Customer_Segmentation_using_big_data_analytics.ipynb

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        "outputId": "04e70d20-fc26-4c2b-d8c7-60eed393b1cd"
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          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HuSHNHzhgupq",
        "outputId": "a6e7c870-5931-4f38-f250-0811019c18fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  /content/drive/My Drive/Online Retail.zip\n",
            "  inflating: Online Retail.xlsx      \n",
            "  inflating: __MACOSX/._Online Retail.xlsx  \n"
          ]
        }
      ],
      "source": [
        "!unzip \"/content/drive/My Drive/Online Retail.zip\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
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          "name": "stdout",
          "text": [
            "Requirement already satisfied: openpyxl in /usr/local/lib/python3.7/dist-packages (3.0.9)\n",
            "Requirement already satisfied: et-xmlfile in /usr/local/lib/python3.7/dist-packages (from openpyxl) (1.1.0)\n"
          ]
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      ],
      "source": [
        "!pip install --upgrade openpyxl"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dVIoIRKiHdWY"
      },
      "source": [
        "**Exploratery Data Analysis**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K6hCqzTjqVQQ"
      },
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import time, warnings\n",
        "import datetime as dt\n",
        "\n",
        "#modules for predictive models\n",
        "import sklearn.cluster as cluster\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.decomposition import PCA\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "from sklearn.cluster import DBSCAN\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "#visualizations\n",
        "import matplotlib.pyplot as plt\n",
        "from pandas.plotting import scatter_matrix\n",
        "%matplotlib inline\n",
        "import seaborn as sns\n",
        "\n",
        "warnings.filterwarnings(\"ignore\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "R1TJ1R97eFZs"
      },
      "source": [
        "**Load the Dataset**"
      ]
    },
    {
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      "execution_count": null,
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        "colab": {
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        "outputId": "83cfc64c-f444-41ab-833e-3eae714c3a9e"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  InvoiceNo StockCode                          Description  Quantity  \\\n",
              "0    536365    85123A   WHITE HANGING HEART T-LIGHT HOLDER         6   \n",
              "1    536365     71053                  WHITE METAL LANTERN         6   \n",
              "2    536365    84406B       CREAM CUPID HEARTS COAT HANGER         8   \n",
              "3    536365    84029G  KNITTED UNION FLAG HOT WATER BOTTLE         6   \n",
              "4    536365    84029E       RED WOOLLY HOTTIE WHITE HEART.         6   \n",
              "\n",
              "          InvoiceDate  UnitPrice  CustomerID         Country  \n",
              "0 2010-12-01 08:26:00       2.55     17850.0  United Kingdom  \n",
              "1 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "2 2010-12-01 08:26:00       2.75     17850.0  United Kingdom  \n",
              "3 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  \n",
              "4 2010-12-01 08:26:00       3.39     17850.0  United Kingdom  "
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      ],
      "source": [
        "df = pd.read_excel(\"Online Retail.xlsx\")\n",
        "df.head()"
      ]
    },
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              "              index  Country\n",
              "0    United Kingdom   495478\n",
              "1           Germany     9495\n",
              "2            France     8557\n",
              "3              EIRE     8196\n",
              "4             Spain     2533\n",
              "5       Netherlands     2371\n",
              "6           Belgium     2069\n",
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              "8          Portugal     1519\n",
              "9         Australia     1259\n",
              "10           Norway     1086\n",
              "11            Italy      803\n",
              "12  Channel Islands      758\n",
              "13          Finland      695\n",
              "14           Cyprus      622\n",
              "15           Sweden      462\n",
              "16      Unspecified      446\n",
              "17          Austria      401\n",
              "18          Denmark      389\n",
              "19            Japan      358"
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.Country.value_counts().reset_index().head(20)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zyvsYY1cHB56"
      },
      "source": [
        "As above we have seen the major count is from United kingdom will be restricted to it which contains most of our customers historical data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mDsrscIoHNxW",
        "outputId": "ea9c7b37-eb81-4468-94c8-0fde65a30d3a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(495478, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "uk = df[df['Country']=='United Kingdom']\n",
        "uk.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dnyFfI-sefwX"
      },
      "source": [
        "**Remove Cancelled Orders or having negative transactions**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "buoSRn1eIZVh",
        "outputId": "6cdf1aff-2286-4582-a1bc-179620168f3c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(486286, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "uk = uk[uk['Quantity']>0]\n",
        "uk.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I8bGd5p3iUuX"
      },
      "source": [
        "**Remove rows where CustomerID are NA or having missing values**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KTuSLVJdJtIS",
        "outputId": "57dd0782-6746-4f0c-c9f1-b3655b485906"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(354345, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "uk.dropna(subset=['CustomerID'],how='all',inplace=True)\n",
        "uk.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ULql-ruQif5b"
      },
      "source": [
        "**Restricting the data to one year because it's better to use a metric per Months or Years in RFM**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RAlAuAglM7M0",
        "outputId": "1d4243d4-ff32-446a-93e6-518a473a7247"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(342478, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ],
      "source": [
        "uk = uk[uk['InvoiceDate']>= \"2010-12-09\"]\n",
        "uk.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qhjJogqTkpBz"
      },
      "outputs": [],
      "source": [
        "data= uk\n",
        "data.to_csv(('/content/drive/MyDrive/data.csv'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KMCWPBu8JwqX",
        "outputId": "a13df457-3a76-4048-a9a9-d9a368336e14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Summary..\n",
            "Number of transactions:  16017\n",
            "Number of products bought:  3611\n",
            "Number of customers: 3863\n",
            "Percentage of customers NA:  0.0 %\n"
          ]
        }
      ],
      "source": [
        "print(\"Summary..\")\n",
        "#exploring the unique values of each attributes\n",
        "print(\"Number of transactions: \", uk['InvoiceNo'].nunique())\n",
        "print(\"Number of products bought: \",uk['StockCode'].nunique())\n",
        "print(\"Number of customers:\", uk['CustomerID'].nunique() )\n",
        "print(\"Percentage of customers NA: \", round(uk['CustomerID'].isnull().sum() * 100 / len(df),2),\"%\" )"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0v9fBYmrL88A"
      },
      "source": [
        "***RFM Analysis***\n",
        "\n",
        "RFM (Recency, Frequency, Monetary) analysis is a customer segmentation technique that divides customers into groups based on past purchase behaviour.\n",
        "\n",
        "RFM assists in categorising customers into different groups or clusters in order to identify customers who are more likely to respond to promotions and also for future personalization services.\n",
        "\n",
        "We will create those 3 customer attributes for each customer"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RNJdzcHDNb0-"
      },
      "source": [
        "**Recency**\n",
        "\n",
        "To calculate recency, we need to choose a date point from which we evaluate **how many days ago was the customer's last purchase.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LJMNtXYdLN7G",
        "outputId": "6d6589c7-ac6e-4368-85d8-ce8de01968fc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Timestamp('2011-12-09 12:49:00')"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "#The last date available in our dataset\n",
        "uk['InvoiceDate'].max()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0NrQgdHLOHCo"
      },
      "source": [
        "The last date we have is 2011-12-09 so we will use it as reference."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KVUJhYl7ODix",
        "outputId": "349fb237-e899-458a-ab78-d815b254d3d7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2011-12-09\n"
          ]
        }
      ],
      "source": [
        "import time, warnings\n",
        "import datetime as dt\n",
        "now = dt.date(2011,12,9)\n",
        "print(now)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Ltf7xT2vOW0o"
      },
      "outputs": [],
      "source": [
        "#create a new column called date which contains the date of invoice only\n",
        "uk['date'] = uk['InvoiceDate'].dt.date"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "c0yWFJ_3OvFr",
        "outputId": "2968c01b-1481-462f-d3c3-df1eff1545b9"
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      "outputs": [
        {
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            "text/plain": [
              "      InvoiceNo StockCode                          Description  Quantity  \\\n",
              "19632    537879     22114    HOT WATER BOTTLE TEA AND SYMPATHY        12   \n",
              "19633    537879     22835      HOT WATER BOTTLE I AM SO POORLY         8   \n",
              "19634    537879     85150        LADIES & GENTLEMEN METAL SIGN         6   \n",
              "19635    537879     85048  15CM CHRISTMAS GLASS BALL 20 LIGHTS         4   \n",
              "19636    537879     21524       DOORMAT SPOTTY HOME SWEET HOME         2   \n",
              "\n",
              "              InvoiceDate  UnitPrice  CustomerID         Country        date  \n",
              "19632 2010-12-09 08:34:00       3.95     14243.0  United Kingdom  2010-12-09  \n",
              "19633 2010-12-09 08:34:00       4.65     14243.0  United Kingdom  2010-12-09  \n",
              "19634 2010-12-09 08:34:00       2.55     14243.0  United Kingdom  2010-12-09  \n",
              "19635 2010-12-09 08:34:00       7.95     14243.0  United Kingdom  2010-12-09  \n",
              "19636 2010-12-09 08:34:00       7.95     14243.0  United Kingdom  2010-12-09  "
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            "text/plain": [
              "   CustomerID LastPurshaseDate\n",
              "0     12346.0       2011-01-18\n",
              "1     12747.0       2011-12-07\n",
              "2     12748.0       2011-12-09\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "#group by customers and check last date of purshace\n",
        "recency_df = uk.groupby(by='CustomerID', as_index=False)['date'].max()\n",
        "recency_df.columns = ['CustomerID','LastPurshaseDate']\n",
        "recency_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RkgXmS1JPYK5"
      },
      "outputs": [],
      "source": [
        "#calculate recency\n",
        "recency_df['Recency'] = recency_df['LastPurshaseDate'].apply(lambda x: (now - x).days)"
      ]
    },
    {
      "cell_type": "code",
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        {
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            "text/plain": [
              "   CustomerID LastPurshaseDate  Recency\n",
              "0     12346.0       2011-01-18      325\n",
              "1     12747.0       2011-12-07        2\n",
              "2     12748.0       2011-12-09        0\n",
              "3     12749.0       2011-12-06        3\n",
              "4     12820.0       2011-12-06        3"
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              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>LastPurshaseDate</th>\n",
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              "      <td>2011-01-18</td>\n",
              "      <td>325</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>12748.0</td>\n",
              "      <td>2011-12-09</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>12749.0</td>\n",
              "      <td>2011-12-06</td>\n",
              "      <td>3</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>12820.0</td>\n",
              "      <td>2011-12-06</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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      ],
      "source": [
        "recency_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aBrIeVnoPYfk"
      },
      "outputs": [],
      "source": [
        "#drop LastPurchaseDate as we don't need it anymore\n",
        "recency_df.drop('LastPurshaseDate',axis=1,inplace=True)"
      ]
    },
    {
      "cell_type": "code",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Recency\n",
              "0     12346.0      325\n",
              "1     12747.0        2\n",
              "2     12748.0        0\n",
              "3     12749.0        3\n",
              "4     12820.0        3"
            ],
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              "      <th>CustomerID</th>\n",
              "      <th>Recency</th>\n",
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              "      <td>12749.0</td>\n",
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              "      <td>12820.0</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "recency_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vHite46zP0rn"
      },
      "source": [
        "Now we have the recency attribute created. e.g: Customer with ID = 12346 did his/her last purshace 325 days ago."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q3sjCi4YQnpT"
      },
      "source": [
        "**Frequency**-\n",
        "\n",
        "Frequency helps us to know **how many times a customer purchased from us**.\n",
        "\n",
        "To do that we need to check how many invoices are registered by the same customer."
      ]
    },
    {
      "cell_type": "code",
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Frequency\n",
              "0     12346.0          1\n",
              "1     12747.0         10\n",
              "2     12748.0        196\n",
              "3     12749.0          5\n",
              "4     12820.0          4"
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              "      <td>196</td>\n",
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              "      <td>5</td>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "# drop duplicates\n",
        "uk_copy = uk\n",
        "uk_copy.drop_duplicates(subset=['InvoiceNo', 'CustomerID'], keep=\"first\", inplace=True)\n",
        "#calculate frequency of purchases\n",
        "frequency_df = uk_copy.groupby(by=['CustomerID'], as_index=False)['InvoiceNo'].count()\n",
        "frequency_df.columns = ['CustomerID','Frequency']\n",
        "frequency_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DpbhDUpNRG00"
      },
      "source": [
        "**Monetary**\n",
        "\n",
        "Monetary attribute answers the question: **How much money did the customer spent over time?**\n",
        "\n",
        "\n",
        "To do that, first, we will create a new column total cost to have the total price per invoice."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Lw-GUDm9Q55M"
      },
      "outputs": [],
      "source": [
        "#create column total cost\n",
        "uk['TotalCost'] = uk['Quantity'] * uk['UnitPrice']"
      ]
    },
    {
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        {
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          "data": {
            "text/plain": [
              "   CustomerID  Monetary\n",
              "0     12346.0  77183.60\n",
              "1     12747.0    658.89\n",
              "2     12748.0   3739.23\n",
              "3     12749.0     98.35\n",
              "4     12820.0     58.20"
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              "  "
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          "metadata": {},
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      ],
      "source": [
        "monetary_df = uk.groupby(by='CustomerID',as_index=False).agg({'TotalCost': 'sum'})\n",
        "monetary_df.columns = ['CustomerID','Monetary']\n",
        "monetary_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J953BJH2R2vJ"
      },
      "source": [
        "**Create RFM Table**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "colab": {
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        "id": "XAjEppN_RaR5",
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      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Recency  Frequency\n",
              "0     12346.0      325          1\n",
              "1     12747.0        2         10\n",
              "2     12748.0        0        196\n",
              "3     12749.0        3          5\n",
              "4     12820.0        3          4"
            ],
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              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
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              "      <th>1</th>\n",
              "      <td>12747.0</td>\n",
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              "      <td>12820.0</td>\n",
              "      <td>3</td>\n",
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              "</table>\n",
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              "\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ],
      "source": [
        "#merge recency dataframe with frequency dataframe\n",
        "temp_df = recency_df.merge(frequency_df,on='CustomerID')\n",
        "temp_df.head()"
      ]
    },
    {
      "cell_type": "code",
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        "id": "-Ss5uqguUAIj",
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        {
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          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary\n",
              "CustomerID                              \n",
              "12346.0         325          1  77183.60\n",
              "12747.0           2         10    658.89\n",
              "12748.0           0        196   3739.23\n",
              "12749.0           3          5     98.35\n",
              "12820.0           3          4     58.20"
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "#merge with monetary dataframe to get a table with the 3 columns\n",
        "rfm_df = temp_df.merge(monetary_df,on='CustomerID')\n",
        "#use CustomerID as index\n",
        "rfm_df.set_index('CustomerID',inplace=True)\n",
        "#check the head\n",
        "rfm_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Peg8rDzSUTLg"
      },
      "source": [
        "Customer with ID = 12346 has recency: 325 days, frequency:1, and monetary: 77183,60 £."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qjEaopo8UZCy"
      },
      "source": [
        "**RFM Table Verification**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      InvoiceNo StockCode                     Description  Quantity  \\\n",
              "61619    541431     23166  MEDIUM CERAMIC TOP STORAGE JAR     74215   \n",
              "\n",
              "              InvoiceDate  UnitPrice  CustomerID         Country        date  \\\n",
              "61619 2011-01-18 10:01:00       1.04     12346.0  United Kingdom  2011-01-18   \n",
              "\n",
              "       TotalCost  \n",
              "61619    77183.6  "
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          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "uk[uk['CustomerID']==12346.0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4g5gLgvzVLmr"
      },
      "source": [
        "As we can see our RFM table is correct. The first customer bought only once, and only one product with huge amount."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6NcWe5-WVQoL"
      },
      "source": [
        "**Customer segments with RFM Model**\n",
        "\n",
        "Before moving to customer segments, Let's see the application of Pareto Principle – commonly referred to as the 80-20 rule on our dataset by applying it to our RFM variables.\n",
        "\n",
        "**Pareto’s rule says 80% of the results come from 20% of the causes.**\n",
        "\n",
        "Similarly, 20% customers contribute to 80% of your total revenue. \n",
        "\n",
        "Let's verify that because that will help us know which customers to focus on when marketing new products."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nc1g8B4VXrEs"
      },
      "source": [
        "**Applying 80-20 rule**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_Z89TXbwXoBP",
        "outputId": "684a2c5a-303c-4c3c-ef44-1ab006f88bd3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The 80% of total revenue is:  890679.54\n"
          ]
        }
      ],
      "source": [
        "#get the 80% of the revenue\n",
        "pareto_cutoff = rfm_df['Monetary'].sum() * 0.8\n",
        "print(\"The 80% of total revenue is: \",round(pareto_cutoff,2))"
      ]
    },
    {
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      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary    Rank\n",
              "CustomerID                                      \n",
              "12346.0         325          1  77183.60     2.0\n",
              "12747.0           2         10    658.89   183.0\n",
              "12748.0           0        196   3739.23    34.0\n",
              "12749.0           3          5     98.35  1140.0\n",
              "12820.0           3          4     58.20  1665.5"
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "customers_rank = rfm_df\n",
        "# Create a new column that is the rank of the value of coverage in ascending order\n",
        "customers_rank['Rank'] = customers_rank['Monetary'].rank(ascending=0)\n",
        "#customers_rank.drop('RevenueRank',axis=1,inplace=True)\n",
        "customers_rank.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cB2fKVBnYd_7"
      },
      "source": [
        "**Top Customers**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        "id": "il9edTYqYgEZ",
        "outputId": "b84bd748-6e38-4163-e46e-73b169d9063b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
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              "            Recency  Frequency   Monetary    Rank\n",
              "CustomerID                                       \n",
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          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "customers_rank.sort_values('Rank',ascending=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VzFJ98raYpYq",
        "outputId": "96c8b3bd-ddd0-4787-c5ea-2a757ee1ad2a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "772.6"
            ]
          },
          "metadata": {},
          "execution_count": 31
        }
      ],
      "source": [
        "#get top 20% of the customers\n",
        "top_20_cutoff = 3863 *20 /100\n",
        "top_20_cutoff"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wrhb454yYpDO",
        "outputId": "ba00c894-1d88-4b2b-8a57-d718b489ca2d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "976841.05"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "#sum the monetary values over the customer with rank <=773\n",
        "revenueByTop20 = customers_rank[customers_rank['Rank'] <= 773]['Monetary'].sum()\n",
        "revenueByTop20"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HgIiiQzib42A"
      },
      "source": [
        "In our case, the 80% of total revenue is not achieved by the 20% of TOP customers but approximately, it does, because they are less than our 20% TOP customers who achieve it. It would be interesting to study this group of customers because they are those who make our most revenue."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9Vjm7VHS2MId"
      },
      "source": [
        "## **Applying K-means clustering on RFM variables**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpCDtBD-p8Wn"
      },
      "source": [
        "**Preprocess Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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          "base_uri": "https://localhost:8080/",
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      },
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary    Rank\n",
              "CustomerID                                      \n",
              "12346.0         325          1  77183.60     2.0\n",
              "12747.0           2         10    658.89   183.0\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "rfm_data=rfm_df\n",
        "rfm_data.head()"
      ]
    },
    {
      "cell_type": "code",
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Recency  Frequency  Monetary      Rank\n",
              "Recency    1.000000  -0.279236 -0.027727  0.323091\n",
              "Frequency -0.279236   1.000000  0.156431 -0.458849\n",
              "Monetary  -0.027727   0.156431  1.000000 -0.130520\n",
              "Rank       0.323091  -0.458849 -0.130520  1.000000"
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>Monetary</th>\n",
              "      <th>Rank</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Recency</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.279236</td>\n",
              "      <td>-0.027727</td>\n",
              "      <td>0.323091</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Frequency</th>\n",
              "      <td>-0.279236</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.156431</td>\n",
              "      <td>-0.458849</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monetary</th>\n",
              "      <td>-0.027727</td>\n",
              "      <td>0.156431</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.130520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Rank</th>\n",
              "      <td>0.323091</td>\n",
              "      <td>-0.458849</td>\n",
              "      <td>-0.130520</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-baf8a880-1d50-404d-9c8b-eebea3756eec')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-baf8a880-1d50-404d-9c8b-eebea3756eec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ],
      "source": [
        "rfm_data.corr()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qxY5zzkWqQRQ"
      },
      "source": [
        "**Data Normalization**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4ks_wNBYhdS5"
      },
      "outputs": [],
      "source": [
        "#log transformation\n",
        "rfm_r_log = np.log(rfm_data['Recency']+0.1) #can't take log(0) and so add a small number\n",
        "rfm_f_log = np.log(rfm_data['Frequency'])\n",
        "rfm_m_log = np.log(rfm_data['Monetary']+0.1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gpE8HGzVhsI0"
      },
      "outputs": [],
      "source": [
        "log_data = pd.DataFrame({'Monetary': rfm_m_log,'Recency': rfm_r_log,'Frequency': rfm_f_log})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "2egvu5Ichv0Y",
        "outputId": "80610063-7cf5-4fc3-b91c-6adb32ca2ffe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Monetary   Recency  Frequency\n",
              "CustomerID                                \n",
              "12346.0     11.253944  5.784133   0.000000\n",
              "12747.0      6.490708  0.741937   2.302585\n",
              "12748.0      8.226662 -2.302585   5.278115\n",
              "12749.0      4.589549  1.131402   1.609438\n",
              "12820.0      4.065602  1.131402   1.386294"
            ],
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              "\n",
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              "      <td>1.609438</td>\n",
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              "    <tr>\n",
              "      <th>12820.0</th>\n",
              "      <td>4.065602</td>\n",
              "      <td>1.131402</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-08b887c7-0917-4c33-b20d-3e7473c54aea');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ],
      "source": [
        "log_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 142
        },
        "id": "66-Wt6VEiFRS",
        "outputId": "b8b23b51-cfbe-4f66-9f4d-f53ba4952910"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Monetary   Recency  Frequency\n",
              "Monetary   1.000000 -0.364351   0.715609\n",
              "Recency   -0.364351  1.000000  -0.569951\n",
              "Frequency  0.715609 -0.569951   1.000000"
            ],
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              "\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "\n",
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              "\n",
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              "        text-align: right;\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Monetary</th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>Monetary</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.364351</td>\n",
              "      <td>0.715609</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Recency</th>\n",
              "      <td>-0.364351</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.569951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Frequency</th>\n",
              "      <td>0.715609</td>\n",
              "      <td>-0.569951</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-374583ea-e522-418d-9b2a-d5db17c10601')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
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              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-374583ea-e522-418d-9b2a-d5db17c10601 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-374583ea-e522-418d-9b2a-d5db17c10601');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "log_data.corr()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m-AP96JLrXsa"
      },
      "source": [
        "## **K - Means Clustering**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y51KNScgrv7C"
      },
      "source": [
        "To Find optimal number of clusters,we used Elbow method where errors are plotted against K (cluster value) to identify optimal number of clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "HB9Vg4FvZLje",
        "outputId": "0111b0f3-2e78-4646-8b35-7a47666f1074"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "data_norm = log_data[[\"Recency\", \"Frequency\", \"Monetary\"]]\n",
        "\n",
        "sse = {}\n",
        "# Fit KMeans and calculate SSE for each k\n",
        "for k in range(1, 11):\n",
        "  \n",
        "    # Initialize KMeans with k clusters\n",
        "    kmeans = KMeans(n_clusters=k, random_state=1)\n",
        "    \n",
        "    # Fit KMeans on the normalized dataset\n",
        "    kmeans.fit(data_norm)\n",
        "    \n",
        "    # Assign sum of squared distances to k element of dictionary\n",
        "    sse[k] = kmeans.inertia_\n",
        "    \n",
        "# Plotting the elbow plot\n",
        "plt.figure(figsize=(10,4))\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('k'); \n",
        "plt.ylabel('Sum of squared errors')\n",
        "sns.pointplot(x=list(sse.keys()), y=list(sse.values()))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qCCXQoylsCwU"
      },
      "source": [
        "From the above plot, we can see that the optimal number of clusters can be taken as 2."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K8IatMersZIu"
      },
      "source": [
        "## **K-means Implementation**\n",
        "A common challenge with k-means is that you must tell it how many clusters you expect. Figuring out how many clusters we need is not obvious from data, thus we will try different clusters numbers and check their silhouette coefficient. The silhouette coefficient for a data point measures how similar it is to its assigned cluster from -1 (dissimilar) to 1 (similar). The elbow method can be used to determine the number of clusters as well."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f-uz89i8iG_6",
        "outputId": "7d2778b4-39b6-4671-b5a4-3dc600b52e2e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "For n_clusters = 2 The average silhouette_score is : 0.38940554101987457\n",
            "For n_clusters = 3 The average silhouette_score is : 0.30561967843301635\n",
            "For n_clusters = 4 The average silhouette_score is : 0.3096566189062348\n",
            "For n_clusters = 5 The average silhouette_score is : 0.2900384811306346\n",
            "For n_clusters = 6 The average silhouette_score is : 0.2951519419132616\n",
            "For n_clusters = 7 The average silhouette_score is : 0.29128648080510633\n",
            "For n_clusters = 8 The average silhouette_score is : 0.29146891086015536\n",
            "For n_clusters = 9 The average silhouette_score is : 0.2953614367144479\n"
          ]
        }
      ],
      "source": [
        "matrix = log_data\n",
        "for n_clusters in range(2,10):\n",
        "    kmeans = KMeans(init='k-means++', n_clusters = n_clusters, n_init=100)\n",
        "    kmeans.fit(matrix)\n",
        "    clusters = kmeans.predict(matrix)\n",
        "    silhouette_avg = silhouette_score(matrix, clusters)\n",
        "    print(\"For n_clusters =\", n_clusters, \"The average silhouette_score is :\", silhouette_avg)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8tv85PuJsq4R"
      },
      "source": [
        "The **best silhouette score** obtained is when the **number of clusters is 2.**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IvkKSb6cl-u-",
        "outputId": "32056c3b-4724-471b-dbe1-1cb4a58bed7b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "score de silhouette: 0.389\n"
          ]
        }
      ],
      "source": [
        "n_clusters = 2\n",
        "kmeans = KMeans(init='k-means++', n_clusters = n_clusters, n_init=30)\n",
        "kmeans.fit(matrix)\n",
        "clusters_customers = kmeans.predict(matrix)\n",
        "silhouette_avg = silhouette_score(matrix, clusters_customers)\n",
        "print('score de silhouette: {:<.3f}'.format(silhouette_avg))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMDusOYxtJbU"
      },
      "source": [
        "**Visualize Clusters**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "TODJANvDq82x",
        "outputId": "3df84729-8192-43b6-a735-904b4c64ab43"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#create a scatter plot\n",
        "plt.scatter(matrix.iloc[:, 0], matrix.iloc[:, 1], c=clusters_customers, s=50, cmap='viridis');\n",
        "#select cluster centers\n",
        "centers = kmeans.cluster_centers_\n",
        "plt.scatter(centers[:, 0], centers[:, 1], c='black', s=200, alpha=0.5);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h4_aKrWmllW5"
      },
      "source": [
        "###**DBSCAN ALGORITHM**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ReqNnsiDOa_A"
      },
      "source": [
        "# Building the Model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HPJWMzepOY0d"
      },
      "outputs": [],
      "source": [
        "# create an object\n",
        "db = DBSCAN(eps=0.8, min_samples=100, metric='euclidean')\n",
        "\n",
        "# fit the model\n",
        "db.fit(log_data)\n",
        "y_pred = db.fit_predict(log_data)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "core_samples_mask = np.zeros_like(db.labels_,dtype=bool)\n",
        "core_samples_mask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-qBSdLRzlkMj",
        "outputId": "99f3740b-6230-4b40-9b39-02b164ef6265"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, ..., False, False, False])"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noqsQf_xmEsc",
        "outputId": "4f2c90d8-fff1-46fa-a568-6db90c79bf2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1, -1, -1, ...,  0, -1,  0])"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "core_samples_mask[db.core_sample_indices_] = True\n",
        "core_samples_mask"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JottrlsTmOSg",
        "outputId": "3182775a-e56c-44ad-87b0-9fd2eb2c8b8f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False, False, ..., False, False,  True])"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MrZ20ItvOeX2"
      },
      "outputs": [],
      "source": [
        "# Cluster labled\n",
        "labels = db.labels_"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rKm4QomLmm11",
        "outputId": "1fa17a22-6449-4fa0-b557-d7e272187c00"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1, -1, -1, ...,  0, -1,  0])"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(log_data['Monetary'],log_data['Recency'],log_data['Frequency'],c=y_pred,cmap=\"Paired\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "9OwB29gqpZut",
        "outputId": "a5b56ba5-7a8c-4601-8cc8-4acfcdca2412"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n_cluster = len(set(labels)) - (1 if -1 in labels else 0)\n",
        "n_noise = list(labels).count(-1)\n",
        "print('No. of cluster in the dataset =',n_cluster)\n",
        "print('No. of noise data sample in dataset=', n_noise)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mFFBeX3rrD7X",
        "outputId": "954a46f9-922c-4712-f041-5cb5ee9b1e61"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "No. of cluster in the dataset = 1\n",
            "No. of noise data sample in dataset= 549\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HE_ytmdVOkwP"
      },
      "source": [
        "# Evaluation\n",
        "\n",
        "### Silhouette"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WyX3iHmsOhRR",
        "outputId": "151d79ff-0f8e-4902-82d7-9630acc8ace0"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The silhouette score is 0.38491471085955686\n"
          ]
        }
      ],
      "source": [
        "cluster_labels = db.labels_   \n",
        "\n",
        "# silhouette score\n",
        "silhouette_avg = silhouette_score(log_data, cluster_labels)\n",
        "print(\"The silhouette score is\", format(silhouette_avg))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 235
        },
        "id": "xH69pixvOn6S",
        "outputId": "27d4517d-dba5-4e9e-bfc0-b62e4af16deb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Monetary   Recency  Frequency  label\n",
              "CustomerID                                       \n",
              "12346.0     11.253944  5.784133   0.000000     -1\n",
              "12747.0      6.490708  0.741937   2.302585     -1\n",
              "12748.0      8.226662 -2.302585   5.278115     -1\n",
              "12749.0      4.589549  1.131402   1.609438      0\n",
              "12820.0      4.065602  1.131402   1.386294      0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7cb6396e-d397-4a37-8001-dfeb4fb5b690\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Monetary</th>\n",
              "      <th>Recency</th>\n",
              "      <th>Frequency</th>\n",
              "      <th>label</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>12346.0</th>\n",
              "      <td>11.253944</td>\n",
              "      <td>5.784133</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12747.0</th>\n",
              "      <td>6.490708</td>\n",
              "      <td>0.741937</td>\n",
              "      <td>2.302585</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12748.0</th>\n",
              "      <td>8.226662</td>\n",
              "      <td>-2.302585</td>\n",
              "      <td>5.278115</td>\n",
              "      <td>-1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12749.0</th>\n",
              "      <td>4.589549</td>\n",
              "      <td>1.131402</td>\n",
              "      <td>1.609438</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12820.0</th>\n",
              "      <td>4.065602</td>\n",
              "      <td>1.131402</td>\n",
              "      <td>1.386294</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7cb6396e-d397-4a37-8001-dfeb4fb5b690')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7cb6396e-d397-4a37-8001-dfeb4fb5b690 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7cb6396e-d397-4a37-8001-dfeb4fb5b690');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "log_data['label']=db.labels_\n",
        "\n",
        "log_data.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 803
        },
        "id": "jI7NWWQDOrDH",
        "outputId": "e6184620-ee9e-4beb-ce43-3cb7990550d2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "for c in log_data.columns[:-1]:\n",
        "    plt.figure(figsize=(6,4))\n",
        "    sns.boxplot(data=log_data, y=c, x='label')\n",
        "    plt.show()"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}