Customer_Lifetime_Value_prediction.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Customer_Lifetime_Value_prediction",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fde3vx7fp8mp"
      },
      "source": [
        "# Submitted By: Sandeep"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MnHbYImcKj2G"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q70WPaZ9jV5A",
        "outputId": "3f922518-027b-42be-b69e-12732fbd99e1"
      },
      "source": [
        "! ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Online_Retail.xlsx  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cJifpcO7Kyqo"
      },
      "source": [
        "df= pd.read_excel('Online_Retail.xlsx')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "PzpiA-7HM03F",
        "outputId": "e9facb6e-542a-49e8-c3f3-b3e96a6281eb"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "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>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ... CustomerID         Country\n",
              "0    536365    85123A  ...    17850.0  United Kingdom\n",
              "1    536365     71053  ...    17850.0  United Kingdom\n",
              "2    536365    84406B  ...    17850.0  United Kingdom\n",
              "3    536365    84029G  ...    17850.0  United Kingdom\n",
              "4    536365    84029E  ...    17850.0  United Kingdom\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kTOb2m14NIJH",
        "outputId": "bc6a8ba2-207c-407d-c0ff-515b0e4fd534"
      },
      "source": [
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo              object\n",
              "StockCode              object\n",
              "Description            object\n",
              "Quantity                int64\n",
              "InvoiceDate    datetime64[ns]\n",
              "UnitPrice             float64\n",
              "CustomerID            float64\n",
              "Country                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QN86ebcJNJ7b",
        "outputId": "24988071-9614-43f3-9c94-270ce58c74d9"
      },
      "source": [
        "df.InvoiceNo.unique"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<bound method Series.unique of 0         536365\n",
              "1         536365\n",
              "2         536365\n",
              "3         536365\n",
              "4         536365\n",
              "           ...  \n",
              "541904    581587\n",
              "541905    581587\n",
              "541906    581587\n",
              "541907    581587\n",
              "541908    581587\n",
              "Name: InvoiceNo, Length: 541909, dtype: object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I_NlJJIONajK",
        "outputId": "312016bf-e471-47fe-ec48-489e74ff0f58"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "541909"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OaFZccnFNd5B",
        "outputId": "b2414b22-5d78-4099-b536-fd118f5f95d3"
      },
      "source": [
        "df.drop_duplicates(keep='first',inplace=True)\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "536641"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i6QN-TfIN52u",
        "outputId": "00d83208-c1cb-480c-c3e2-991fd8a2b583"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo           0\n",
              "StockCode           0\n",
              "Description      1454\n",
              "Quantity            0\n",
              "InvoiceDate         0\n",
              "UnitPrice           0\n",
              "CustomerID     135037\n",
              "Country             0\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MTQTg6yjODIf",
        "outputId": "fbcec0ec-9460-4863-cb36-c565673b1196"
      },
      "source": [
        "df = df[df['Quantity']>0]\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "526054"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TC6MCiaeOP_V",
        "outputId": "cf2ce41e-eab8-4885-de91-46ad038d88da"
      },
      "source": [
        "df.InvoiceNo = df.InvoiceNo.astype(str)\n",
        "df.dtypes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "InvoiceNo              object\n",
              "StockCode              object\n",
              "Description            object\n",
              "Quantity                int64\n",
              "InvoiceDate    datetime64[ns]\n",
              "UnitPrice             float64\n",
              "CustomerID            float64\n",
              "Country                object\n",
              "dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DE6yPDzcRjhi",
        "outputId": "0085781e-804a-420e-81dd-86fe36478003"
      },
      "source": [
        "#number of canceled transactions\n",
        "count =0\n",
        "for i in df.InvoiceNo:\n",
        "  if i[0] == 'C' or str(i)[0] == 'c':\n",
        "    count += 1\n",
        "print(count)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I5c-sOw-Rnme",
        "outputId": "e2bda4f0-587c-4bc0-9e5b-190d623d88b9"
      },
      "source": [
        "df = df[~df.InvoiceNo.str.contains(\"C\")]\n",
        "# the number is 0 because we have already remove  quantity with 0 value.\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "526054"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 111
        },
        "id": "mpWsQU6WRvAV",
        "outputId": "5bad26c4-01be-4b85-d778-ddc6786dfe06"
      },
      "source": [
        "df[df.UnitPrice < 0]"
      ],
      "execution_count": null,
      "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>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>299983</th>\n",
              "      <td>A563186</td>\n",
              "      <td>B</td>\n",
              "      <td>Adjust bad debt</td>\n",
              "      <td>1</td>\n",
              "      <td>2011-08-12 14:51:00</td>\n",
              "      <td>-11062.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>299984</th>\n",
              "      <td>A563187</td>\n",
              "      <td>B</td>\n",
              "      <td>Adjust bad debt</td>\n",
              "      <td>1</td>\n",
              "      <td>2011-08-12 14:52:00</td>\n",
              "      <td>-11062.06</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       InvoiceNo StockCode  ... CustomerID         Country\n",
              "299983   A563186         B  ...        NaN  United Kingdom\n",
              "299984   A563187         B  ...        NaN  United Kingdom\n",
              "\n",
              "[2 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4WWyQNnJShah",
        "outputId": "e96a7f39-7f6f-4b71-90ed-a6b6f3a0f10b"
      },
      "source": [
        "df = df.dropna()\n",
        "len(df)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "392732"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMgFQ3XUUie1"
      },
      "source": [
        "df.CustomerID = df.CustomerID.astype(str)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "i_W607NMUAii",
        "outputId": "2a426aff-2ba5-4eba-f7f2-6d03904165a1"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "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>Quantity</th>\n",
              "      <th>UnitPrice</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>392732.000000</td>\n",
              "      <td>392732.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.153718</td>\n",
              "      <td>3.125596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>181.588420</td>\n",
              "      <td>22.240725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>6.000000</td>\n",
              "      <td>1.950000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>3.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>80995.000000</td>\n",
              "      <td>8142.750000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            Quantity      UnitPrice\n",
              "count  392732.000000  392732.000000\n",
              "mean       13.153718       3.125596\n",
              "std       181.588420      22.240725\n",
              "min         1.000000       0.000000\n",
              "25%         2.000000       1.250000\n",
              "50%         6.000000       1.950000\n",
              "75%        12.000000       3.750000\n",
              "max     80995.000000    8142.750000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wr3OKlepUpFT",
        "outputId": "d6b7fccc-7f80-4060-a9be-323aeb95b9a6"
      },
      "source": [
        "df.Country.unique()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany',\n",
              "       'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal',\n",
              "       'Italy', 'Belgium', 'Lithuania', 'Japan', 'Iceland',\n",
              "       'Channel Islands', 'Denmark', 'Cyprus', 'Sweden', 'Finland',\n",
              "       'Austria', 'Greece', 'Singapore', 'Lebanon',\n",
              "       'United Arab Emirates', 'Israel', 'Saudi Arabia', 'Czech Republic',\n",
              "       'Canada', 'Unspecified', 'Brazil', 'USA', 'European Community',\n",
              "       'Bahrain', 'Malta', 'RSA'], dtype=object)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H1GdcQ0bvGG1",
        "outputId": "55ffc366-283c-4d1d-c04b-4b1acd0f09bc"
      },
      "source": [
        "df.InvoiceDate.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: FutureWarning: Treating datetime data as categorical rather than numeric in `.describe` is deprecated and will be removed in a future version of pandas. Specify `datetime_is_numeric=True` to silence this warning and adopt the future behavior now.\n",
            "  \"\"\"Entry point for launching an IPython kernel.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count                  392732\n",
              "unique                  17286\n",
              "top       2011-11-14 15:27:00\n",
              "freq                      542\n",
              "first     2010-12-01 08:26:00\n",
              "last      2011-12-09 12:50:00\n",
              "Name: InvoiceDate, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bJ8GagbIU1yi"
      },
      "source": [
        "df1 = df[df['InvoiceDate']>= \"2010-12-09\"] # one year data\n",
        "len(df1)\n",
        "tx_data = df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvB-35UG3bKS"
      },
      "source": [
        "tx_uk = df[df['Country']=='United Kingdom'].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fjVzg8j09SKW"
      },
      "source": [
        "tx_uk['InvoiceYearMonth'] = tx_uk['InvoiceDate'].map(lambda date: 100*date.year + date.month)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 357
        },
        "id": "hK-P8CZm9QI_",
        "outputId": "e66cc8af-2a27-4680-a37a-1974102572b4"
      },
      "source": [
        "tx_uk.head()"
      ],
      "execution_count": null,
      "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>InvoiceNo</th>\n",
              "      <th>StockCode</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Country</th>\n",
              "      <th>InvoiceYearMonth</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>536365</td>\n",
              "      <td>85123A</td>\n",
              "      <td>WHITE HANGING HEART T-LIGHT HOLDER</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.55</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>201012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>536365</td>\n",
              "      <td>71053</td>\n",
              "      <td>WHITE METAL LANTERN</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>201012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>536365</td>\n",
              "      <td>84406B</td>\n",
              "      <td>CREAM CUPID HEARTS COAT HANGER</td>\n",
              "      <td>8</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>2.75</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>201012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029G</td>\n",
              "      <td>KNITTED UNION FLAG HOT WATER BOTTLE</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>201012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>536365</td>\n",
              "      <td>84029E</td>\n",
              "      <td>RED WOOLLY HOTTIE WHITE HEART.</td>\n",
              "      <td>6</td>\n",
              "      <td>2010-12-01 08:26:00</td>\n",
              "      <td>3.39</td>\n",
              "      <td>17850.0</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>201012</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  InvoiceNo StockCode  ...         Country  InvoiceYearMonth\n",
              "0    536365    85123A  ...  United Kingdom            201012\n",
              "1    536365     71053  ...  United Kingdom            201012\n",
              "2    536365    84406B  ...  United Kingdom            201012\n",
              "3    536365    84029G  ...  United Kingdom            201012\n",
              "4    536365    84029E  ...  United Kingdom            201012\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B_edfJqT9pt3"
      },
      "source": [
        "*Segmentation technique*\n",
        "**RFM**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JnLqVgNH9xld"
      },
      "source": [
        "Recency"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "knir7jLn9wd0"
      },
      "source": [
        "tx_user = pd.DataFrame(tx_data['CustomerID'].unique())\n",
        "tx_user.columns = ['CustomerID']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Njwfi4Xs-DcI"
      },
      "source": [
        "tx_max_purchase = tx_uk.groupby('CustomerID').InvoiceDate.max().reset_index()\n",
        "tx_max_purchase.columns = ['CustomerID','MaxPurchaseDate']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "1ctat0zx-aOZ",
        "outputId": "fb4f4165-93e1-481e-c882-32b4f108ed0f"
      },
      "source": [
        "tx_max_purchase['Recency'] = (tx_max_purchase['MaxPurchaseDate'].max() - tx_max_purchase['MaxPurchaseDate']).dt.days\n",
        "tx_max_purchase.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>MaxPurchaseDate</th>\n",
              "      <th>Recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>2011-01-18 10:01:00</td>\n",
              "      <td>325</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12747.0</td>\n",
              "      <td>2011-12-07 14:34:00</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12748.0</td>\n",
              "      <td>2011-12-09 12:20:00</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12749.0</td>\n",
              "      <td>2011-12-06 09:56:00</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12820.0</td>\n",
              "      <td>2011-12-06 15:12:00</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID     MaxPurchaseDate  Recency\n",
              "0    12346.0 2011-01-18 10:01:00      325\n",
              "1    12747.0 2011-12-07 14:34:00        1\n",
              "2    12748.0 2011-12-09 12:20:00        0\n",
              "3    12749.0 2011-12-06 09:56:00        3\n",
              "4    12820.0 2011-12-06 15:12:00        2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "Ihoc3hQF-kqr",
        "outputId": "ea30c2b1-3a72-43f4-a815-0157f8b2360a"
      },
      "source": [
        "tx_user = pd.merge(tx_user, tx_max_purchase[['CustomerID','Recency']], on='CustomerID')\n",
        "tx_user.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17850.0</td>\n",
              "      <td>371</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13047.0</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>13748.0</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>15100.0</td>\n",
              "      <td>333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15291.0</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Recency\n",
              "0    17850.0      371\n",
              "1    13047.0       31\n",
              "2    13748.0       95\n",
              "3    15100.0      333\n",
              "4    15291.0       25"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 290
        },
        "id": "u8xUa7x5-nPX",
        "outputId": "fcf6773a-3ded-44e4-df83-c6605b3e5f61"
      },
      "source": [
        "\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "sse={} # error\n",
        "tx_recency = tx_user[['Recency']]\n",
        "for k in range(1, 10):\n",
        "    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(tx_recency)\n",
        "    tx_recency[\"clusters\"] = kmeans.labels_  #cluster names corresponding to recency values \n",
        "    sse[k] = kmeans.inertia_ #sse corresponding to clusters\n",
        "plt.figure()\n",
        "plt.plot(list(sse.keys()), list(sse.values()))\n",
        "plt.xlabel(\"Number of cluster\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qhTzq9j1-qg2"
      },
      "source": [
        "#going with 3 clusters\n",
        "kmeans = KMeans(n_clusters=3)\n",
        "tx_user['RecencyCluster'] = kmeans.fit_predict(tx_user[['Recency']])\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "9ZNFEi16_RDe",
        "outputId": "c5900b90-db00-4df4-9a34-b718bddc2ea2"
      },
      "source": [
        "tx_user.groupby('RecencyCluster')['Recency'].describe()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2638.0</td>\n",
              "      <td>31.322214</td>\n",
              "      <td>25.272426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>558.0</td>\n",
              "      <td>292.654122</td>\n",
              "      <td>45.303609</td>\n",
              "      <td>224.0</td>\n",
              "      <td>254.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>325.0</td>\n",
              "      <td>373.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>725.0</td>\n",
              "      <td>153.960000</td>\n",
              "      <td>37.985019</td>\n",
              "      <td>93.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count        mean        std  ...    50%    75%    max\n",
              "RecencyCluster                                 ...                     \n",
              "0               2638.0   31.322214  25.272426  ...   24.0   50.0   92.0\n",
              "1                558.0  292.654122  45.303609  ...  283.0  325.0  373.0\n",
              "2                725.0  153.960000  37.985019  ...  154.0  185.0  222.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YWtgo0Hd_WQH"
      },
      "source": [
        "def order_cluster(cluster_field_name, target_field_name,df,ascending):\n",
        "    new_cluster_field_name = 'new_' + cluster_field_name\n",
        "    df_new = df.groupby(cluster_field_name)[target_field_name].mean().reset_index()\n",
        "    df_new = df_new.sort_values(by=target_field_name,ascending=ascending).reset_index(drop=True)\n",
        "    df_new['index'] = df_new.index\n",
        "    df_final = pd.merge(df,df_new[[cluster_field_name,'index']], on=cluster_field_name)\n",
        "    df_final = df_final.drop([cluster_field_name],axis=1)\n",
        "    df_final = df_final.rename(columns={\"index\":cluster_field_name})\n",
        "    return df_final\n",
        "\n",
        "tx_user = order_cluster('RecencyCluster', 'Recency',tx_user,False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "Hjg6tCMp_kLi",
        "outputId": "51a72624-70f3-47a7-bbe0-6608bdee48ce"
      },
      "source": [
        "tx_user.groupby('RecencyCluster')['Recency'].describe()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>558.0</td>\n",
              "      <td>292.654122</td>\n",
              "      <td>45.303609</td>\n",
              "      <td>224.0</td>\n",
              "      <td>254.0</td>\n",
              "      <td>283.0</td>\n",
              "      <td>325.0</td>\n",
              "      <td>373.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>725.0</td>\n",
              "      <td>153.960000</td>\n",
              "      <td>37.985019</td>\n",
              "      <td>93.0</td>\n",
              "      <td>120.0</td>\n",
              "      <td>154.0</td>\n",
              "      <td>185.0</td>\n",
              "      <td>222.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2638.0</td>\n",
              "      <td>31.322214</td>\n",
              "      <td>25.272426</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>24.0</td>\n",
              "      <td>50.0</td>\n",
              "      <td>92.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count        mean        std  ...    50%    75%    max\n",
              "RecencyCluster                                 ...                     \n",
              "0                558.0  292.654122  45.303609  ...  283.0  325.0  373.0\n",
              "1                725.0  153.960000  37.985019  ...  154.0  185.0  222.0\n",
              "2               2638.0   31.322214  25.272426  ...   24.0   50.0   92.0\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YZj30rpp_yhX"
      },
      "source": [
        "**Frequency**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QwiYi6Va_nxs"
      },
      "source": [
        "tx_frequency = tx_uk.groupby('CustomerID').InvoiceDate.count().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WajtbU3V_7ed"
      },
      "source": [
        "tx_frequency.columns = ['CustomerID','Frequency']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "J0-BTgVD_-kI",
        "outputId": "0833d756-ea21-498e-a67c-844734a85d8a"
      },
      "source": [
        "tx_user = pd.merge(tx_user, tx_frequency, on='CustomerID')\n",
        "tx_user.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17850.0</td>\n",
              "      <td>371</td>\n",
              "      <td>0</td>\n",
              "      <td>297</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15100.0</td>\n",
              "      <td>333</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>18074.0</td>\n",
              "      <td>373</td>\n",
              "      <td>0</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16250.0</td>\n",
              "      <td>260</td>\n",
              "      <td>0</td>\n",
              "      <td>24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13747.0</td>\n",
              "      <td>373</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Recency  RecencyCluster  Frequency\n",
              "0    17850.0      371               0        297\n",
              "1    15100.0      333               0          3\n",
              "2    18074.0      373               0         13\n",
              "3    16250.0      260               0         24\n",
              "4    13747.0      373               0          1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "Q5_xmR2RAD4b",
        "outputId": "8da636d2-09cb-4a88-c091-c09737800f59"
      },
      "source": [
        "\n",
        "sse={} # error\n",
        "tx_recency = tx_user[['Frequency']]\n",
        "for k in range(1, 10):\n",
        "    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(tx_recency)\n",
        "    tx_recency[\"clusters\"] = kmeans.labels_  #cluster names corresponding to recency values \n",
        "    sse[k] = kmeans.inertia_ #sse corresponding to clusters\n",
        "plt.figure()\n",
        "plt.plot(list(sse.keys()), list(sse.values()))\n",
        "plt.xlabel(\"Number of cluster\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "DqigXCh9AKHa",
        "outputId": "b2b12d20-50fa-4aee-a147-fe3b5dd75b3c"
      },
      "source": [
        "kmeans=KMeans(n_clusters=4)\n",
        "tx_user['FrequencyCluster']=kmeans.fit_predict(tx_user[['Frequency']])\n",
        "\n",
        "#order the frequency cluster\n",
        "tx_user = order_cluster('FrequencyCluster', 'Frequency', tx_user, True )\n",
        "tx_user.groupby('FrequencyCluster')['Frequency'].describe()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3468.0</td>\n",
              "      <td>48.214533</td>\n",
              "      <td>43.356567</td>\n",
              "      <td>1.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>33.0</td>\n",
              "      <td>71.0</td>\n",
              "      <td>183.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>428.0</td>\n",
              "      <td>319.754673</td>\n",
              "      <td>130.117216</td>\n",
              "      <td>184.0</td>\n",
              "      <td>218.0</td>\n",
              "      <td>277.0</td>\n",
              "      <td>384.0</td>\n",
              "      <td>785.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>22.0</td>\n",
              "      <td>1271.090909</td>\n",
              "      <td>489.487284</td>\n",
              "      <td>828.0</td>\n",
              "      <td>951.0</td>\n",
              "      <td>1100.0</td>\n",
              "      <td>1423.5</td>\n",
              "      <td>2677.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>3.0</td>\n",
              "      <td>5733.333333</td>\n",
              "      <td>1718.216032</td>\n",
              "      <td>4413.0</td>\n",
              "      <td>4762.0</td>\n",
              "      <td>5111.0</td>\n",
              "      <td>6393.5</td>\n",
              "      <td>7676.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                   count         mean          std  ...     50%     75%     max\n",
              "FrequencyCluster                                    ...                        \n",
              "0                 3468.0    48.214533    43.356567  ...    33.0    71.0   183.0\n",
              "1                  428.0   319.754673   130.117216  ...   277.0   384.0   785.0\n",
              "2                   22.0  1271.090909   489.487284  ...  1100.0  1423.5  2677.0\n",
              "3                    3.0  5733.333333  1718.216032  ...  5111.0  6393.5  7676.0\n",
              "\n",
              "[4 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EI6bS0HuASqA"
      },
      "source": [
        "tx_uk['Revenue'] = tx_uk['UnitPrice'] * tx_uk['Quantity']\n",
        "tx_revenue = tx_uk.groupby('CustomerID').Revenue.sum().reset_index()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "riSSLlXpAb8s"
      },
      "source": [
        "tx_user = pd.merge(tx_user, tx_revenue, on='CustomerID')\n"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "n66f6QoqAXR1",
        "outputId": "78cde18c-340d-4b78-a62d-379f911c79d0"
      },
      "source": [
        "tx_revenue.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12346.0</td>\n",
              "      <td>77183.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12747.0</td>\n",
              "      <td>4196.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12748.0</td>\n",
              "      <td>33053.19</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12749.0</td>\n",
              "      <td>4090.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12820.0</td>\n",
              "      <td>942.34</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID   Revenue\n",
              "0    12346.0  77183.60\n",
              "1    12747.0   4196.01\n",
              "2    12748.0  33053.19\n",
              "3    12749.0   4090.88\n",
              "4    12820.0    942.34"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "FUF0kozvAjzu",
        "outputId": "1176d8ae-b1c4-40f4-8eaf-14a65f46021f"
      },
      "source": [
        "tx_user.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17850.0</td>\n",
              "      <td>371</td>\n",
              "      <td>0</td>\n",
              "      <td>297</td>\n",
              "      <td>1</td>\n",
              "      <td>5391.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15808.0</td>\n",
              "      <td>305</td>\n",
              "      <td>0</td>\n",
              "      <td>195</td>\n",
              "      <td>1</td>\n",
              "      <td>3651.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14688.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>324</td>\n",
              "      <td>1</td>\n",
              "      <td>5579.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16029.0</td>\n",
              "      <td>38</td>\n",
              "      <td>2</td>\n",
              "      <td>241</td>\n",
              "      <td>1</td>\n",
              "      <td>80850.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13408.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>478</td>\n",
              "      <td>1</td>\n",
              "      <td>28117.04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Recency  RecencyCluster  Frequency  FrequencyCluster   Revenue\n",
              "0    17850.0      371               0        297                 1   5391.21\n",
              "1    15808.0      305               0        195                 1   3651.27\n",
              "2    14688.0        7               2        324                 1   5579.10\n",
              "3    16029.0       38               2        241                 1  80850.84\n",
              "4    13408.0        1               2        478                 1  28117.04"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KJN4iXdaAsJ1",
        "outputId": "73834400-9bda-4fa9-fc71-466275f35f3e"
      },
      "source": [
        "\n",
        "sse={} # error\n",
        "tx_recency = tx_user[['Revenue']]\n",
        "for k in range(1, 10):\n",
        "    kmeans = KMeans(n_clusters=k, max_iter=1000).fit(tx_recency)\n",
        "    tx_recency[\"clusters\"] = kmeans.labels_  #cluster names corresponding to recency values \n",
        "    sse[k] = kmeans.inertia_ #sse corresponding to clusters\n",
        "plt.figure()\n",
        "plt.plot(list(sse.keys()), list(sse.values()))\n",
        "plt.xlabel(\"Number of cluster\")\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n",
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "5amugbcYAyGk",
        "outputId": "47d3f8ae-dc75-4c9a-f6f4-85b6ce56b8c3"
      },
      "source": [
        "kmeans = KMeans(n_clusters=3)\n",
        "tx_user['RevenueCluster'] = kmeans.fit_predict(tx_user[['Revenue']])\n",
        "\n",
        "#order the cluster numbers\n",
        "tx_user = order_cluster('RevenueCluster', 'Revenue',tx_user,True)\n",
        "\n",
        "#show details of the dataframe\n",
        "tx_user.groupby('RevenueCluster')['Revenue'].describe()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RevenueCluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>3888.0</td>\n",
              "      <td>1352.299026</td>\n",
              "      <td>2083.663313</td>\n",
              "      <td>0.00</td>\n",
              "      <td>294.9625</td>\n",
              "      <td>637.775</td>\n",
              "      <td>1532.715</td>\n",
              "      <td>21086.30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>30.0</td>\n",
              "      <td>46825.514667</td>\n",
              "      <td>17975.526858</td>\n",
              "      <td>25977.16</td>\n",
              "      <td>32138.5575</td>\n",
              "      <td>41287.900</td>\n",
              "      <td>58699.180</td>\n",
              "      <td>91062.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3.0</td>\n",
              "      <td>207506.863333</td>\n",
              "      <td>46986.066791</td>\n",
              "      <td>168472.50</td>\n",
              "      <td>181431.6450</td>\n",
              "      <td>194390.790</td>\n",
              "      <td>227024.045</td>\n",
              "      <td>259657.30</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 count           mean  ...         75%        max\n",
              "RevenueCluster                         ...                       \n",
              "0               3888.0    1352.299026  ...    1532.715   21086.30\n",
              "1                 30.0   46825.514667  ...   58699.180   91062.38\n",
              "2                  3.0  207506.863333  ...  227024.045  259657.30\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 351
        },
        "id": "kXM8FRCPA6sL",
        "outputId": "4ba914fb-784a-42a9-ee4a-e4abd4f9e936"
      },
      "source": [
        "tx_user['OverallScore'] = tx_user['RecencyCluster'] + tx_user['FrequencyCluster'] + tx_user['RevenueCluster']\n",
        "tx_user.groupby('OverallScore')['Recency','Frequency','Revenue'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:2: FutureWarning: Indexing with multiple keys (implicitly converted to a tuple of keys) will be deprecated, use a list instead.\n",
            "  \n"
          ],
          "name": "stderr"
        },
        {
          "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>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Revenue</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OverallScore</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>292.537906</td>\n",
              "      <td>22.498195</td>\n",
              "      <td>366.528863</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>155.138462</td>\n",
              "      <td>36.402797</td>\n",
              "      <td>853.384337</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>35.110154</td>\n",
              "      <td>59.834089</td>\n",
              "      <td>1229.563084</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>16.876847</td>\n",
              "      <td>315.992611</td>\n",
              "      <td>5039.343202</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.400000</td>\n",
              "      <td>822.433333</td>\n",
              "      <td>27676.685000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>4.142857</td>\n",
              "      <td>1225.000000</td>\n",
              "      <td>106338.568571</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.333333</td>\n",
              "      <td>5733.333333</td>\n",
              "      <td>46245.940000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                 Recency    Frequency        Revenue\n",
              "OverallScore                                        \n",
              "0             292.537906    22.498195     366.528863\n",
              "1             155.138462    36.402797     853.384337\n",
              "2              35.110154    59.834089    1229.563084\n",
              "3              16.876847   315.992611    5039.343202\n",
              "4               7.400000   822.433333   27676.685000\n",
              "5               4.142857  1225.000000  106338.568571\n",
              "6               1.333333  5733.333333   46245.940000"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZID73GA-A_x1"
      },
      "source": [
        "tx_user['Segment'] = 'Bronze'\n",
        "tx_user.loc[tx_user['OverallScore']>2,'Segment'] = 'Silver' \n",
        "tx_user.loc[tx_user['OverallScore']>4,'Segment'] = 'Gold' "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "zLezSY3CBRH8",
        "outputId": "35ad2b94-6930-4416-cdeb-8f8cd16fb3df"
      },
      "source": [
        "tx_user.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>RevenueCluster</th>\n",
              "      <th>OverallScore</th>\n",
              "      <th>Segment</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>17850.0</td>\n",
              "      <td>371</td>\n",
              "      <td>0</td>\n",
              "      <td>297</td>\n",
              "      <td>1</td>\n",
              "      <td>5391.21</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15808.0</td>\n",
              "      <td>305</td>\n",
              "      <td>0</td>\n",
              "      <td>195</td>\n",
              "      <td>1</td>\n",
              "      <td>3651.27</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>Bronze</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14688.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>324</td>\n",
              "      <td>1</td>\n",
              "      <td>5579.10</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13767.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>368</td>\n",
              "      <td>1</td>\n",
              "      <td>17220.36</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13448.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>194</td>\n",
              "      <td>1</td>\n",
              "      <td>3534.69</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Recency  RecencyCluster  ...  RevenueCluster  OverallScore  Segment\n",
              "0    17850.0      371               0  ...               0             1   Bronze\n",
              "1    15808.0      305               0  ...               0             1   Bronze\n",
              "2    14688.0        7               2  ...               0             3   Silver\n",
              "3    13767.0        1               2  ...               0             3   Silver\n",
              "4    13448.0       16               2  ...               0             3   Silver\n",
              "\n",
              "[5 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F-TT0bC-BUra"
      },
      "source": [
        "from datetime import date, datetime,timedelta\n",
        "tx_6m1 = tx_uk[(tx_uk.InvoiceDate < datetime(2011,6,1)) & (tx_uk.InvoiceDate >= datetime(2011,1,1))].reset_index(drop=True) #6 months time\n",
        "tx_6m2 = tx_uk[(tx_uk.InvoiceDate >= datetime(2011,6,1)) & (tx_uk.InvoiceDate < datetime(2011,12,1))].reset_index(drop=True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bDUyHArDB3BU"
      },
      "source": [
        "tx_6m2['Revenue'] = tx_6m2['UnitPrice'] * tx_6m2['Quantity']\n",
        "tx_user_6m = tx_6m2.groupby('CustomerID')['Revenue'].sum().reset_index()\n",
        "tx_user_6m.columns = ['CustomerID','m6_Revenue']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "id": "7F4lu2TjCnI6",
        "outputId": "d525efe2-9e34-4829-aeba-b8fcb8b8ab7c"
      },
      "source": [
        "tx_user_6m.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>m6_Revenue</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>12747.0</td>\n",
              "      <td>1666.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>12748.0</td>\n",
              "      <td>22616.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12749.0</td>\n",
              "      <td>2468.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12820.0</td>\n",
              "      <td>561.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12822.0</td>\n",
              "      <td>948.88</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  m6_Revenue\n",
              "0    12747.0     1666.11\n",
              "1    12748.0    22616.37\n",
              "2    12749.0     2468.72\n",
              "3    12820.0      561.53\n",
              "4    12822.0      948.88"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7W1we7XdCq-H"
      },
      "source": [
        "tx_merge = pd.merge(tx_user, tx_user_6m, on='CustomerID', how='left')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sc3iD_8VC1uH"
      },
      "source": [
        "tx_merge = tx_merge[tx_merge['m6_Revenue']<tx_merge['m6_Revenue'].quantile(0.99)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2wYS57kC7cm"
      },
      "source": [
        "kmeans = KMeans(n_clusters=3)\n",
        "tx_merge['LTVCluster'] = kmeans.fit_predict(tx_merge[['m6_Revenue']])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "6w8f9iFPC_bs",
        "outputId": "005b1389-2569-4f95-c769-44ef72ff228a"
      },
      "source": [
        "tx_merge.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>RevenueCluster</th>\n",
              "      <th>OverallScore</th>\n",
              "      <th>Segment</th>\n",
              "      <th>m6_Revenue</th>\n",
              "      <th>LTVCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14688.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>324</td>\n",
              "      <td>1</td>\n",
              "      <td>5579.10</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>2163.40</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13767.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>368</td>\n",
              "      <td>1</td>\n",
              "      <td>17220.36</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>9093.09</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13448.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>194</td>\n",
              "      <td>1</td>\n",
              "      <td>3534.69</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>2111.43</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>15513.0</td>\n",
              "      <td>32</td>\n",
              "      <td>2</td>\n",
              "      <td>308</td>\n",
              "      <td>1</td>\n",
              "      <td>14758.22</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>8688.76</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>17920.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>665</td>\n",
              "      <td>1</td>\n",
              "      <td>4119.50</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>2115.79</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Recency  RecencyCluster  ...  Segment  m6_Revenue  LTVCluster\n",
              "2    14688.0        7               2  ...   Silver     2163.40           2\n",
              "3    13767.0        1               2  ...   Silver     9093.09           1\n",
              "4    13448.0       16               2  ...   Silver     2111.43           2\n",
              "5    15513.0       32               2  ...   Silver     8688.76           1\n",
              "6    17920.0        3               2  ...   Silver     2115.79           2\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 173
        },
        "id": "xgd7CquNDDfo",
        "outputId": "e73a3926-42c0-4ef0-dc6f-56d0f737fb6b"
      },
      "source": [
        "tx_merge = order_cluster('LTVCluster', 'm6_Revenue',tx_merge,True)\n",
        "\n",
        "#creatinga new cluster dataframe\n",
        "tx_cluster = tx_merge.copy()\n",
        "\n",
        "#see details of the clusters\n",
        "tx_cluster.groupby('LTVCluster')['m6_Revenue'].describe()"
      ],
      "execution_count": null,
      "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>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LTVCluster</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2522.0</td>\n",
              "      <td>506.062947</td>\n",
              "      <td>355.158090</td>\n",
              "      <td>0.00</td>\n",
              "      <td>219.665</td>\n",
              "      <td>397.25</td>\n",
              "      <td>738.895</td>\n",
              "      <td>1434.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>519.0</td>\n",
              "      <td>2368.044624</td>\n",
              "      <td>818.740458</td>\n",
              "      <td>1445.00</td>\n",
              "      <td>1710.760</td>\n",
              "      <td>2074.16</td>\n",
              "      <td>2887.305</td>\n",
              "      <td>4575.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>69.0</td>\n",
              "      <td>6878.172319</td>\n",
              "      <td>1819.328956</td>\n",
              "      <td>4745.69</td>\n",
              "      <td>5517.630</td>\n",
              "      <td>6379.09</td>\n",
              "      <td>7508.750</td>\n",
              "      <td>11992.40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             count         mean          std  ...      50%       75%       max\n",
              "LTVCluster                                    ...                             \n",
              "0           2522.0   506.062947   355.158090  ...   397.25   738.895   1434.37\n",
              "1            519.0  2368.044624   818.740458  ...  2074.16  2887.305   4575.92\n",
              "2             69.0  6878.172319  1819.328956  ...  6379.09  7508.750  11992.40\n",
              "\n",
              "[3 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 224
        },
        "id": "5bgh52H0DIVS",
        "outputId": "e598f283-741e-47b4-d796-2236a6da9b5a"
      },
      "source": [
        "tx_cluster.head()"
      ],
      "execution_count": null,
      "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>CustomerID</th>\n",
              "      <th>Recency</th>\n",
              "      <th>RecencyCluster</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>FrequencyCluster</th>\n",
              "      <th>Revenue</th>\n",
              "      <th>RevenueCluster</th>\n",
              "      <th>OverallScore</th>\n",
              "      <th>Segment</th>\n",
              "      <th>m6_Revenue</th>\n",
              "      <th>LTVCluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>14688.0</td>\n",
              "      <td>7</td>\n",
              "      <td>2</td>\n",
              "      <td>324</td>\n",
              "      <td>1</td>\n",
              "      <td>5579.10</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>2163.40</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>13448.0</td>\n",
              "      <td>16</td>\n",
              "      <td>2</td>\n",
              "      <td>194</td>\n",
              "      <td>1</td>\n",
              "      <td>3534.69</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>2111.43</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>17920.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>665</td>\n",
              "      <td>1</td>\n",
              "      <td>4119.50</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>2115.79</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>17377.0</td>\n",
              "      <td>23</td>\n",
              "      <td>2</td>\n",
              "      <td>404</td>\n",
              "      <td>1</td>\n",
              "      <td>4058.32</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>2460.70</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>17346.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2</td>\n",
              "      <td>483</td>\n",
              "      <td>1</td>\n",
              "      <td>2622.88</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>Silver</td>\n",
              "      <td>1702.10</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CustomerID  Recency  RecencyCluster  ...  Segment  m6_Revenue  LTVCluster\n",
              "0    14688.0        7               2  ...   Silver     2163.40           1\n",
              "1    13448.0       16               2  ...   Silver     2111.43           1\n",
              "2    17920.0        3               2  ...   Silver     2115.79           1\n",
              "3    17377.0       23               2  ...   Silver     2460.70           1\n",
              "4    17346.0        3               2  ...   Silver     1702.10           1\n",
              "\n",
              "[5 rows x 11 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJkWA6_hDL_j"
      },
      "source": [
        "tx_class = pd.get_dummies(tx_cluster)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HB1YUWffDQdU",
        "outputId": "b6098f67-47c0-4230-f67f-46126986c932"
      },
      "source": [
        "corr_matrix = tx_class.corr()\n",
        "corr_matrix['LTVCluster'].sort_values(ascending=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LTVCluster            1.000000\n",
              "m6_Revenue            0.867601\n",
              "Revenue               0.808438\n",
              "Frequency             0.527689\n",
              "Segment_Silver        0.502959\n",
              "                        ...   \n",
              "CustomerID_13596.0   -0.008248\n",
              "CustomerID_13332.0   -0.008248\n",
              "CustomerID_14946.0   -0.008248\n",
              "Recency              -0.274558\n",
              "Segment_Bronze       -0.502959\n",
              "Name: LTVCluster, Length: 3121, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xafXvj4ODUeo"
      },
      "source": [
        "X = tx_class.drop(['LTVCluster','m6_Revenue'],axis=1)\n",
        "y = tx_class['LTVCluster']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7qTboIpoDZ-y"
      },
      "source": [
        "#split training and test sets\n",
        "from sklearn.model_selection import KFold, cross_val_score, train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.05, random_state=56)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KdymWrouEhAt",
        "outputId": "ac5163f8-03e3-42ff-bdf0-629a6a00b4f4"
      },
      "source": [
        "import xgboost as xgb\n",
        "ltv_xgb_model = xgb.XGBClassifier(max_depth=5, learning_rate=0.1,n_jobs=-1).fit(X_train, y_train)\n",
        "\n",
        "print('Accuracy of XGB classifier on training set: {:.2f}'\n",
        "       .format(ltv_xgb_model.score(X_train, y_train)))\n",
        "print('Accuracy of XGB classifier on test set: {:.2f}'\n",
        "       .format(ltv_xgb_model.score(X_test[X_train.columns], y_test)))\n",
        "\n",
        "y_pred = ltv_xgb_model.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy of XGB classifier on training set: 0.97\n",
            "Accuracy of XGB classifier on test set: 0.92\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V292RourYQQ2"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cross_val=cross_val_score(ltv_xgb_model,X,y,cv=10,scoring='accuracy').mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O0FSafYiazjD",
        "outputId": "4e8c920f-0599-4b97-ea9b-74a073f33323"
      },
      "source": [
        "cross_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8807073954983922"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qEZiw-mLbC8m"
      },
      "source": [
        "filename = 'SimpleXgboost'\n",
        "pickle.dump(ltv_xgb_model, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RXJv24G1jHsE"
      },
      "source": [
        "***Tuning XGB Model***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WBZIGm94V_iJ"
      },
      "source": [
        "from sklearn.model_selection import RandomizedSearchCV"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XGHrbDhXon7W"
      },
      "source": [
        "\n",
        "from scipy.stats import randint"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ejnmOwT0uPvS"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc = StandardScaler()\n",
        "X_train = sc.fit_transform(X_train)\n",
        "X_test = sc.transform(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "O23L3ldVVSQX"
      },
      "source": [
        "params={\n",
        " \"learning_rate\"    : [0.05, 0.10, 0.15, 0.20, 0.25, 0.30 ] ,\n",
        " \"max_depth\"        : [ 3, 4, 5, 6, 8, 10, 12, 15],\n",
        " \"min_child_weight\" : [ 1, 3, 5, 7 ],\n",
        " \"gamma\"            : [ 0.0, 0.1, 0.2 , 0.3, 0.4 ],\n",
        " \"colsample_bytree\" : [ 0.3, 0.4, 0.5 , 0.7 ]\n",
        "    \n",
        "}"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NUUhrGgaVo0C"
      },
      "source": [
        "random_classifier = xgb.XGBClassifier()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1IERflOzViOQ"
      },
      "source": [
        "random_search=RandomizedSearchCV(random_classifier,param_distributions=params,n_iter=5,n_jobs=-1,cv=5,verbose=3)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VnErj77Tdtnk"
      },
      "source": [
        "from sklearn import preprocessing\n",
        "z = preprocessing.label_binarize(y, classes=[0, 1, 2, 3])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "48iCT2NYV84n",
        "outputId": "5b1594a6-b371-4551-a806-f5cd9e3f788b"
      },
      "source": [
        "random_search.fit(X, y)\n",
        "print(random_search.best_params_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Fitting 5 folds for each of 5 candidates, totalling 25 fits\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "[Parallel(n_jobs=-1)]: Using backend LokyBackend with 2 concurrent workers.\n",
            "[Parallel(n_jobs=-1)]: Done  25 out of  25 | elapsed:  9.6min finished\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "{'min_child_weight': 5, 'max_depth': 4, 'learning_rate': 0.05, 'gamma': 0.0, 'colsample_bytree': 0.4}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAM3WAmHiGMX",
        "outputId": "98ac4252-ebdd-4a36-8bc7-32da336cfd90"
      },
      "source": [
        "random_search.best_score_"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8765273311897108"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8lmhC1dhmlh"
      },
      "source": [
        "tuned_xgb = xgb.XGBClassifier(min_child_weight=5,max_depth=4,learning_rate=0.05,gamma=0.0,colsample_bytree=0.4)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zFtA791xiVkA",
        "outputId": "93301eb7-ccca-4c10-caa6-36c863410a26"
      },
      "source": [
        "tuned_xgb.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "              colsample_bynode=1, colsample_bytree=0.4, gamma=0.0,\n",
              "              learning_rate=0.05, max_delta_step=0, max_depth=4,\n",
              "              min_child_weight=5, missing=None, n_estimators=100, n_jobs=1,\n",
              "              nthread=None, objective='multi:softprob', random_state=0,\n",
              "              reg_alpha=0, reg_lambda=1, scale_pos_weight=1, seed=None,\n",
              "              silent=None, subsample=1, verbosity=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pj8zT-JZilhe"
      },
      "source": [
        "y_pred = tuned_xgb.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QxNDqPb3jVfU"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "accuracy_score=accuracy_score(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "daYtimqzjWXb",
        "outputId": "ad5e8d0a-4bc3-4078-8e07-06c5d50f9e34"
      },
      "source": [
        "accuracy_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9551282051282052"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 141
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ixHcjD3FjXG_"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cross_val=cross_val_score(tuned_xgb,X,y,cv=10,scoring='accuracy').mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ONPGR9Woj4em",
        "outputId": "9a731f45-4b6d-4eed-badd-3516e8777163"
      },
      "source": [
        "cross_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8893890675241156"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZLccvsAwknPU"
      },
      "source": [
        "filename = 'TunedXgboost'\n",
        "pickle.dump(tuned_xgb, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qQCwIIIPhXQW"
      },
      "source": [
        "***Using Random Forest Classifier***"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XL9G1lORGhxJ"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ASdb0JNBGpHG"
      },
      "source": [
        "est = RandomForestClassifier(n_jobs=-1)\n",
        "rf_p_dist={'max_depth':[3,5,10,None],\n",
        "              'n_estimators':[10,100,200,300,400,500],\n",
        "              'max_features':randint(1,3),\n",
        "               'criterion':['gini','entropy'],\n",
        "               'bootstrap':[True,False],\n",
        "               'min_samples_leaf':randint(1,4),\n",
        "              }\n",
        "def hypertuning_rscv(est, p_distr, nbr_iter,X,y):\n",
        "    rdmsearch = RandomizedSearchCV(est, param_distributions=p_distr,\n",
        "                                  n_jobs=-1, n_iter=nbr_iter, cv=9)\n",
        "    #CV = Cross-Validation ( here using Stratified KFold CV)\n",
        "    rdmsearch.fit(X,y)\n",
        "    ht_params = rdmsearch.best_params_\n",
        "    ht_score = rdmsearch.best_score_\n",
        "    return ht_params, ht_score\n",
        "\n",
        "rf_parameters, rf_ht_score = hypertuning_rscv(est, rf_p_dist, 40, X, y)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K91vvUQqGu50",
        "outputId": "88c4cfc1-f644-4005-9b28-4e888b72c74f"
      },
      "source": [
        "print(rf_parameters)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'bootstrap': False, 'criterion': 'gini', 'max_depth': None, 'max_features': 1, 'min_samples_leaf': 1, 'n_estimators': 200}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C4C6a_feJUPu",
        "outputId": "928785a6-a0a9-449c-d809-7dc9ec68901a"
      },
      "source": [
        "clasifier=RandomForestClassifier(bootstrap= False, criterion= 'gini', max_depth= None, max_features= 1, min_samples_leaf= 1, n_estimators= 200)\n",
        "\n",
        "clasifier.fit(X_train,y_train)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestClassifier(bootstrap=False, ccp_alpha=0.0, class_weight=None,\n",
              "                       criterion='gini', max_depth=None, max_features=1,\n",
              "                       max_leaf_nodes=None, max_samples=None,\n",
              "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
              "                       min_samples_leaf=1, min_samples_split=2,\n",
              "                       min_weight_fraction_leaf=0.0, n_estimators=200,\n",
              "                       n_jobs=None, oob_score=False, random_state=None,\n",
              "                       verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8zOoCCI9KCl-"
      },
      "source": [
        "y_pred = clasifier.predict(X_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n3BLdetEKGL8"
      },
      "source": [
        "from sklearn.metrics import confusion_matrix,accuracy_score\n",
        "cm = confusion_matrix(y_test, y_pred)\n",
        "\n",
        "accuracy_score=accuracy_score(y_test,y_pred)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7OpBe4zjPEEm",
        "outputId": "4267a192-5e6d-4581-a57f-bb7abe8a090e"
      },
      "source": [
        "accuracy_score"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9423076923076923"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Db4_U9U5PFjO"
      },
      "source": [
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "cross_val=cross_val_score(clasifier,X,y,cv=10,scoring='accuracy').mean()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Rg8-OKWrSKWE",
        "outputId": "e488c977-01ee-4392-a70b-9c19f82df6a9"
      },
      "source": [
        "cross_val"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8633440514469453"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 105
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1f5gMzmkPNB1"
      },
      "source": [
        "import pickle"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_eXCP0ewQrwP"
      },
      "source": [
        "filename = 'random_forest_retail.sav'\n",
        "pickle.dump(clasifier, open(filename, 'wb'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MlcMN6W0RKLt",
        "outputId": "ea20c6f7-69a2-4175-ab4a-a6d1caa856b2"
      },
      "source": [
        "!ls"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Online_Retail.xlsx  random_forest_retail.sav  sample_data\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Akhot-w6RS61"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}