00    40.29 51.43          4    449.31                   0.36                   1.09                   470.58  543.92       B\n",
       "2    12352.00    37.14 42.57          8    313.25                   0.72                   2.15                   320.65  733.91       B\n",
       "3    12356.00    43.14 46.71          3    630.63                   0.34                   1.02                   670.51  726.14       B\n",
       "4    12358.00    21.29 21.71          2    584.03                   0.48                   1.43                   641.34  974.46       A\n",
       "5    12359.00    39.14 47.57          4   1090.24                   0.39                   1.17                  1140.47 1425.16       A\n",
       "6    12360.00    21.14 28.86          3    887.35                   0.47                   1.40                   942.94 1406.09       A\n",
       "7    12362.00    41.71 42.43         10    522.62                   0.89                   2.65                   532.20 1497.55       A\n",
       "8    12363.00    18.86 34.86          2    276.00                   0.30                   0.89                   304.16  286.68       C\n",
       "9    12364.00    14.86 16.29          4    328.27                   0.86                   2.53                   344.08  926.08       A"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In order to make effective observations, let's segment according to CLTV values.\n",
    "\n",
    "clv_final[\"segment\"] = pd.qcut(clv_final[\"clv\"], 4, labels=[\"D\", \"C\", \"B\", \"A\"])\n",
    "\n",
    "clv_final.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a16e7c12",
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    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">customerID</th>\n",
       "      <th colspan=\"3\" halign=\"left\">recency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">T</th>\n",
       "      <th colspan=\"3\" halign=\"left\">frequency</th>\n",
       "      <th colspan=\"3\" halign=\"left\">monetary</th>\n",
       "      <th colspan=\"3\" halign=\"left\">expected_purc_1_month</th>\n",
       "      <th colspan=\"3\" halign=\"left\">expected_purc_3_month</th>\n",
       "      <th colspan=\"3\" halign=\"left\">expected_average_profit</th>\n",
       "      <th colspan=\"3\" halign=\"left\">clv</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
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       "      <th>mean</th>\n",
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       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>segment</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",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>712</td>\n",
       "      <td>11068782.00</td>\n",
       "      <td>15546.04</td>\n",
       "      <td>712</td>\n",
       "      <td>15626.57</td>\n",
       "      <td>21.95</td>\n",
       "      <td>712</td>\n",
       "      <td>28852.71</td>\n",
       "      <td>40.52</td>\n",
       "      <td>712</td>\n",
       "      <td>2172</td>\n",
       "      <td>3.05</td>\n",
       "      <td>712</td>\n",
       "      <td>134207.89</td>\n",
       "      <td>188.49</td>\n",
       "      <td>712</td>\n",
       "      <td>199.16</td>\n",
       "      <td>0.28</td>\n",
       "      <td>712</td>\n",
       "      <td>591.08</td>\n",
       "      <td>0.83</td>\n",
       "      <td>712</td>\n",
       "      <td>145466.27</td>\n",
       "      <td>204.31</td>\n",
       "      <td>712</td>\n",
       "      <td>102592.82</td>\n",
       "      <td>144.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>711</td>\n",
       "      <td>10891907.00</td>\n",
       "      <td>15319.14</td>\n",
       "      <td>711</td>\n",
       "      <td>21871.43</td>\n",
       "      <td>30.76</td>\n",
       "      <td>711</td>\n",
       "      <td>27260.43</td>\n",
       "      <td>38.34</td>\n",
       "      <td>711</td>\n",
       "      <td>2920</td>\n",
       "      <td>4.11</td>\n",
       "      <td>711</td>\n",
       "      <td>195709.86</td>\n",
       "      <td>275.26</td>\n",
       "      <td>711</td>\n",
       "      <td>339.67</td>\n",
       "      <td>0.48</td>\n",
       "      <td>711</td>\n",
       "      <td>1007.92</td>\n",
       "      <td>1.42</td>\n",
       "      <td>711</td>\n",
       "      <td>208782.89</td>\n",
       "      <td>293.65</td>\n",
       "      <td>711</td>\n",
       "      <td>271322.19</td>\n",
       "      <td>381.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>711</td>\n",
       "      <td>10909320.00</td>\n",
       "      <td>15343.63</td>\n",
       "      <td>711</td>\n",
       "      <td>20930.43</td>\n",
       "      <td>29.44</td>\n",
       "      <td>711</td>\n",
       "      <td>24792.29</td>\n",
       "      <td>34.87</td>\n",
       "      <td>711</td>\n",
       "      <td>3863</td>\n",
       "      <td>5.43</td>\n",
       "      <td>711</td>\n",
       "      <td>262716.27</td>\n",
       "      <td>369.50</td>\n",
       "      <td>711</td>\n",
       "      <td>455.59</td>\n",
       "      <td>0.64</td>\n",
       "      <td>711</td>\n",
       "      <td>1350.24</td>\n",
       "      <td>1.90</td>\n",
       "      <td>711</td>\n",
       "      <td>276899.10</td>\n",
       "      <td>389.45</td>\n",
       "      <td>711</td>\n",
       "      <td>489439.19</td>\n",
       "      <td>688.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>711</td>\n",
       "      <td>10636202.00</td>\n",
       "      <td>14959.50</td>\n",
       "      <td>711</td>\n",
       "      <td>22412.43</td>\n",
       "      <td>31.52</td>\n",
       "      <td>711</td>\n",
       "      <td>24678.29</td>\n",
       "      <td>34.71</td>\n",
       "      <td>711</td>\n",
       "      <td>8087</td>\n",
       "      <td>11.37</td>\n",
       "      <td>711</td>\n",
       "      <td>408233.77</td>\n",
       "      <td>574.17</td>\n",
       "      <td>711</td>\n",
       "      <td>769.38</td>\n",
       "      <td>1.08</td>\n",
       "      <td>711</td>\n",
       "      <td>2282.08</td>\n",
       "      <td>3.21</td>\n",
       "      <td>711</td>\n",
       "      <td>424922.69</td>\n",
       "      <td>597.64</td>\n",
       "      <td>711</td>\n",
       "      <td>1288258.25</td>\n",
       "      <td>1811.90</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        customerID                      recency                    T                frequency             monetary                  expected_purc_1_month  \\\n",
       "             count         sum     mean   count      sum  mean count      sum  mean     count   sum  mean    count       sum   mean                 count   \n",
       "segment                                                                                                                                                     \n",
       "D              712 11068782.00 15546.04     712 15626.57 21.95   712 28852.71 40.52       712  2172  3.05      712 134207.89 188.49                   712   \n",
       "C              711 10891907.00 15319.14     711 21871.43 30.76   711 27260.43 38.34       711  2920  4.11      711 195709.86 275.26                   711   \n",
       "B              711 10909320.00 15343.63     711 20930.43 29.44   711 24792.29 34.87       711  3863  5.43      711 262716.27 369.50                   711   \n",
       "A              711 10636202.00 14959.50     711 22412.43 31.52   711 24678.29 34.71       711  8087 11.37      711 408233.77 574.17                   711   \n",
       "\n",
       "                    expected_purc_3_month              expected_average_profit                    clv                     \n",
       "           sum mean                 count     sum mean                   count       sum   mean count        sum    mean  \n",
       "segment                                                                                                                   \n",
       "D       199.16 0.28                   712  591.08 0.83                     712 145466.27 204.31   712  102592.82  144.09  \n",
       "C       339.67 0.48                   711 1007.92 1.42                     711 208782.89 293.65   711  271322.19  381.61  \n",
       "B       455.59 0.64                   711 1350.24 1.90                     711 276899.10 389.45   711  489439.19  688.38  \n",
       "A       769.38 1.08                   711 2282.08 3.21                     711 424922.69 597.64   711 1288258.25 1811.90  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clv_final.groupby(\"segment\").agg({\"count\", \"mean\", \"sum\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e719f7f7",
   "metadata": {
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     "status": "completed"
    },
    "tags": []
   },
   "source": [
    "<p style=\"padding:15px;\n",
    "background-color:#f4ebdc;\n",
    "margin:0; color:#B3005E; border:2px dotted #C689C6; font-family:Charter; font-weight: bold; font-size:250%; text-align:center; overflow:hidden; font-weight:500\">If you like this then please UPVOTE this. Your opinions and suggestions are very important to me...Thanks <img src=\"https://media.giphy.com/media/WUlplcMpOCEmTGBtBW/giphy.gif\" width=\"100\"> <div style=\"float:center;margin-left:270px;max-width:50%;\">"
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  },
  {
   "cell_type": "markdown",
   "id": "46102590",
   "metadata": {
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     "duration": 0.028389,
     "end_time": "2023-03-27T00:16:13.834464",
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     "status": "completed"
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    "<p style=\"padding:15px;\n",
    "background-color:#f4ebdc;\n",
    "margin:0; color:#B3005E; border:2px dotted #C689C6; font-family:Charter; font-weight: bold; font-size:250%; text-align:center; overflow:hidden; font-weight:500\">\n",
    "FOR MORE:</p> \n",
    "\n",
    "https://www.linkedin.com/in/serdar-ozturk/\n",
    "\n",
    "https://github.com/Serdar-Trk\n",
    "\n",
    "https://medium.com/@serdar.f95\n",
    "\n",
    "<p style=\"padding:15px;\n",
    "background-color:#f4ebdc;\n",
    "margin:0; color:#B3005E; border:2px dotted #C689C6; font-family:Charter; font-weight: bold; font-size:250%; text-align:center; overflow:hidden; font-weight:500\">CREDITS:</p> \n",
    "\n",
    "[https://numpy.org/](http://)\n",
    "\n",
    "[https://seaborn.pydata.org/](http://)\n",
    "\n",
    "[https://pandas.pydata.org](http://)\n",
    "\n",
    "[https://pandas.pydata.org/](http://)\n",
    "\n",
    "[https://learning.miuul.com](http://)"
   ]
  }
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