DSF_Retail_RFM_Analysis.ipynb

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    "colab": {
      "name": "DSF_Retail_RFM_Analysis.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "KxzFCMU8awKU",
        "colab_type": "code",
        "outputId": "03f48d49-0d17-4e0c-88f1-0fcd19887397",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fXTf-Z9sbRxf",
        "colab_type": "code",
        "outputId": "c6c1c947-c2ca-436f-fee5-b98ebeff5c1a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "cd 'gdrive/My Drive/DSF Project: Retail/'"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/gdrive/My Drive/DSF Project: Retail\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WMKRSbI7bXU6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import cv2\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from mpl_toolkits.mplot3d import Axes3D\n",
        "import time\n",
        "import warnings\n",
        "import seaborn as sns"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KM8O89oobrHI",
        "colab_type": "text"
      },
      "source": [
        "# **RFM Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lo4T84BjMiI0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "columns = ['Date','Transaction Time','Customer Number','Receipt Number','Item Number','Item Description','Net Sales']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E-70IkpKSRwr",
        "colab_type": "code",
        "outputId": "3244242a-68c6-4ddc-d6bd-45bdd3dea0db",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "train_RFM = pd.read_csv(r'costello_ace_2017-2018.csv',usecols= columns)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/IPython/core/interactiveshell.py:2718: DtypeWarning: Columns (10) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "  interactivity=interactivity, compiler=compiler, result=result)\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0yQQwaUFmsyK",
        "colab_type": "code",
        "outputId": "b28aa2a0-d217-4726-f780-0b8ba53faec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "import gc\n",
        "gc.collect()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IHpnS69ahjSQ",
        "colab_type": "code",
        "outputId": "0b6d809d-f869-4b92-dad9-5fc2b8787c5a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "train_RFM.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17328044, 7)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nAo4Ly8-TE2L",
        "colab_type": "code",
        "outputId": "e99de95a-2c8a-46fa-b093-060e93bb980f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_RFM.tail(50)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
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              "    <tr>\n",
              "      <th>17328029</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>1011006</td>\n",
              "      <td>JNT CMPND ALL PURP 1.75P</td>\n",
              "      <td>5.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328030</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>1504125</td>\n",
              "      <td>GOO GONE REMOVER 2OZ</td>\n",
              "      <td>2.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328031</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>9269862</td>\n",
              "      <td>CMN Donations</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328032</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:19:37</td>\n",
              "      <td>442546</td>\n",
              "      <td>B02346</td>\n",
              "      <td>7055973</td>\n",
              "      <td>ADJUSTABLE FLAG BRACKET</td>\n",
              "      <td>17.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328033</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:19:37</td>\n",
              "      <td>442546</td>\n",
              "      <td>B02346</td>\n",
              "      <td>7098189</td>\n",
              "      <td>5' ALUM FLAG POLE</td>\n",
              "      <td>15.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328034</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:20:40</td>\n",
              "      <td>500218</td>\n",
              "      <td>H88642</td>\n",
              "      <td>H80051</td>\n",
              "      <td>PHILIP PN SMS Z 8X1 1/4  100</td>\n",
              "      <td>2.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328035</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:20:40</td>\n",
              "      <td>500218</td>\n",
              "      <td>H88642</td>\n",
              "      <td>H48023</td>\n",
              "      <td>XL-TR WSH LATH SP 8X1.25</td>\n",
              "      <td>2.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328036</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:21:20</td>\n",
              "      <td>*5</td>\n",
              "      <td>B02347</td>\n",
              "      <td>7104888</td>\n",
              "      <td>TRAP SPIDER &amp; CRICKET PK</td>\n",
              "      <td>13.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328037</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:37:18</td>\n",
              "      <td>*5</td>\n",
              "      <td>B52086</td>\n",
              "      <td>S9596</td>\n",
              "      <td>9A19 LED 120V 2700K 4PK</td>\n",
              "      <td>7.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328038</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>3016607</td>\n",
              "      <td>BATTRY ALKLN AAA CD4 ACE</td>\n",
              "      <td>1.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328039</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>10541</td>\n",
              "      <td>PALMYRA PUSHBROOM 18\"</td>\n",
              "      <td>7.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328040</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>12172</td>\n",
              "      <td>WIRE BRUSH MINI SS 3PK</td>\n",
              "      <td>1.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328041</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>1455567</td>\n",
              "      <td>BRUSH SCRUB HANG-UP</td>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328042</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>2301166</td>\n",
              "      <td>CM SLIDE LOCK KNIFE</td>\n",
              "      <td>5.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328043</th>\n",
              "      <td>\u001a</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Date Transaction Time  ...              Item Description Net Sales\n",
              "17327994  9/30/2018         18:07:47  ...      TRAP SPIDER & CRICKET PK     13.98\n",
              "17327995  9/30/2018         18:07:47  ...                        $5 OFF        -5\n",
              "17327996  9/30/2018         18:07:52  ...      CLEANR KABOOM TLEGRT32OZ      4.87\n",
              "17327997  9/30/2018         18:07:52  ...       CLEANR BATHROOM 20OZ SB      4.45\n",
              "17327998  9/30/2018         18:07:52  ...      LINER MAGNTC 70X71 CLEAR       1.7\n",
              "17327999  9/30/2018         18:08:26  ...       ACE BAG 13GAL 22CT DRAW      3.36\n",
              "17328000  9/30/2018         18:08:45  ...      WALLBOARD ANC KIT 21NC#8     19.99\n",
              "17328001  9/30/2018         18:08:45  ...      BATTRY ALKLN DURA 9V CD2     10.49\n",
              "17328002  9/30/2018         18:09:20  ...     60 WATT CL APPLIANCE BULB      8.97\n",
              "17328003  9/30/2018         18:09:20  ...        FLYING INSECT KLLR18OZ         0\n",
              "17328004  9/30/2018         18:09:20  ...        FLYING INSECT KLLR18OZ      3.99\n",
              "17328005  9/30/2018         18:09:21  ...         SHARKBITE TEE 1/2\" LF     12.99\n",
              "17328006  9/30/2018         18:09:21  ...      SB HI-EAR ELBOW 1/2\"F LF      8.99\n",
              "17328007  9/30/2018         18:09:50  ...      STAPLE COXL GRY7/16\"CD15      7.98\n",
              "17328008  9/30/2018         18:09:50  ...                        $5 OFF        -5\n",
              "17328009  9/30/2018         18:10:59  ...        FILLR WOOD STNABLE 1OZ      7.49\n",
              "17328010  9/30/2018         18:10:59  ...       PUTTY PAINTRS 1/2PT DAP      5.99\n",
              "17328011  9/30/2018         18:10:59  ...        PUTTY WOOD RED MAHOGNY      4.99\n",
              "17328012  9/30/2018         18:11:00  ...                     FASTENERS       0.6\n",
              "17328013  9/30/2018         18:11:00  ...                     FASTENERS      9.54\n",
              "17328014  9/30/2018         18:11:38  ...                     FASTENERS      3.57\n",
              "17328015  9/30/2018         18:11:38  ...                     FASTENERS      2.67\n",
              "17328016  9/30/2018         18:11:38  ...                     FASTENERS         2\n",
              "17328017  9/30/2018         18:11:38  ...                     FASTENERS      0.98\n",
              "17328018  9/30/2018         18:14:20  ...      SANDPAPR ALOX9X11 MED5PK      1.17\n",
              "17328019  9/30/2018         18:16:03  ...       POSTER STRPS SM COMMAND     10.49\n",
              "17328020  9/30/2018         18:16:03  ...                DR PEPPER 20OZ      1.22\n",
              "17328021  9/30/2018         18:16:03  ...                 CMN Donations         1\n",
              "17328022  9/30/2018         18:16:03  ...        PATTIE YORK MINT 1.5OZ      0.89\n",
              "17328023  9/30/2018         18:17:28  ...      COLOR SAMPLE BM BASE1 PT      6.99\n",
              "17328024  9/30/2018         18:17:28  ...      STAIN MINWX8OZ WTHRD OAK      7.99\n",
              "17328025  9/30/2018         18:17:28  ...      STAIN MINWX 8OZ DRFTWOOD      7.99\n",
              "17328026  9/30/2018         18:17:30  ...              REVENGE RAT TRAP      2.99\n",
              "17328027  9/30/2018         18:17:30  ...               TRAP RAT VICTOR      3.99\n",
              "17328028  9/30/2018         18:17:30  ...      ANCHOR 10-12W/WH SCRWCD4      2.69\n",
              "17328029  9/30/2018         18:17:30  ...      JNT CMPND ALL PURP 1.75P      5.99\n",
              "17328030  9/30/2018         18:17:30  ...          GOO GONE REMOVER 2OZ      2.99\n",
              "17328031  9/30/2018         18:17:30  ...                 CMN Donations         1\n",
              "17328032  9/30/2018         18:19:37  ...       ADJUSTABLE FLAG BRACKET     17.99\n",
              "17328033  9/30/2018         18:19:37  ...             5' ALUM FLAG POLE     15.99\n",
              "17328034  9/30/2018         18:20:40  ...  PHILIP PN SMS Z 8X1 1/4  100      2.88\n",
              "17328035  9/30/2018         18:20:40  ...      XL-TR WSH LATH SP 8X1.25      2.35\n",
              "17328036  9/30/2018         18:21:20  ...      TRAP SPIDER & CRICKET PK     13.98\n",
              "17328037  9/30/2018         18:37:18  ...       9A19 LED 120V 2700K 4PK      7.99\n",
              "17328038  9/30/2018         20:07:23  ...      BATTRY ALKLN AAA CD4 ACE      1.06\n",
              "17328039  9/30/2018         20:07:23  ...         PALMYRA PUSHBROOM 18\"       7.7\n",
              "17328040  9/30/2018         20:07:23  ...        WIRE BRUSH MINI SS 3PK      1.21\n",
              "17328041  9/30/2018         20:07:23  ...           BRUSH SCRUB HANG-UP      1.23\n",
              "17328042  9/30/2018         20:07:23  ...           CM SLIDE LOCK KNIFE      5.67\n",
              "17328043          \u001a              NaN  ...                           NaN       NaN\n",
              "\n",
              "[50 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YdPEpsZAsDz2",
        "colab_type": "text"
      },
      "source": [
        "**Data Preprocessing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N5hRhpGvTdsF",
        "colab_type": "code",
        "outputId": "ba931c83-deba-4052-e1ee-bb55e2a616a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "train_RFM.isna().sum()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                 0\n",
              "Transaction Time     1\n",
              "Customer Number     63\n",
              "Receipt Number       1\n",
              "Item Number          1\n",
              "Item Description     1\n",
              "Net Sales            1\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YCPZxPQmZ07X",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM = train_RFM.drop(train_RFM.loc[train_RFM['Customer Number'].isnull()].index) # Removing all record with NaN value\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdQxG6UNcZTQ",
        "colab_type": "code",
        "outputId": "d89ef50a-b96f-4749-fea4-249b89245fa8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 151
        }
      },
      "source": [
        "train_RFM.isna().sum()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                0\n",
              "Transaction Time    0\n",
              "Customer Number     0\n",
              "Receipt Number      0\n",
              "Item Number         0\n",
              "Item Description    0\n",
              "Net Sales           0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F8LB1m1iZ6Gy",
        "colab_type": "code",
        "outputId": "04b71711-dfa8-4d2e-dce2-3a7298d5559c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 639
        }
      },
      "source": [
        "train_RFM.tail(20)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Transaction Time</th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Receipt Number</th>\n",
              "      <th>Item Number</th>\n",
              "      <th>Item Description</th>\n",
              "      <th>Net Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>17328023</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:28</td>\n",
              "      <td>145989</td>\n",
              "      <td>H88641</td>\n",
              "      <td>B01271XH</td>\n",
              "      <td>COLOR SAMPLE BM BASE1 PT</td>\n",
              "      <td>6.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328024</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:28</td>\n",
              "      <td>145989</td>\n",
              "      <td>H88641</td>\n",
              "      <td>1515014</td>\n",
              "      <td>STAIN MINWX8OZ WTHRD OAK</td>\n",
              "      <td>7.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328025</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:28</td>\n",
              "      <td>145989</td>\n",
              "      <td>H88641</td>\n",
              "      <td>12389</td>\n",
              "      <td>STAIN MINWX 8OZ DRFTWOOD</td>\n",
              "      <td>7.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328026</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>AR47070</td>\n",
              "      <td>REVENGE RAT TRAP</td>\n",
              "      <td>2.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328027</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>70737</td>\n",
              "      <td>TRAP RAT VICTOR</td>\n",
              "      <td>3.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328028</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>5333075</td>\n",
              "      <td>ANCHOR 10-12W/WH SCRWCD4</td>\n",
              "      <td>2.69</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328029</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>1011006</td>\n",
              "      <td>JNT CMPND ALL PURP 1.75P</td>\n",
              "      <td>5.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328030</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>1504125</td>\n",
              "      <td>GOO GONE REMOVER 2OZ</td>\n",
              "      <td>2.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328031</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:17:30</td>\n",
              "      <td>297854</td>\n",
              "      <td>B52085</td>\n",
              "      <td>9269862</td>\n",
              "      <td>CMN Donations</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328032</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:19:37</td>\n",
              "      <td>442546</td>\n",
              "      <td>B02346</td>\n",
              "      <td>7055973</td>\n",
              "      <td>ADJUSTABLE FLAG BRACKET</td>\n",
              "      <td>17.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328033</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:19:37</td>\n",
              "      <td>442546</td>\n",
              "      <td>B02346</td>\n",
              "      <td>7098189</td>\n",
              "      <td>5' ALUM FLAG POLE</td>\n",
              "      <td>15.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328034</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:20:40</td>\n",
              "      <td>500218</td>\n",
              "      <td>H88642</td>\n",
              "      <td>H80051</td>\n",
              "      <td>PHILIP PN SMS Z 8X1 1/4  100</td>\n",
              "      <td>2.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328035</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:20:40</td>\n",
              "      <td>500218</td>\n",
              "      <td>H88642</td>\n",
              "      <td>H48023</td>\n",
              "      <td>XL-TR WSH LATH SP 8X1.25</td>\n",
              "      <td>2.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328036</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:21:20</td>\n",
              "      <td>*5</td>\n",
              "      <td>B02347</td>\n",
              "      <td>7104888</td>\n",
              "      <td>TRAP SPIDER &amp; CRICKET PK</td>\n",
              "      <td>13.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328037</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>18:37:18</td>\n",
              "      <td>*5</td>\n",
              "      <td>B52086</td>\n",
              "      <td>S9596</td>\n",
              "      <td>9A19 LED 120V 2700K 4PK</td>\n",
              "      <td>7.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328038</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>3016607</td>\n",
              "      <td>BATTRY ALKLN AAA CD4 ACE</td>\n",
              "      <td>1.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328039</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>10541</td>\n",
              "      <td>PALMYRA PUSHBROOM 18\"</td>\n",
              "      <td>7.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328040</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>12172</td>\n",
              "      <td>WIRE BRUSH MINI SS 3PK</td>\n",
              "      <td>1.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328041</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>1455567</td>\n",
              "      <td>BRUSH SCRUB HANG-UP</td>\n",
              "      <td>1.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17328042</th>\n",
              "      <td>9/30/2018</td>\n",
              "      <td>20:07:23</td>\n",
              "      <td>99989</td>\n",
              "      <td>474</td>\n",
              "      <td>2301166</td>\n",
              "      <td>CM SLIDE LOCK KNIFE</td>\n",
              "      <td>5.67</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "               Date Transaction Time  ...              Item Description Net Sales\n",
              "17328023  9/30/2018         18:17:28  ...      COLOR SAMPLE BM BASE1 PT      6.99\n",
              "17328024  9/30/2018         18:17:28  ...      STAIN MINWX8OZ WTHRD OAK      7.99\n",
              "17328025  9/30/2018         18:17:28  ...      STAIN MINWX 8OZ DRFTWOOD      7.99\n",
              "17328026  9/30/2018         18:17:30  ...              REVENGE RAT TRAP      2.99\n",
              "17328027  9/30/2018         18:17:30  ...               TRAP RAT VICTOR      3.99\n",
              "17328028  9/30/2018         18:17:30  ...      ANCHOR 10-12W/WH SCRWCD4      2.69\n",
              "17328029  9/30/2018         18:17:30  ...      JNT CMPND ALL PURP 1.75P      5.99\n",
              "17328030  9/30/2018         18:17:30  ...          GOO GONE REMOVER 2OZ      2.99\n",
              "17328031  9/30/2018         18:17:30  ...                 CMN Donations         1\n",
              "17328032  9/30/2018         18:19:37  ...       ADJUSTABLE FLAG BRACKET     17.99\n",
              "17328033  9/30/2018         18:19:37  ...             5' ALUM FLAG POLE     15.99\n",
              "17328034  9/30/2018         18:20:40  ...  PHILIP PN SMS Z 8X1 1/4  100      2.88\n",
              "17328035  9/30/2018         18:20:40  ...      XL-TR WSH LATH SP 8X1.25      2.35\n",
              "17328036  9/30/2018         18:21:20  ...      TRAP SPIDER & CRICKET PK     13.98\n",
              "17328037  9/30/2018         18:37:18  ...       9A19 LED 120V 2700K 4PK      7.99\n",
              "17328038  9/30/2018         20:07:23  ...      BATTRY ALKLN AAA CD4 ACE      1.06\n",
              "17328039  9/30/2018         20:07:23  ...         PALMYRA PUSHBROOM 18\"       7.7\n",
              "17328040  9/30/2018         20:07:23  ...        WIRE BRUSH MINI SS 3PK      1.21\n",
              "17328041  9/30/2018         20:07:23  ...           BRUSH SCRUB HANG-UP      1.23\n",
              "17328042  9/30/2018         20:07:23  ...           CM SLIDE LOCK KNIFE      5.67\n",
              "\n",
              "[20 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6SkchylRpaS4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YO4nqBhZoGlD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#train_RFM['Date'] = train_RFM['Date'].astype('str')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mOzK-lnvn1MS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM=train_RFM.sort_values(by = ['Date'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FJ-HLBmFp_Iv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "size = train_RFM.shape[0]-25\n",
        "train_RFM = train_RFM[0:size]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a5Exs6aljs3x",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "train_RFM['Date'] = pd.to_datetime(train_RFM['Date'])\n",
        "#train_RFM['year'] = train_RFM['Date'].dt.year\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vq2qkpTDp6x_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM['year'], train_RFM['months'] = train_RFM['Date'].dt.year, train_RFM['Date'].dt.month"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0MbBRanFqDQ-",
        "colab_type": "code",
        "outputId": "8a2dfadf-ad71-4ac3-acec-008eff815664",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "train_RFM"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Receipt Number</th>\n",
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              "    <tr>\n",
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              "      <td>2017-01-01</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>*5</td>\n",
              "      <td>I29245</td>\n",
              "      <td>PSWITCH832</td>\n",
              "      <td>LED NIGHT LIGHT SWITCH 2PK</td>\n",
              "      <td>11.24</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863920</th>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>3202181</td>\n",
              "      <td>BLANK WALLPLATE 1G WHITE</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863921</th>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>33180</td>\n",
              "      <td>TAPE ELECT3/4X60'VYL ACE</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867755</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>37009048</td>\n",
              "      <td>9\" PLASTIC TRAY LINER</td>\n",
              "      <td>1.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867754</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>18159</td>\n",
              "      <td>PAD REPLACE TRIMLINE2PK</td>\n",
              "      <td>5.49</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867753</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>18158</td>\n",
              "      <td>PAINT EDGER</td>\n",
              "      <td>4.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867752</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>68003060</td>\n",
              "      <td>3\" TRIM &amp; TOUCH UP ROLLER KIT</td>\n",
              "      <td>4.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867751</th>\n",
              "      <td>2018-09-09</td>\n",
              "      <td>12:05:19</td>\n",
              "      <td>*5</td>\n",
              "      <td>F84625</td>\n",
              "      <td>4495354</td>\n",
              "      <td>AUGER 1/4\"X25' PISTOL GR</td>\n",
              "      <td>-29.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17327956 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "               Date Transaction Time Customer Number  ... Net Sales  year months\n",
              "5868993  2017-01-01         15:54:38           30247  ...     12.74  2017      1\n",
              "5863918  2017-01-01         14:26:52          233070  ...      1.99  2017      1\n",
              "5863919  2017-01-01         14:26:52              *5  ...     11.24  2017      1\n",
              "5863920  2017-01-01         14:26:52          233070  ...      0.99  2017      1\n",
              "5863921  2017-01-01         14:26:52          233070  ...      0.99  2017      1\n",
              "...             ...              ...             ...  ...       ...   ...    ...\n",
              "16867755 2018-09-09         12:05:21          136015  ...      1.98  2018      9\n",
              "16867754 2018-09-09         12:05:21          136015  ...      5.49  2018      9\n",
              "16867753 2018-09-09         12:05:21          136015  ...      4.99  2018      9\n",
              "16867752 2018-09-09         12:05:21          136015  ...      4.99  2018      9\n",
              "16867751 2018-09-09         12:05:19              *5  ...    -29.99  2018      9\n",
              "\n",
              "[17327956 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j57s8xIXqHdd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM['months']=train_RFM.months.astype(str).str.zfill(2)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vLF5kZARqTbR",
        "colab_type": "code",
        "outputId": "985ad7a8-54fb-4756-d54b-8c52b90618a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train_RFM.head()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "      <td>14:26:52</td>\n",
              "      <td>*5</td>\n",
              "      <td>I29245</td>\n",
              "      <td>PSWITCH832</td>\n",
              "      <td>LED NIGHT LIGHT SWITCH 2PK</td>\n",
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              "      <td>A15017</td>\n",
              "      <td>3202181</td>\n",
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              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863921</th>\n",
              "      <td>2017-01-01</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>33180</td>\n",
              "      <td>TAPE ELECT3/4X60'VYL ACE</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Date Transaction Time Customer Number  ... Net Sales  year months\n",
              "5868993 2017-01-01         15:54:38           30247  ...     12.74  2017     01\n",
              "5863918 2017-01-01         14:26:52          233070  ...      1.99  2017     01\n",
              "5863919 2017-01-01         14:26:52              *5  ...     11.24  2017     01\n",
              "5863920 2017-01-01         14:26:52          233070  ...      0.99  2017     01\n",
              "5863921 2017-01-01         14:26:52          233070  ...      0.99  2017     01\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "X_v6yIDTqdUi",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM['Date']=train_RFM['year'].astype('str') + train_RFM['months']\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wCM2Qx83qyNa",
        "colab_type": "code",
        "outputId": "fc215530-0073-4de5-ced0-c3b5cd29e91c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "train_RFM.tail(50)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th>Item Description</th>\n",
              "      <th>Net Sales</th>\n",
              "      <th>year</th>\n",
              "      <th>months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>16867800</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:06</td>\n",
              "      <td>*5</td>\n",
              "      <td>F84626</td>\n",
              "      <td>7105786</td>\n",
              "      <td>LIQUID ANT BAIT 6PK</td>\n",
              "      <td>6.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867799</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:06</td>\n",
              "      <td>*5</td>\n",
              "      <td>D94255</td>\n",
              "      <td>57063200</td>\n",
              "      <td>VT TITAN 2\" SHRT CT TRIM BSH</td>\n",
              "      <td>6.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867798</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:04</td>\n",
              "      <td>*5</td>\n",
              "      <td>G47821</td>\n",
              "      <td>10704</td>\n",
              "      <td>OIL MULTI-PURP 3IN1 3OZ</td>\n",
              "      <td>4.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867797</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:04</td>\n",
              "      <td>*5</td>\n",
              "      <td>G47821</td>\n",
              "      <td>70723</td>\n",
              "      <td>TWINE TW NYLON #18X525GD</td>\n",
              "      <td>10.49</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867796</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:04</td>\n",
              "      <td>*5</td>\n",
              "      <td>G47821</td>\n",
              "      <td>81355</td>\n",
              "      <td>FUNNEL FLEXIBLE 11\"</td>\n",
              "      <td>4.49</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867795</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:03</td>\n",
              "      <td>*A32VT</td>\n",
              "      <td>D32949</td>\n",
              "      <td>3435</td>\n",
              "      <td>CWB ORANGE SNITZ</td>\n",
              "      <td>1.79</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867794</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:03</td>\n",
              "      <td>*A32VT</td>\n",
              "      <td>D32949</td>\n",
              "      <td>8207433</td>\n",
              "      <td>HICKORY BBQ PELLETS 20LB</td>\n",
              "      <td>19.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867793</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:06:03</td>\n",
              "      <td>*A32VT</td>\n",
              "      <td>D32949</td>\n",
              "      <td>8306540</td>\n",
              "      <td>PECAN BBQ PELLETS 20LB</td>\n",
              "      <td>19.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867792</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:59</td>\n",
              "      <td>117005</td>\n",
              "      <td>J44340</td>\n",
              "      <td>9247289</td>\n",
              "      <td>HIGHLIGHTER FL YLW 2PK</td>\n",
              "      <td>2.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867791</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:59</td>\n",
              "      <td>117005</td>\n",
              "      <td>J44340</td>\n",
              "      <td>UT3PK</td>\n",
              "      <td>3PK UTILITY LIGHTER</td>\n",
              "      <td>5.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867790</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:59</td>\n",
              "      <td>117005</td>\n",
              "      <td>J44340</td>\n",
              "      <td>EA1173</td>\n",
              "      <td>MICRO NYLON WIRE 3'-ASST</td>\n",
              "      <td>9.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867789</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:59</td>\n",
              "      <td>117005</td>\n",
              "      <td>J44340</td>\n",
              "      <td>1491455</td>\n",
              "      <td>POLISH GRANITE GG 24OZ</td>\n",
              "      <td>10.49</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867788</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:58</td>\n",
              "      <td>427894</td>\n",
              "      <td>I55249</td>\n",
              "      <td>4101572</td>\n",
              "      <td>HOSE DISCHARGE 1-1/2\"</td>\n",
              "      <td>14.94</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867787</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:58</td>\n",
              "      <td>427894</td>\n",
              "      <td>I55249</td>\n",
              "      <td>4035465</td>\n",
              "      <td>HOSE SPA 1-1/2ID X 1.90D</td>\n",
              "      <td>41.93</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867768</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:36</td>\n",
              "      <td>501006</td>\n",
              "      <td>F64727</td>\n",
              "      <td>4172722</td>\n",
              "      <td>ALLERGEN FILTER 14X30X1</td>\n",
              "      <td>12.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867767</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:32</td>\n",
              "      <td>*5</td>\n",
              "      <td>A53386</td>\n",
              "      <td>88851</td>\n",
              "      <td>WINDSHIELD WASH ACE GAL</td>\n",
              "      <td>3.49</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867771</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:38</td>\n",
              "      <td>*5</td>\n",
              "      <td>U48365</td>\n",
              "      <td>3502994</td>\n",
              "      <td>PRO BATTERY CHARGER</td>\n",
              "      <td>24.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867765</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:32</td>\n",
              "      <td>*5</td>\n",
              "      <td>B73584</td>\n",
              "      <td>ARB7047026</td>\n",
              "      <td>REVENGE MOUSE GLUE TRAP 4PK</td>\n",
              "      <td>3.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867745</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*A95JT</td>\n",
              "      <td>B73580</td>\n",
              "      <td>COUPON5</td>\n",
              "      <td>IN-STORE COUPON SAVE $5.00</td>\n",
              "      <td>-5</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867744</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*5</td>\n",
              "      <td>G47820</td>\n",
              "      <td>2019966</td>\n",
              "      <td>MARKER CHINA RED</td>\n",
              "      <td>2.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867766</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:32</td>\n",
              "      <td>*5</td>\n",
              "      <td>A53386</td>\n",
              "      <td>21250SC1</td>\n",
              "      <td>KEY SCHLAGE SC1250PK</td>\n",
              "      <td>6.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867742</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*5</td>\n",
              "      <td>G47820</td>\n",
              "      <td>56</td>\n",
              "      <td>FASTENERS</td>\n",
              "      <td>7.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867741</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*A95JT</td>\n",
              "      <td>B73580</td>\n",
              "      <td>90658</td>\n",
              "      <td>CANDY LIFESAVR ASSORTED</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867740</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*5</td>\n",
              "      <td>G47820</td>\n",
              "      <td>2071439</td>\n",
              "      <td>RULE TAPE 1-1/4\"X25'FAT</td>\n",
              "      <td>22.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867746</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*5</td>\n",
              "      <td>G47820</td>\n",
              "      <td>BAG</td>\n",
              "      <td>SUFFOLK COUNTY BAG CHARGE</td>\n",
              "      <td>0.05</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867739</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*A95JT</td>\n",
              "      <td>B73580</td>\n",
              "      <td>6165849</td>\n",
              "      <td>LATCHING TOTE 7.5QT CLR</td>\n",
              "      <td>7.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867737</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:03</td>\n",
              "      <td>*5</td>\n",
              "      <td>U48363</td>\n",
              "      <td>12293</td>\n",
              "      <td>LUBRICANT SLCN SPRY 11OZ</td>\n",
              "      <td>6.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867736</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:04:59</td>\n",
              "      <td>*5</td>\n",
              "      <td>B73645</td>\n",
              "      <td>8611261</td>\n",
              "      <td>CHAFING FUEL GEL 6.24OZ</td>\n",
              "      <td>6</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867735</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:04:55</td>\n",
              "      <td>*21885</td>\n",
              "      <td>H79270</td>\n",
              "      <td>1023381</td>\n",
              "      <td>SILICONE II K&amp;B CLR2.8OZ</td>\n",
              "      <td>12.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867734</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:04:49</td>\n",
              "      <td>*5</td>\n",
              "      <td>H27660</td>\n",
              "      <td>3008737</td>\n",
              "      <td>BATTERY WATCH/CALC 2032</td>\n",
              "      <td>11.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867733</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:04:47</td>\n",
              "      <td>340258</td>\n",
              "      <td>F64726</td>\n",
              "      <td>1211234</td>\n",
              "      <td>BRUSH DSHWASHING HOMEPRO</td>\n",
              "      <td>7.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867732</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:04:47</td>\n",
              "      <td>340258</td>\n",
              "      <td>F64726</td>\n",
              "      <td>1372168</td>\n",
              "      <td>CITRUS AIR FRSR SOLID8OZ</td>\n",
              "      <td>8.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867738</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:05</td>\n",
              "      <td>*5</td>\n",
              "      <td>U48364</td>\n",
              "      <td>3008737</td>\n",
              "      <td>BATTERY WATCH/CALC 2032</td>\n",
              "      <td>17.97</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867747</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:10</td>\n",
              "      <td>*5</td>\n",
              "      <td>C20712</td>\n",
              "      <td>36193</td>\n",
              "      <td>WIRE CONN WTPRF 4PK TAN</td>\n",
              "      <td>4.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867743</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:09</td>\n",
              "      <td>*A95JT</td>\n",
              "      <td>B73580</td>\n",
              "      <td>8478034</td>\n",
              "      <td>STEP STOOL 2STEPS 325LB</td>\n",
              "      <td>16.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867749</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:17</td>\n",
              "      <td>1511</td>\n",
              "      <td>I85200</td>\n",
              "      <td>1531359</td>\n",
              "      <td>WD40 SMART STRAW 8OZ</td>\n",
              "      <td>4.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867764</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:32</td>\n",
              "      <td>*5</td>\n",
              "      <td>A53386</td>\n",
              "      <td>3008737</td>\n",
              "      <td>BATTERY WATCH/CALC 2032</td>\n",
              "      <td>5.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867763</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:32</td>\n",
              "      <td>*5</td>\n",
              "      <td>B73584</td>\n",
              "      <td>51848</td>\n",
              "      <td>CLOTH HDW 24\"X5' 1/4\"MSH</td>\n",
              "      <td>14.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867748</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:10</td>\n",
              "      <td>*5</td>\n",
              "      <td>C20712</td>\n",
              "      <td>3121423</td>\n",
              "      <td>CONN WIRE AQUABL/RED CD5</td>\n",
              "      <td>5.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867761</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:29</td>\n",
              "      <td>131626</td>\n",
              "      <td>H79271</td>\n",
              "      <td>3294584</td>\n",
              "      <td>FUSETRON 30A EASYID CD/2</td>\n",
              "      <td>20.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867760</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:29</td>\n",
              "      <td>131626</td>\n",
              "      <td>H79271</td>\n",
              "      <td>4549952</td>\n",
              "      <td>PVC CMNT HT MD BL LVA8OZ</td>\n",
              "      <td>6.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867759</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:29</td>\n",
              "      <td>9109</td>\n",
              "      <td>E18889</td>\n",
              "      <td>3462686</td>\n",
              "      <td>BULB SOFTWHT 29W ACE 4PK</td>\n",
              "      <td>5.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867758</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:28</td>\n",
              "      <td>300014</td>\n",
              "      <td>F84624</td>\n",
              "      <td>HH50165</td>\n",
              "      <td>$5 REWARDF</td>\n",
              "      <td>-5</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867762</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:32</td>\n",
              "      <td>*5</td>\n",
              "      <td>A53386</td>\n",
              "      <td>12766</td>\n",
              "      <td>GLUE EPXY 5MIN .84OZTUBE</td>\n",
              "      <td>6.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867756</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:28</td>\n",
              "      <td>300014</td>\n",
              "      <td>F84624</td>\n",
              "      <td>56</td>\n",
              "      <td>FASTENERS</td>\n",
              "      <td>0.9</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867755</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>37009048</td>\n",
              "      <td>9\" PLASTIC TRAY LINER</td>\n",
              "      <td>1.98</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867754</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>18159</td>\n",
              "      <td>PAD REPLACE TRIMLINE2PK</td>\n",
              "      <td>5.49</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867753</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>18158</td>\n",
              "      <td>PAINT EDGER</td>\n",
              "      <td>4.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867752</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:21</td>\n",
              "      <td>136015</td>\n",
              "      <td>B43393</td>\n",
              "      <td>68003060</td>\n",
              "      <td>3\" TRIM &amp; TOUCH UP ROLLER KIT</td>\n",
              "      <td>4.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867751</th>\n",
              "      <td>201809</td>\n",
              "      <td>12:05:19</td>\n",
              "      <td>*5</td>\n",
              "      <td>F84625</td>\n",
              "      <td>4495354</td>\n",
              "      <td>AUGER 1/4\"X25' PISTOL GR</td>\n",
              "      <td>-29.99</td>\n",
              "      <td>2018</td>\n",
              "      <td>09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Date Transaction Time Customer Number  ... Net Sales  year months\n",
              "16867800  201809         12:06:06              *5  ...      6.99  2018     09\n",
              "16867799  201809         12:06:06              *5  ...      6.99  2018     09\n",
              "16867798  201809         12:06:04              *5  ...      4.99  2018     09\n",
              "16867797  201809         12:06:04              *5  ...     10.49  2018     09\n",
              "16867796  201809         12:06:04              *5  ...      4.49  2018     09\n",
              "16867795  201809         12:06:03          *A32VT  ...      1.79  2018     09\n",
              "16867794  201809         12:06:03          *A32VT  ...     19.99  2018     09\n",
              "16867793  201809         12:06:03          *A32VT  ...     19.99  2018     09\n",
              "16867792  201809         12:05:59          117005  ...      2.99  2018     09\n",
              "16867791  201809         12:05:59          117005  ...      5.99  2018     09\n",
              "16867790  201809         12:05:59          117005  ...      9.98  2018     09\n",
              "16867789  201809         12:05:59          117005  ...     10.49  2018     09\n",
              "16867788  201809         12:05:58          427894  ...     14.94  2018     09\n",
              "16867787  201809         12:05:58          427894  ...     41.93  2018     09\n",
              "16867768  201809         12:05:36          501006  ...     12.99  2018     09\n",
              "16867767  201809         12:05:32              *5  ...      3.49  2018     09\n",
              "16867771  201809         12:05:38              *5  ...     24.99  2018     09\n",
              "16867765  201809         12:05:32              *5  ...      3.99  2018     09\n",
              "16867745  201809         12:05:09          *A95JT  ...        -5  2018     09\n",
              "16867744  201809         12:05:09              *5  ...      2.98  2018     09\n",
              "16867766  201809         12:05:32              *5  ...      6.98  2018     09\n",
              "16867742  201809         12:05:09              *5  ...      7.99  2018     09\n",
              "16867741  201809         12:05:09          *A95JT  ...      0.99  2018     09\n",
              "16867740  201809         12:05:09              *5  ...     22.99  2018     09\n",
              "16867746  201809         12:05:09              *5  ...      0.05  2018     09\n",
              "16867739  201809         12:05:09          *A95JT  ...      7.98  2018     09\n",
              "16867737  201809         12:05:03              *5  ...      6.99  2018     09\n",
              "16867736  201809         12:04:59              *5  ...         6  2018     09\n",
              "16867735  201809         12:04:55          *21885  ...     12.98  2018     09\n",
              "16867734  201809         12:04:49              *5  ...     11.98  2018     09\n",
              "16867733  201809         12:04:47          340258  ...      7.99  2018     09\n",
              "16867732  201809         12:04:47          340258  ...      8.99  2018     09\n",
              "16867738  201809         12:05:05              *5  ...     17.97  2018     09\n",
              "16867747  201809         12:05:10              *5  ...      4.99  2018     09\n",
              "16867743  201809         12:05:09          *A95JT  ...     16.99  2018     09\n",
              "16867749  201809         12:05:17            1511  ...      4.99  2018     09\n",
              "16867764  201809         12:05:32              *5  ...      5.99  2018     09\n",
              "16867763  201809         12:05:32              *5  ...     14.99  2018     09\n",
              "16867748  201809         12:05:10              *5  ...      5.99  2018     09\n",
              "16867761  201809         12:05:29          131626  ...     20.99  2018     09\n",
              "16867760  201809         12:05:29          131626  ...      6.99  2018     09\n",
              "16867759  201809         12:05:29            9109  ...      5.99  2018     09\n",
              "16867758  201809         12:05:28          300014  ...        -5  2018     09\n",
              "16867762  201809         12:05:32              *5  ...      6.99  2018     09\n",
              "16867756  201809         12:05:28          300014  ...       0.9  2018     09\n",
              "16867755  201809         12:05:21          136015  ...      1.98  2018     09\n",
              "16867754  201809         12:05:21          136015  ...      5.49  2018     09\n",
              "16867753  201809         12:05:21          136015  ...      4.99  2018     09\n",
              "16867752  201809         12:05:21          136015  ...      4.99  2018     09\n",
              "16867751  201809         12:05:19              *5  ...    -29.99  2018     09\n",
              "\n",
              "[50 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIFndYRWdveC",
        "colab_type": "code",
        "outputId": "61078de2-eb27-4ac8-bbbd-95a5e68cb050",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train_RFM.isna().sum()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                0\n",
              "Transaction Time    0\n",
              "Customer Number     0\n",
              "Receipt Number      0\n",
              "Item Number         0\n",
              "Item Description    0\n",
              "Net Sales           0\n",
              "year                0\n",
              "months              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nDShboazih1I",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM['Net Sales'] = train_RFM['Net Sales'].astype('str')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LijOlmqmddf2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM['Net Sales'] = train_RFM['Net Sales'].replace(',', '')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "re9bW7FZjfuH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_RFM = train_RFM[pd.to_numeric(train_RFM['Net Sales'], errors='coerce').notnull()]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EwC3dn4Fj8ba",
        "colab_type": "code",
        "outputId": "78298e79-fb1d-4424-a9e6-2761cc4f30c4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 118
        }
      },
      "source": [
        "train_RFM['Net Sales']=train_RFM['Net Sales'].astype('float')"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zo-0wpj_j0dw",
        "colab_type": "code",
        "outputId": "ed818422-7ce5-4910-8d60-083c9f30f232",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train_RFM.isna().sum()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                0\n",
              "Transaction Time    0\n",
              "Customer Number     0\n",
              "Receipt Number      0\n",
              "Item Number         0\n",
              "Item Description    0\n",
              "Net Sales           0\n",
              "year                0\n",
              "months              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rEr04ZsBtG-t",
        "colab_type": "text"
      },
      "source": [
        "Total Number of Customers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5lejoDxMq6Ad",
        "colab_type": "code",
        "outputId": "c6500985-0e32-467a-9faa-746f8736f940",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Cust=train_RFM[['Customer Number']].drop_duplicates()\n",
        "print (\"No of customers are :\" , len(Cust))\n",
        "#Cust_country=train_RFM[['Country','CustomerID']].drop_duplicates()\n",
        "Cust_freq=train_RFM[['Receipt Number','Customer Number']].drop_duplicates()"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "No of customers are : 349280\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z_TrPe0MtV1m",
        "colab_type": "text"
      },
      "source": [
        "**Frequency Flag**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7MWVthI4eNJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_freq=train_RFM[['Receipt Number','Customer Number']].drop_duplicates()\n",
        "#Cust_freq_count = Cust_freq.groupby([\"Customer Number\"])[\"Receipt Number\"].aggregate(\"count\").\\\n",
        "#reset_index().sort_values(by='Receipt Number', ascending=False)\n",
        "#Cust_freq_count_UK=Cust_freq_count[Cust_freq_count['Country']==\"United Kingdom\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qsWzln8i7RxR",
        "colab_type": "code",
        "outputId": "6b249844-65f5-4972-fb12-a760e00d843b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "Cust_freq"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Receipt Number</th>\n",
              "      <th>Customer Number</th>\n",
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              "  <tbody>\n",
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              "      <td>I85200</td>\n",
              "      <td>1511</td>\n",
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              "      <th>16867761</th>\n",
              "      <td>H79271</td>\n",
              "      <td>131626</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867759</th>\n",
              "      <td>E18889</td>\n",
              "      <td>9109</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867758</th>\n",
              "      <td>F84624</td>\n",
              "      <td>300014</td>\n",
              "    </tr>\n",
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              "      <th>16867755</th>\n",
              "      <td>B43393</td>\n",
              "      <td>136015</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5693251 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Receipt Number Customer Number\n",
              "5868993          B15056           30247\n",
              "5863918          A15017          233070\n",
              "5863919          I29245              *5\n",
              "5863927          E79230          *27144\n",
              "5863929          I88880          106492\n",
              "...                 ...             ...\n",
              "16867749         I85200            1511\n",
              "16867761         H79271          131626\n",
              "16867759         E18889            9109\n",
              "16867758         F84624          300014\n",
              "16867755         B43393          136015\n",
              "\n",
              "[5693251 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jnHGDn8N7a-Q",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "#Counting Frquency for each Customer\n",
        "Cust_freq_count = Cust_freq.groupby([\"Customer Number\"])[\"Receipt Number\"].aggregate(\"count\").\\ \n",
        "reset_index()   \n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uZjpFm8L5QzX",
        "colab_type": "code",
        "outputId": "52dca015-6af7-4227-e54c-dad38c3ecfbc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "Cust_freq_count"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "      <th></th>\n",
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              "      <th>Receipt Number</th>\n",
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              "      <th>2</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>*100</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>*1000</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349275</th>\n",
              "      <td>999966</td>\n",
              "      <td>14</td>\n",
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              "      <th>349276</th>\n",
              "      <td>99997</td>\n",
              "      <td>26</td>\n",
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              "    <tr>\n",
              "      <th>349277</th>\n",
              "      <td>999977</td>\n",
              "      <td>4</td>\n",
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              "      <td>99998</td>\n",
              "      <td>154</td>\n",
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              "      <td>99999</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>349280 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Customer Number  Receipt Number\n",
              "0                   *0              56\n",
              "1                   *1              33\n",
              "2                  *10               6\n",
              "3                 *100               5\n",
              "4                *1000               9\n",
              "...                ...             ...\n",
              "349275          999966              14\n",
              "349276           99997              26\n",
              "349277          999977               4\n",
              "349278           99998             154\n",
              "349279           99999              27\n",
              "\n",
              "[349280 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ICWmIIax74Ac",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_freq_count = Cust_freq_count.sort_values(by='Receipt Number', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oce70gBE8CEl",
        "colab_type": "code",
        "outputId": "90550716-f473-4ba8-8ee6-8e2d00fed6b6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "Cust_freq_count.head(10)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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            "text/plain": [
              "       Customer Number  Receipt Number\n",
              "24768               *5          982026\n",
              "29919               *6           28775\n",
              "87121            10000           14887\n",
              "75474           *A96T6            8961\n",
              "69786           *A739A            8523\n",
              "87128           100020            7477\n",
              "349274           99996            6478\n",
              "7660               *22            5196\n",
              "7037               *21            4791\n",
              "65469           *A621Q            3487"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fvonpejdruqE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "unique_invoice=Cust_freq_count[['Receipt Number']].drop_duplicates()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dIKbIg-mtUQW",
        "colab_type": "code",
        "outputId": "d1acf147-15dd-448b-cba8-f40e9358c2bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "unique_invoice"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<p>424 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Receipt Number\n",
              "24768           982026\n",
              "29919            28775\n",
              "87121            14887\n",
              "75474             8961\n",
              "69786             8523\n",
              "...                ...\n",
              "218297               5\n",
              "167472               4\n",
              "304323               3\n",
              "302684               2\n",
              "159879               1\n",
              "\n",
              "[424 rows x 1 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONI1xo48smKm",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "unique_invoice['Frequency_Band'] = pd.qcut(unique_invoice['Receipt Number'], 5)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "12csHWrFuSN-",
        "colab_type": "code",
        "outputId": "d37c19ce-6eec-49da-d460-ddd4ee303924",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "#Frequency Bands\n",
        "unique_invoice=unique_invoice[['Frequency_Band']].drop_duplicates()\n",
        "unique_invoice"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Frequency_Band\n",
              "24768   (388.4, 982026.0]\n",
              "94237      (255.8, 388.4]\n",
              "337913     (170.2, 255.8]\n",
              "241172      (85.6, 170.2]\n",
              "50821       (0.999, 85.6]"
            ]
          },
          "metadata": {
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          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RlIWygBsuq3_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def f(row):\n",
        "    if row['Receipt Number'] <= 85.6:\n",
        "        val = 1\n",
        "    elif row['Receipt Number'] > 85.6 and row['Receipt Number'] <= 170.2:\n",
        "        val = 2\n",
        "    elif row['Receipt Number'] > 170.2 and row['Receipt Number'] <= 255.8:\n",
        "        val = 3\n",
        "    elif row['Receipt Number'] > 255.8 and row['Receipt Number'] <= 388.4:\n",
        "        val = 4\n",
        "    else:\n",
        "        val = 5\n",
        "    return val"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_7rEa5hDvG1g",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_freq_count['Freq_Flag'] = Cust_freq_count.apply(f, axis=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SCx6N4-vu0l",
        "colab_type": "code",
        "outputId": "03d986e7-a526-410e-a1a6-7841d787c610",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "Cust_freq_count"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
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              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>307520</th>\n",
              "      <td>502898</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64395</th>\n",
              "      <td>*A56MW</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64394</th>\n",
              "      <td>*A56MS</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64393</th>\n",
              "      <td>*A56MR</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>300216</th>\n",
              "      <td>452641</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>349280 rows × 3 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Customer Number  Receipt Number  Freq_Flag\n",
              "24768               *5          982026          5\n",
              "29919               *6           28775          5\n",
              "87121            10000           14887          5\n",
              "75474           *A96T6            8961          5\n",
              "69786           *A739A            8523          5\n",
              "...                ...             ...        ...\n",
              "307520          502898               1          1\n",
              "64395           *A56MW               1          1\n",
              "64394           *A56MS               1          1\n",
              "64393           *A56MR               1          1\n",
              "300216          452641               1          1\n",
              "\n",
              "[349280 rows x 3 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82DeFYB_wcUH",
        "colab_type": "code",
        "outputId": "a12ced67-c8fa-49be-b2a2-9e4cd02d7574",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "import seaborn as sns\n",
        "color = sns.color_palette()\n",
        "plt.figure(figsize=(8,6))\n",
        "\n",
        "sns.countplot(x=\"Freq_Flag\", data=Cust_freq_count, color=color[1])\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "plt.xlabel('Freq_Flag', fontsize=12)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.title(\"Frequency of Freq_Flag\", fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kSVKXQUGSJHUZFCRJUtfEg0KSbyW5OskVSda12pOTrE1yQ3vcvdWT5Iwk65Nc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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ab5hUB7luEXt",
        "colab_type": "text"
      },
      "source": [
        "**Recency Flag**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VS-4B1fmygVd",
        "colab_type": "code",
        "outputId": "54ad81ef-8dc8-4419-b017-f423cc504a3a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train_RFM.head()"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Transaction Time</th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Receipt Number</th>\n",
              "      <th>Item Number</th>\n",
              "      <th>Item Description</th>\n",
              "      <th>Net Sales</th>\n",
              "      <th>year</th>\n",
              "      <th>months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5868993</th>\n",
              "      <td>201701</td>\n",
              "      <td>15:54:38</td>\n",
              "      <td>30247</td>\n",
              "      <td>B15056</td>\n",
              "      <td>6237762</td>\n",
              "      <td>GLAD FORCEFLEX BLK 50CT</td>\n",
              "      <td>12.74</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863918</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>30733</td>\n",
              "      <td>BOX SGL GNG PVC OW 14CU\"</td>\n",
              "      <td>1.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863919</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>*5</td>\n",
              "      <td>I29245</td>\n",
              "      <td>PSWITCH832</td>\n",
              "      <td>LED NIGHT LIGHT SWITCH 2PK</td>\n",
              "      <td>11.24</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863920</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>3202181</td>\n",
              "      <td>BLANK WALLPLATE 1G WHITE</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863921</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>33180</td>\n",
              "      <td>TAPE ELECT3/4X60'VYL ACE</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date Transaction Time Customer Number  ... Net Sales  year months\n",
              "5868993  201701         15:54:38           30247  ...     12.74  2017     01\n",
              "5863918  201701         14:26:52          233070  ...      1.99  2017     01\n",
              "5863919  201701         14:26:52              *5  ...     11.24  2017     01\n",
              "5863920  201701         14:26:52          233070  ...      0.99  2017     01\n",
              "5863921  201701         14:26:52          233070  ...      0.99  2017     01\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zQHVg6nFxpam",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_date=train_RFM[['Customer Number','Date']].drop_duplicates()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmKF8TV4yr1B",
        "colab_type": "code",
        "outputId": "b94fd4e0-ddc5-42b8-e652-2d19ebaa1736",
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      },
      "source": [
        "Cust_date"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Date</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5868993</th>\n",
              "      <td>30247</td>\n",
              "      <td>201701</td>\n",
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              "    <tr>\n",
              "      <th>5863918</th>\n",
              "      <td>233070</td>\n",
              "      <td>201701</td>\n",
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              "      <td>*5</td>\n",
              "      <td>201701</td>\n",
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              "    <tr>\n",
              "      <th>5863927</th>\n",
              "      <td>*27144</td>\n",
              "      <td>201701</td>\n",
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              "    <tr>\n",
              "      <th>5863929</th>\n",
              "      <td>106492</td>\n",
              "      <td>201701</td>\n",
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              "      <th>...</th>\n",
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              "      <td>...</td>\n",
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              "      <th>16867786</th>\n",
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              "      <td>201809</td>\n",
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              "    <tr>\n",
              "      <th>16867792</th>\n",
              "      <td>117005</td>\n",
              "      <td>201809</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16867735</th>\n",
              "      <td>*21885</td>\n",
              "      <td>201809</td>\n",
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              "    <tr>\n",
              "      <th>16867758</th>\n",
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              "      <td>136015</td>\n",
              "      <td>201809</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2396215 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         Customer Number    Date\n",
              "5868993            30247  201701\n",
              "5863918           233070  201701\n",
              "5863919               *5  201701\n",
              "5863927           *27144  201701\n",
              "5863929           106492  201701\n",
              "...                  ...     ...\n",
              "16867786          232440  201809\n",
              "16867792          117005  201809\n",
              "16867735          *21885  201809\n",
              "16867758          300014  201809\n",
              "16867755          136015  201809\n",
              "\n",
              "[2396215 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3TS8KRjY5uUA",
        "colab_type": "code",
        "outputId": "7f522a49-a8a8-4c8d-c9b3-752517e531e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 758
        }
      },
      "source": [
        "Cust_date.loc[(Cust_date['Customer Number'] == '*5')]"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
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              "  <tbody>\n",
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              "    <tr>\n",
              "      <th>6418118</th>\n",
              "      <td>*5</td>\n",
              "      <td>201801</td>\n",
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              "    <tr>\n",
              "      <th>14714253</th>\n",
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              "      <td>201710</td>\n",
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              "    <tr>\n",
              "      <th>15350388</th>\n",
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              "      <td>201810</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13203006</th>\n",
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              "      <td>201711</td>\n",
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              "      <td>201712</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4937520</th>\n",
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              "      <td>201702</td>\n",
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              "      <th>5422049</th>\n",
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              "    <tr>\n",
              "      <th>2434267</th>\n",
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              "      <td>201809</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Customer Number    Date\n",
              "5863919               *5  201701\n",
              "6418118               *5  201801\n",
              "14714253              *5  201710\n",
              "15350388              *5  201810\n",
              "13203006              *5  201711\n",
              "13949713              *5  201811\n",
              "3184195               *5  201712\n",
              "4088353               *5  201812\n",
              "4937520               *5  201702\n",
              "5422049               *5  201802\n",
              "10298641              *5  201703\n",
              "10830385              *5  201803\n",
              "0                     *5  201704\n",
              "889954                *5  201804\n",
              "11404229              *5  201705\n",
              "12274095              *5  201805\n",
              "8616608               *5  201706\n",
              "9433074               *5  201806\n",
              "6983738               *5  201707\n",
              "7769321               *5  201807\n",
              "1683595               *5  201708\n",
              "2434267               *5  201808\n",
              "16021337              *5  201709\n",
              "16686611              *5  201809"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v5XV39Xf1FGk",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_date['Date'] = Cust_date['Date'].astype('int') "
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHTPCP6Ryusx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def f2(row):\n",
        "    if row['Date'] > 201804:\n",
        "        val = 5\n",
        "    elif row['Date'] <= 201804 and row['Date'] > 201712:\n",
        "        val = 4\n",
        "    elif row['Date'] <= 201712 and row['Date'] > 201708:\n",
        "        val = 3\n",
        "    elif row['Date'] <= 201708 and row['Date'] > 201704:\n",
        "        val = 2\n",
        "    else:\n",
        "        val = 1\n",
        "    return val"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3-B1BM0u0YUp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_date['Recency_Flag'] = Cust_date.apply(f2, axis=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2QH68GnT0srF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_date = Cust_date.groupby(\"Customer Number\", as_index=False)[\"Recency_Flag\"].max()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KDlSTKlz3lNv",
        "colab_type": "code",
        "outputId": "0554572f-7c4a-4852-8fef-e623abed4797",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        }
      },
      "source": [
        "Cust_date"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Recency_Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>*0</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>*1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>*10</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>*100</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>*1000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349275</th>\n",
              "      <td>999966</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349276</th>\n",
              "      <td>99997</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349277</th>\n",
              "      <td>999977</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349278</th>\n",
              "      <td>99998</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349279</th>\n",
              "      <td>99999</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>349280 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       Customer Number  Recency_Flag\n",
              "0                   *0             5\n",
              "1                   *1             5\n",
              "2                  *10             5\n",
              "3                 *100             5\n",
              "4                *1000             5\n",
              "...                ...           ...\n",
              "349275          999966             3\n",
              "349276           99997             4\n",
              "349277          999977             2\n",
              "349278           99998             5\n",
              "349279           99999             4\n",
              "\n",
              "[349280 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aJZCdfcZ2Wum",
        "colab_type": "code",
        "outputId": "29b57ecd-b8df-42e1-d34f-b00fc54c220d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.countplot(x=\"Recency_Flag\", data=Cust_date, color=color[1])\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "plt.xlabel('Recency_Flag', fontsize=12)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.title(\"Frequency of Recency_Flag\", fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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A1sBJVXXRhJu1OSzZ0yZLiMd48/MYb34e44WxbI7zsh/MKEmS7rot4dSDJEm6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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MnLZrobWuRrk",
        "colab_type": "text"
      },
      "source": [
        "**Monetary Flag**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HxVt_CcqYt-X",
        "colab_type": "code",
        "outputId": "d45d7663-b9f2-4432-b332-0bcca88cfa9f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "gc.collect()"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4977"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G20eJ6QDAmAP",
        "colab_type": "code",
        "outputId": "c7661f8b-0e05-4982-9e3c-41cfc8d7ac11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train_RFM.dtypes"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                 object\n",
              "Transaction Time     object\n",
              "Customer Number      object\n",
              "Receipt Number       object\n",
              "Item Number          object\n",
              "Item Description     object\n",
              "Net Sales           float64\n",
              "year                  int64\n",
              "months               object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tPLLqmYDXK7N",
        "colab_type": "code",
        "outputId": "dbf295ad-2930-431d-a20e-8470a3bccb9a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 185
        }
      },
      "source": [
        "train_RFM.isna().sum()"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                0\n",
              "Transaction Time    0\n",
              "Customer Number     0\n",
              "Receipt Number      0\n",
              "Item Number         0\n",
              "Item Description    0\n",
              "Net Sales           0\n",
              "year                0\n",
              "months              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OmhdDqot3IOC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_monetory = train_RFM.groupby([\"Customer Number\"])[\"Net Sales\"].aggregate(\"sum\").\\\n",
        "reset_index()#.sort_values(by='Net Sales', ascending=False)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-hn9RXXm3e5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_monetory = Cust_monetory.sort_values(by='Net Sales', ascending=False)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFX48L25nFuH",
        "colab_type": "code",
        "outputId": "f92fa62b-0976-4e3c-89bc-b3f6201582d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "unique_price=Cust_monetory[['Net Sales']].drop_duplicates()\n",
        "unique_price=unique_price[unique_price['Net Sales'] > 0]\n",
        "unique_price['monetary_Band'] = pd.qcut(unique_price['Net Sales'], 5)\n",
        "unique_price=unique_price[['monetary_Band']].drop_duplicates()\n",
        "unique_price"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>monetary_Band</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24768</th>\n",
              "      <td>(807.57, 48857385.73]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>132162</th>\n",
              "      <td>(454.55, 807.57]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190562</th>\n",
              "      <td>(262.42, 454.55]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>130931</th>\n",
              "      <td>(128.24, 262.42]</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>251084</th>\n",
              "      <td>(-0.000999999999999787, 128.24]</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                          monetary_Band\n",
              "24768             (807.57, 48857385.73]\n",
              "132162                 (454.55, 807.57]\n",
              "190562                 (262.42, 454.55]\n",
              "130931                 (128.24, 262.42]\n",
              "251084  (-0.000999999999999787, 128.24]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oOMYw-7nnUMJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def f3(row):\n",
        "    if row['Net Sales'] <= 128.24:\n",
        "        val = 1\n",
        "    elif row['Net Sales'] > 128.24 and row['Net Sales'] <= 262.42:\n",
        "        val = 2\n",
        "    elif row['Net Sales'] > 262.42 and row['Net Sales'] <= 454.55:\n",
        "        val = 3\n",
        "    elif row['Net Sales'] > 454.55 and row['Net Sales'] <= 807.57:\n",
        "        val = 4\n",
        "    else:\n",
        "        val = 5\n",
        "    return val"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kYFWrGt3nndB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Cust_monetory['Monetary_Flag'] = Cust_monetory.apply(f3, axis=1)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yvvnYAR2oEpZ",
        "colab_type": "code",
        "outputId": "2e8f23d4-6dab-43a9-f264-157dce9df748",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 405
        }
      },
      "source": [
        "plt.figure(figsize=(8,6))\n",
        "sns.countplot(x=\"Monetary_Flag\", data=Cust_monetory, color=color[1])\n",
        "plt.ylabel('Count', fontsize=12)\n",
        "plt.xlabel('Monetary_Flag', fontsize=12)\n",
        "plt.xticks(rotation='vertical')\n",
        "plt.title(\"Frequency of Monetary_Flag\", fontsize=15)\n",
        "plt.show()"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oSJKkXgYFSZLUy6AgSZJ6GRQkSVIvg4IkSeplUJAkSb0MCpIkqZdBQZIk9TIo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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HuTbRoQQuclJ",
        "colab_type": "text"
      },
      "source": [
        "**Merging Recency,Frequency and Monetary Flag**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9TvxgKpkoaHv",
        "colab_type": "code",
        "outputId": "72c7f632-89d6-462a-8061-8f405c947123",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 343
        }
      },
      "source": [
        "Cust_All=pd.merge(Cust_date,Cust_freq_count[['Customer Number','Freq_Flag']],\\\n",
        "                     on=['Customer Number'],how='left')\n",
        "Cust_All=pd.merge(Cust_All,Cust_monetory[['Customer Number','Monetary_Flag']],\\\n",
        "                     on=['Customer Number'],how='left')\n",
        "Cust_All.tail(10)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Recency_Flag</th>\n",
              "      <th>Freq_Flag</th>\n",
              "      <th>Monetary_Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>349270</th>\n",
              "      <td>99994</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349271</th>\n",
              "      <td>999948</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349272</th>\n",
              "      <td>999949</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349273</th>\n",
              "      <td>99995</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349274</th>\n",
              "      <td>99996</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349275</th>\n",
              "      <td>999966</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349276</th>\n",
              "      <td>99997</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349277</th>\n",
              "      <td>999977</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349278</th>\n",
              "      <td>99998</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>349279</th>\n",
              "      <td>99999</td>\n",
              "      <td>4</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Customer Number  Recency_Flag  Freq_Flag  Monetary_Flag\n",
              "349270           99994             2          1              4\n",
              "349271          999948             2          1              1\n",
              "349272          999949             1          1              1\n",
              "349273           99995             5          1              4\n",
              "349274           99996             5          5              5\n",
              "349275          999966             3          1              1\n",
              "349276           99997             4          1              5\n",
              "349277          999977             2          1              1\n",
              "349278           99998             5          2              5\n",
              "349279           99999             4          1              5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lNEjddifydMU",
        "colab_type": "code",
        "outputId": "316cad32-150c-4f80-e43f-1e9ba25383aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 77
        }
      },
      "source": [
        "Cust_All.loc[(Cust_All['Customer Number'] == '*5')]"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Recency_Flag</th>\n",
              "      <th>Freq_Flag</th>\n",
              "      <th>Monetary_Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>24768</th>\n",
              "      <td>*5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Customer Number  Recency_Flag  Freq_Flag  Monetary_Flag\n",
              "24768              *5             5          5              5"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Yd7q_5muuSw",
        "colab_type": "text"
      },
      "source": [
        "**Segmenting Customers on the basis of RFM Scores**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gfdS55f6pIqd",
        "colab_type": "code",
        "outputId": "ec90eb56-6342-4239-c6f8-4669a4249c28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Champions\n",
        "Champions = Cust_All.loc[(Cust_All['Recency_Flag'] >=4) & (Cust_All['Freq_Flag'] >= 4) & (Cust_All['Monetary_Flag'] >= 4)]\n",
        "Champions.shape"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(248, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qA52EUf9sQAP",
        "colab_type": "code",
        "outputId": "6f814d34-6118-4224-ae0f-9631f5f32c86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Loyal_Customers\n",
        "Loyal_Customers = Cust_All.loc[(Cust_All['Freq_Flag'] >= 4)]\n",
        "Loyal_Customers.shape"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(255, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qDxrpGB_8Vy9",
        "colab_type": "code",
        "outputId": "33eab04a-5cbf-419e-99ff-8c0c24116c68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Potential_Loyalist\n",
        "Potential_Loyalist = Cust_All.loc[(Cust_All['Recency_Flag'] ==5) & (Cust_All['Freq_Flag'] != 1 ) & (Cust_All['Monetary_Flag'] == 4)]\n",
        "Potential_Loyalist.shape"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(54, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rzBSgWAc9rBu",
        "colab_type": "code",
        "outputId": "3e37df78-6e32-4a8f-e5e1-1c6f99b381fe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Recent Customers\n",
        "Recent_Customers = Cust_All.loc[(Cust_All['Recency_Flag'] ==5) & (Cust_All['Freq_Flag'] < 5 ) ]\n",
        "Recent_Customers.shape"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(270887, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gYRDxpYX_Qi0",
        "colab_type": "code",
        "outputId": "5a956088-c72c-4a56-d23a-ef3a062fb3ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Promising\n",
        "Promising = Cust_All.loc[(Cust_All['Recency_Flag'] ==5) & (Cust_All['Freq_Flag'] >=2 ) & (Cust_All['Freq_Flag'] <=4 ) & (Cust_All['Monetary_Flag'] <= 3 )   ]\n",
        "Promising.shape"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(16, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6CORvmCZ_rOw",
        "colab_type": "code",
        "outputId": "07a1241a-fda5-4ba9-ad4a-72fb5459b22c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Customers_Needing_Attention\n",
        "Customers_Needing_Attention = Cust_All.loc[(Cust_All['Recency_Flag'] >= 3) &  (Cust_All['Recency_Flag'] < 5) & (Cust_All['Freq_Flag'] >= 2 ) & (Cust_All['Monetary_Flag'] >= 3)]\n",
        "Customers_Needing_Attention.shape"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(26, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a31n9T9qATYe",
        "colab_type": "code",
        "outputId": "ca053294-324d-492d-fcad-847248672d65",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#About_To_Sleep\n",
        "About_To_Sleep = Cust_All.loc[(Cust_All['Recency_Flag'] <=3) & (Cust_All['Freq_Flag'] <= 3 ) & (Cust_All['Monetary_Flag'] <= 3)]\n",
        "About_To_Sleep.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(53609, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O4-MBhGmCa7D",
        "colab_type": "code",
        "outputId": "ae617ad5-2398-4724-c465-ad398a26ba2a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#At_Risk\n",
        "At_Risk = Cust_All.loc[(Cust_All['Recency_Flag'] <=3) & (Cust_All['Freq_Flag'] >= 2 ) & (Cust_All['Monetary_Flag'] >= 2)]\n",
        "At_Risk.shape"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(17, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4hFEy_gGDFDC",
        "colab_type": "code",
        "outputId": "c5eb7b54-f3b1-4547-cc78-8894e92aa1e5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Cant_Lose_Them\n",
        "Cant_Lose_Them = Cust_All.loc[(Cust_All['Recency_Flag'] <=4) & (Cust_All['Freq_Flag'] >= 3 ) & (Cust_All['Monetary_Flag'] >= 3)]\n",
        "Cant_Lose_Them.shape"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C3e3vPzxDoyY",
        "colab_type": "code",
        "outputId": "5af24535-f2d1-41dc-9198-0386b2c0a682",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Hibernating\n",
        "Hibernating = Cust_All.loc[(Cust_All['Recency_Flag'] <=2) & (Cust_All['Freq_Flag'] <= 2 ) & (Cust_All['Monetary_Flag'] <= 2)]\n",
        "Hibernating.shape"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(31090, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "h6pS66XHD9uk",
        "colab_type": "code",
        "outputId": "192f5040-0168-454e-d33e-72c85e0af618",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#Lost\n",
        "Lost = Cust_All.loc[(Cust_All['Recency_Flag'] ==1)& (Cust_All['Freq_Flag'] == 1 ) & (Cust_All['Monetary_Flag'] == 1)]\n",
        "Lost.shape"
      ],
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(11253, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UyW__rGfuq0l",
        "colab_type": "code",
        "outputId": "018a78e1-f50b-4edd-9af8-146c7a9ac9b5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "#spend_thrift_cust\n",
        "spend_thrift_cust = Cust_All.loc[(Cust_All['Freq_Flag'] == 5)& (Cust_All['Monetary_Flag'] == 5)]\n",
        "spend_thrift_cust.shape"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3aAfrbUVFLdK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "customer_segments = ['Champions','Loyal_Customers','Promising','Need_Attention','At_Risk','Cant_Lose_Them']\n",
        "freq_segments = [Champions.shape[0],Loyal_Customers.shape[0],Promising.shape[0],Customers_Needing_Attention.shape[0],At_Risk.shape[0],Cant_Lose_Them.shape[0]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "94RByUsXJ-WN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data = {'customer_segments' : ['Champions','Loyal_Customers','Promising','Customers_Needing_Attention','At_Risk','Cant_Lose_Them'],\n",
        "'freq_segments' : [Champions.shape[0],Loyal_Customers.shape[0],Promising.shape[0],Customers_Needing_Attention.shape[0],At_Risk.shape[0],Cant_Lose_Them.shape[0]]}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z0wX_oz3HwpC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "RFM_seg = pd.DataFrame(data)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MQshKgF8I2qj",
        "colab_type": "code",
        "outputId": "d42a15bd-7fc3-4ed0-d5ea-899ff6f509f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        }
      },
      "source": [
        "RFM_seg"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>customer_segments</th>\n",
              "      <th>freq_segments</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Champions</td>\n",
              "      <td>248</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Loyal_Customers</td>\n",
              "      <td>255</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Promising</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Customers_Needing_Attention</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>At_Risk</td>\n",
              "      <td>17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Cant_Lose_Them</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             customer_segments  freq_segments\n",
              "0                    Champions            248\n",
              "1              Loyal_Customers            255\n",
              "2                    Promising             16\n",
              "3  Customers_Needing_Attention             26\n",
              "4                      At_Risk             17\n",
              "5               Cant_Lose_Them             10"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fa7_CBdHElJE",
        "colab_type": "code",
        "outputId": "50a62bc8-a6b7-4b5b-e8a2-e54866868090",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 738
        }
      },
      "source": [
        "fig2 =plt.figure(figsize=(12,11))\n",
        "sns.barplot(customer_segments,freq_segments)\n",
        "plt.ylabel( \"Frequency of Segments\", fontsize=13)\n",
        "plt.xlabel('Customer Segments', fontsize=13)\n",
        "plt.xticks(fontsize=13,rotation=30)\n",
        "plt.title(\"Customer Segmentation using RFM\", fontsize=15)\n",
        "plt.show()\n",
        "fig2.savefig('RFM_barplot.png')"
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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AytnRsJBnJTm+qt6e5DeS3HVh3k2SfGxzygIAgNWz3XDd3X9bVV9P8otJfr+7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\n",
            "text/plain": [
              "<Figure size 864x792 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3KzUup6MvOyQ",
        "colab_type": "text"
      },
      "source": [
        "Figure: Customer Segmentation using RFM analysis: Differentitation of Customers on the basis of set of RFM values"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BHzNGqQ7XNe2",
        "colab_type": "code",
        "outputId": "d30e1d18-d3df-473a-b78c-f1b949a50222",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "Cust_All.shape"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(349280, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Z-gA5pVnWJyC",
        "colab_type": "code",
        "outputId": "aa717ab0-da39-47c8-8d8c-bf0587cd95c1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        }
      },
      "source": [
        "from matplotlib.ticker import MaxNLocator\n",
        "\n",
        "#ax = plt.figure().gca()\n",
        "#ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "\n",
        "fig = plt.figure(figsize=(10, 8))\n",
        "ax = fig.add_subplot(111, projection='3d')\n",
        "\n",
        "xs = Cust_All['Recency_Flag']\n",
        "ys = Cust_All['Freq_Flag']\n",
        "zs = Cust_All['Monetary_Flag']\n",
        "volume = xs*ys*zs\n",
        "sc = ax.scatter(xs, ys, zs, s=volume*10,  edgecolors='white')\n",
        "#sc = ax.scatter(xs, ys, zs, s=150,  edgecolors='white')\n",
        "\n",
        "ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "ax.xaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "ax.zaxis.set_major_locator(MaxNLocator(integer=True))\n",
        "\n",
        "ax.set_xlabel('Recency',fontsize=12)\n",
        "ax.set_ylabel('Frequency',fontsize=12)\n",
        "ax.set_zlabel('Monetary',fontsize=12)\n",
        "plt.title(\"Customer Significance and Valuability\", fontsize=14)\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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T6Xz9w1JFBEkdeA/Q1hmUhQcVPJR5j9ccH8++Vx6PByaTCUNDQ6iqqvL1k5Ka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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1hHSl-9HvIbk",
        "colab_type": "text"
      },
      "source": [
        "Figure: Customer Significance and Valuability using RFM analysis: 1. Color Density: Color density is directly proportional to\n",
        "the Number of Customers with same RFM set values i.e. Darker the colour, more the number of Customers with that\n",
        "set of RFM value. 2. Size: Customer significance to the store is directly proportional to the size of the bubble i.e. Most\n",
        "Valuable Customers will have largest bubble with RFM values set of 5,5,5."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKd1NOhBtzqc",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "fig.savefig('3d_scatter.png')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5q3tgtxYbOs7",
        "colab_type": "text"
      },
      "source": [
        "## **Validation**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LY1EWQaSbNCL",
        "colab_type": "code",
        "outputId": "7c6300a8-8a20-459c-c45b-23523610b646",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "train_RFM.head()"
      ],
      "execution_count": 85,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Transaction Time</th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Receipt Number</th>\n",
              "      <th>Item Number</th>\n",
              "      <th>Item Description</th>\n",
              "      <th>Net Sales</th>\n",
              "      <th>year</th>\n",
              "      <th>months</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>5868993</th>\n",
              "      <td>201701</td>\n",
              "      <td>15:54:38</td>\n",
              "      <td>30247</td>\n",
              "      <td>B15056</td>\n",
              "      <td>6237762</td>\n",
              "      <td>GLAD FORCEFLEX BLK 50CT</td>\n",
              "      <td>12.74</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863918</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>30733</td>\n",
              "      <td>BOX SGL GNG PVC OW 14CU\"</td>\n",
              "      <td>1.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863919</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>*5</td>\n",
              "      <td>I29245</td>\n",
              "      <td>PSWITCH832</td>\n",
              "      <td>LED NIGHT LIGHT SWITCH 2PK</td>\n",
              "      <td>11.24</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863920</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>3202181</td>\n",
              "      <td>BLANK WALLPLATE 1G WHITE</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5863921</th>\n",
              "      <td>201701</td>\n",
              "      <td>14:26:52</td>\n",
              "      <td>233070</td>\n",
              "      <td>A15017</td>\n",
              "      <td>33180</td>\n",
              "      <td>TAPE ELECT3/4X60'VYL ACE</td>\n",
              "      <td>0.99</td>\n",
              "      <td>2017</td>\n",
              "      <td>01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           Date Transaction Time Customer Number  ... Net Sales  year months\n",
              "5868993  201701         15:54:38           30247  ...     12.74  2017     01\n",
              "5863918  201701         14:26:52          233070  ...      1.99  2017     01\n",
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              "5863921  201701         14:26:52          233070  ...      0.99  2017     01\n",
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              "            Date Transaction Time Customer Number  ... Net Sales  year months\n",
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              "...          ...              ...             ...  ...       ...   ...    ...\n",
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              "\n",
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          "data": {
            "text/plain": [
              "*5        4967743\n",
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            ],
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              "            Date Transaction Time Customer Number  ... Net Sales  year months\n",
              "5862808   201701         14:04:01           10000  ...     17.99  2017     01\n",
              "6041366   201701         13:46:40           10000  ...      7.18  2017     01\n",
              "6038577   201701         12:10:30           10000  ...     17.59  2017     01\n",
              "6037625   201701         11:33:29           10000  ...     17.59  2017     01\n",
              "6037593   201701         11:32:04           10000  ...     39.98  2017     01\n",
              "...          ...              ...             ...  ...       ...   ...    ...\n",
              "16869008  201809         12:32:33           10000  ...    179.99  2018     09\n",
              "16869007  201809         12:32:33           10000  ...   -405.00  2018     09\n",
              "16869886  201809         12:51:44           10000  ...     42.39  2018     09\n",
              "16869887  201809         12:51:44           10000  ...     13.49  2018     09\n",
              "16869888  201809         12:51:44           10000  ...      3.58  2018     09\n",
              "\n",
              "[39117 rows x 9 columns]"
            ]
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        "train_RFM.loc[train_RFM['Customer Number'] == '24805']['Net Sales'].sum()"
      ],
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      "outputs": [
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          "output_type": "execute_result",
          "data": {
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              "12.58"
            ]
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            "tags": []
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      },
      "source": [
        "Cust_All.loc[(Cust_All['Customer Number'] == '24805')  ]"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Recency_Flag</th>\n",
              "      <th>Freq_Flag</th>\n",
              "      <th>Monetary_Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>173460</th>\n",
              "      <td>24805</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Customer Number  Recency_Flag  Freq_Flag  Monetary_Flag\n",
              "173460           24805             5          1              1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zz_Dwje1i1sa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Customer_Number = ['10000','24805']\n",
        "Recent_Date = ['09/2018','09/2018']\n",
        "Frequency_of_Shopping = ['39117','1']\n",
        "Total_Net_sales = ['1117686.91','12.58']\n",
        "resultDf = pd.DataFrame(list(zip(Customer_Number, Recent_Date,Frequency_of_Shopping,Total_Net_sales)), \n",
        "               columns =['Customer Number', 'Recent_Date','Frequency_of_Shopping','Total_Net_sales'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g3LM5vW4wTv_",
        "colab_type": "text"
      },
      "source": [
        " Table 1"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xR-qGTa_ki-M",
        "colab_type": "code",
        "outputId": "1b3cc722-b2e9-47d1-91d8-6ebc4d8f42a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "resultDf"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Recent_Date</th>\n",
              "      <th>Frequency_of_Shopping</th>\n",
              "      <th>Total_Net_sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>10000</td>\n",
              "      <td>09/2018</td>\n",
              "      <td>39117</td>\n",
              "      <td>1117686.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>24805</td>\n",
              "      <td>09/2018</td>\n",
              "      <td>1</td>\n",
              "      <td>12.58</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Customer Number Recent_Date Frequency_of_Shopping Total_Net_sales\n",
              "0           10000     09/2018                 39117      1117686.91\n",
              "1           24805     09/2018                     1           12.58"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j1J0TwenwXcU",
        "colab_type": "text"
      },
      "source": [
        "Table 2"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Irw6kXLRlL98",
        "colab_type": "code",
        "outputId": "58086f90-afb1-4fb8-d793-afd8c29f7674",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "Cust_All.loc[(Cust_All['Customer Number'].isin(['10000','24805']))  ]"
      ],
      "execution_count": 95,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "        vertical-align: middle;\n",
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              "\n",
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              "    }\n",
              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Customer Number</th>\n",
              "      <th>Recency_Flag</th>\n",
              "      <th>Freq_Flag</th>\n",
              "      <th>Monetary_Flag</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>87121</th>\n",
              "      <td>10000</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>173460</th>\n",
              "      <td>24805</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       Customer Number  Recency_Flag  Freq_Flag  Monetary_Flag\n",
              "87121            10000             5          5              5\n",
              "173460           24805             5          1              1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUvNSV7gvg_3",
        "colab_type": "text"
      },
      "source": [
        "## **Comparing Results to Validate**\n",
        "\n",
        "**Recency:**\n",
        "Our dataset contains latest transactions up to 09/2018 for all the customers. Recent_Date of transaction for both\n",
        "Customers is from 09/2018 which is under last 4 months. Thus, they should have Recency_Flag score 5 which\n",
        "we can validate from the table 2.\n",
        "\n",
        "**Frequency:**\n",
        "From table 1, Frequency of shopping for Customer Number ’10000’ is greater than the Customer Number ’24805’.\n",
        "Comparatively, Freq_Flag value for Customer Number ’10000’ should be the highest and for ’24805’ should be\n",
        "the lowest. Thus, we can validate that from table 2 as Freq_Flag for ’10000’ is 5 and for ’24805’ is 1.\n",
        "\n",
        "**Monetary:**\n",
        "Total Net Sales Amount for Customer Number ’10000’ is greater than the Customer Number ’24805’ as shown\n",
        "in table 1. Comparatively, Monetary_Flag value for Customer Number ’10000’ should be the highest and for\n",
        "’24805’ should be the lowest. Thus, we can validate that from table 2 as Monetary_Flag for ’10000’ is 5 and for\n",
        "’24805’ is 1."
      ]
    }
  ]
}