birch.ipynb

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      "name": "birch.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "amNKCrPN4hnx"
      },
      "source": [
        "# Birch clustering on Credit card dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QH_W2fvR4trQ"
      },
      "source": [
        "Libraries : "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_V3TyG-N8moY"
      },
      "source": [
        "# Birch for Clustering\n",
        "from sklearn.cluster import Birch\n",
        "\n",
        "# Essentials:\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import matplotlib.cm as cm\n",
        "\n",
        "#ignore warnings\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "# t-SNE visualization\n",
        "from sklearn.manifold import TSNE\n",
        "\n",
        "# imputation\n",
        "from sklearn.impute import KNNImputer\n",
        "\n",
        "# Scaling\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "\n",
        "# PCA\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "# K-means for Clustering\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# elbow method\n",
        "from yellowbrick.cluster import KElbowVisualizer\n",
        "\n",
        "# cluster metrics\n",
        "from sklearn.metrics import davies_bouldin_score\n",
        "from sklearn.metrics import silhouette_score\n",
        "\n",
        "# Silhouette Visualizer\n",
        "from yellowbrick.cluster import SilhouetteVisualizer\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LdGNz-RH46vg"
      },
      "source": [
        "Take a look at dataset : "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "X2So6nW54y3U",
        "outputId": "0ff46e88-dc3a-40e8-f94a-2e2bc0c1e10d"
      },
      "source": [
        "cc_general = pd.read_csv('/content/CC_GENERAL.csv')\n",
        "cc_general.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CUST_ID</th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <th>TENURE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>C10001</td>\n",
              "      <td>40.900749</td>\n",
              "      <td>0.818182</td>\n",
              "      <td>95.40</td>\n",
              "      <td>0.00</td>\n",
              "      <td>95.4</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1000.0</td>\n",
              "      <td>201.802084</td>\n",
              "      <td>139.509787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>C10002</td>\n",
              "      <td>3202.467416</td>\n",
              "      <td>0.909091</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>6442.945483</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>7000.0</td>\n",
              "      <td>4103.032597</td>\n",
              "      <td>1072.340217</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>C10003</td>\n",
              "      <td>2495.148862</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>773.17</td>\n",
              "      <td>773.17</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>12</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>622.066742</td>\n",
              "      <td>627.284787</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>C10004</td>\n",
              "      <td>1666.670542</td>\n",
              "      <td>0.636364</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>1499.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>205.788017</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>7500.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C10005</td>\n",
              "      <td>817.714335</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>16.00</td>\n",
              "      <td>16.00</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1200.0</td>\n",
              "      <td>678.334763</td>\n",
              "      <td>244.791237</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  CUST_ID      BALANCE  ...  PRC_FULL_PAYMENT  TENURE\n",
              "0  C10001    40.900749  ...          0.000000      12\n",
              "1  C10002  3202.467416  ...          0.222222      12\n",
              "2  C10003  2495.148862  ...          0.000000      12\n",
              "3  C10004  1666.670542  ...          0.000000      12\n",
              "4  C10005   817.714335  ...          0.000000      12\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v3XO_uDa5HgQ"
      },
      "source": [
        "Understand the features : "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "9dX2WANm5C_L",
        "outputId": "cc5e2f0f-7431-49bc-c1be-d779aac6480c"
      },
      "source": [
        "cc_general.describe()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <th>TENURE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8949.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8637.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "      <td>8950.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1564.474828</td>\n",
              "      <td>0.877271</td>\n",
              "      <td>1003.204834</td>\n",
              "      <td>592.437371</td>\n",
              "      <td>411.067645</td>\n",
              "      <td>978.871112</td>\n",
              "      <td>0.490351</td>\n",
              "      <td>0.202458</td>\n",
              "      <td>0.364437</td>\n",
              "      <td>0.135144</td>\n",
              "      <td>3.248827</td>\n",
              "      <td>14.709832</td>\n",
              "      <td>4494.449450</td>\n",
              "      <td>1733.143852</td>\n",
              "      <td>864.206542</td>\n",
              "      <td>0.153715</td>\n",
              "      <td>11.517318</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2081.531879</td>\n",
              "      <td>0.236904</td>\n",
              "      <td>2136.634782</td>\n",
              "      <td>1659.887917</td>\n",
              "      <td>904.338115</td>\n",
              "      <td>2097.163877</td>\n",
              "      <td>0.401371</td>\n",
              "      <td>0.298336</td>\n",
              "      <td>0.397448</td>\n",
              "      <td>0.200121</td>\n",
              "      <td>6.824647</td>\n",
              "      <td>24.857649</td>\n",
              "      <td>3638.815725</td>\n",
              "      <td>2895.063757</td>\n",
              "      <td>2372.446607</td>\n",
              "      <td>0.292499</td>\n",
              "      <td>1.338331</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>50.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.019163</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>128.281915</td>\n",
              "      <td>0.888889</td>\n",
              "      <td>39.635000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1600.000000</td>\n",
              "      <td>383.276166</td>\n",
              "      <td>169.123707</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>873.385231</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>361.280000</td>\n",
              "      <td>38.000000</td>\n",
              "      <td>89.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.166667</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3000.000000</td>\n",
              "      <td>856.901546</td>\n",
              "      <td>312.343947</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>2054.140036</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1110.130000</td>\n",
              "      <td>577.405000</td>\n",
              "      <td>468.637500</td>\n",
              "      <td>1113.821139</td>\n",
              "      <td>0.916667</td>\n",
              "      <td>0.300000</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.222222</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>17.000000</td>\n",
              "      <td>6500.000000</td>\n",
              "      <td>1901.134317</td>\n",
              "      <td>825.485459</td>\n",
              "      <td>0.142857</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>19043.138560</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>49039.570000</td>\n",
              "      <td>40761.250000</td>\n",
              "      <td>22500.000000</td>\n",
              "      <td>47137.211760</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.500000</td>\n",
              "      <td>123.000000</td>\n",
              "      <td>358.000000</td>\n",
              "      <td>30000.000000</td>\n",
              "      <td>50721.483360</td>\n",
              "      <td>76406.207520</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            BALANCE  BALANCE_FREQUENCY  ...  PRC_FULL_PAYMENT       TENURE\n",
              "count   8950.000000        8950.000000  ...       8950.000000  8950.000000\n",
              "mean    1564.474828           0.877271  ...          0.153715    11.517318\n",
              "std     2081.531879           0.236904  ...          0.292499     1.338331\n",
              "min        0.000000           0.000000  ...          0.000000     6.000000\n",
              "25%      128.281915           0.888889  ...          0.000000    12.000000\n",
              "50%      873.385231           1.000000  ...          0.000000    12.000000\n",
              "75%     2054.140036           1.000000  ...          0.142857    12.000000\n",
              "max    19043.138560           1.000000  ...          1.000000    12.000000\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZafaZQe05QzS"
      },
      "source": [
        "check the missing values :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-iYEwKmQ5K61",
        "outputId": "f4658dcf-a312-4bad-fd6b-ff680eac7d24"
      },
      "source": [
        "cc_general.isnull().sum()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CUST_ID                               0\n",
              "BALANCE                               0\n",
              "BALANCE_FREQUENCY                     0\n",
              "PURCHASES                             0\n",
              "ONEOFF_PURCHASES                      0\n",
              "INSTALLMENTS_PURCHASES                0\n",
              "CASH_ADVANCE                          0\n",
              "PURCHASES_FREQUENCY                   0\n",
              "ONEOFF_PURCHASES_FREQUENCY            0\n",
              "PURCHASES_INSTALLMENTS_FREQUENCY      0\n",
              "CASH_ADVANCE_FREQUENCY                0\n",
              "CASH_ADVANCE_TRX                      0\n",
              "PURCHASES_TRX                         0\n",
              "CREDIT_LIMIT                          1\n",
              "PAYMENTS                              0\n",
              "MINIMUM_PAYMENTS                    313\n",
              "PRC_FULL_PAYMENT                      0\n",
              "TENURE                                0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MaGEA0r5YQI"
      },
      "source": [
        "Check for outliers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tng91SyS5Veb"
      },
      "source": [
        "def outlier_percent(data):\n",
        "    Q1 = data.quantile(0.25)\n",
        "    Q3 = data.quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    minimum = Q1 - (1.5 * IQR)\n",
        "    maximum = Q3 + (1.5 * IQR)\n",
        "    num_outliers =  np.sum((data < minimum) |(data > maximum))\n",
        "    num_total = data.count()\n",
        "    return (num_outliers/num_total)*100"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F2Uoj8Zb5cJX",
        "outputId": "f9e9b15c-80c3-490b-9945-cc4e6e8cfd87"
      },
      "source": [
        "non_categorical_data = cc_general.drop(['CUST_ID'], axis=1)\n",
        "for column in non_categorical_data.columns:\n",
        "    data = non_categorical_data[column]\n",
        "    percent = str(round(outlier_percent(data), 2))\n",
        "    print(f'Outliers in \"{column}\": {percent}%')"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Outliers in \"BALANCE\": 7.77%\n",
            "Outliers in \"BALANCE_FREQUENCY\": 16.68%\n",
            "Outliers in \"PURCHASES\": 9.03%\n",
            "Outliers in \"ONEOFF_PURCHASES\": 11.32%\n",
            "Outliers in \"INSTALLMENTS_PURCHASES\": 9.69%\n",
            "Outliers in \"CASH_ADVANCE\": 11.51%\n",
            "Outliers in \"PURCHASES_FREQUENCY\": 0.0%\n",
            "Outliers in \"ONEOFF_PURCHASES_FREQUENCY\": 8.74%\n",
            "Outliers in \"PURCHASES_INSTALLMENTS_FREQUENCY\": 0.0%\n",
            "Outliers in \"CASH_ADVANCE_FREQUENCY\": 5.87%\n",
            "Outliers in \"CASH_ADVANCE_TRX\": 8.98%\n",
            "Outliers in \"PURCHASES_TRX\": 8.56%\n",
            "Outliers in \"CREDIT_LIMIT\": 2.77%\n",
            "Outliers in \"PAYMENTS\": 9.03%\n",
            "Outliers in \"MINIMUM_PAYMENTS\": 9.74%\n",
            "Outliers in \"PRC_FULL_PAYMENT\": 16.47%\n",
            "Outliers in \"TENURE\": 15.26%\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_Ml0mBwR5htz"
      },
      "source": [
        "# Preprocessing: "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Hk1cvAqm5lOI"
      },
      "source": [
        "remove the outliers:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_ByNKxIg5d-A",
        "outputId": "3cc6032b-7802-4593-c116-c64a267441dd"
      },
      "source": [
        "for column in non_categorical_data.columns:\n",
        "    data = non_categorical_data[column]\n",
        "    \n",
        "    Q1 = data.quantile(0.25)\n",
        "    Q3 = data.quantile(0.75)\n",
        "    IQR = Q3 - Q1\n",
        "    minimum = Q1 - (1.5 * IQR)\n",
        "    maximum = Q3 + (1.5 * IQR)\n",
        " \n",
        "    outliers = ((data < minimum) |(data > maximum))\n",
        "    non_categorical_data[column].loc[outliers] = np.nan\n",
        "    \n",
        "non_categorical_data.isnull().sum()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BALANCE                              695\n",
              "BALANCE_FREQUENCY                   1493\n",
              "PURCHASES                            808\n",
              "ONEOFF_PURCHASES                    1013\n",
              "INSTALLMENTS_PURCHASES               867\n",
              "CASH_ADVANCE                        1030\n",
              "PURCHASES_FREQUENCY                    0\n",
              "ONEOFF_PURCHASES_FREQUENCY           782\n",
              "PURCHASES_INSTALLMENTS_FREQUENCY       0\n",
              "CASH_ADVANCE_FREQUENCY               525\n",
              "CASH_ADVANCE_TRX                     804\n",
              "PURCHASES_TRX                        766\n",
              "CREDIT_LIMIT                         249\n",
              "PAYMENTS                             808\n",
              "MINIMUM_PAYMENTS                    1154\n",
              "PRC_FULL_PAYMENT                    1474\n",
              "TENURE                              1366\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BS6tlkRI5zvx"
      },
      "source": [
        "# Imputing the missing data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oyzTrOpA51py"
      },
      "source": [
        "I use KNN imputer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Q6gt0qG5qeh",
        "outputId": "e5b72058-0a06-48e4-8961-dd0abfa3ed7d"
      },
      "source": [
        "imputer = KNNImputer()\n",
        "imp_data = pd.DataFrame(imputer.fit_transform(non_categorical_data), columns=non_categorical_data.columns)\n",
        "imp_data.isnull().sum()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "BALANCE                             0\n",
              "BALANCE_FREQUENCY                   0\n",
              "PURCHASES                           0\n",
              "ONEOFF_PURCHASES                    0\n",
              "INSTALLMENTS_PURCHASES              0\n",
              "CASH_ADVANCE                        0\n",
              "PURCHASES_FREQUENCY                 0\n",
              "ONEOFF_PURCHASES_FREQUENCY          0\n",
              "PURCHASES_INSTALLMENTS_FREQUENCY    0\n",
              "CASH_ADVANCE_FREQUENCY              0\n",
              "CASH_ADVANCE_TRX                    0\n",
              "PURCHASES_TRX                       0\n",
              "CREDIT_LIMIT                        0\n",
              "PAYMENTS                            0\n",
              "MINIMUM_PAYMENTS                    0\n",
              "PRC_FULL_PAYMENT                    0\n",
              "TENURE                              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Za1XPs-6Diq"
      },
      "source": [
        "# Scale the Data:"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHPFiPiF6H25"
      },
      "source": [
        "I use StandardScaler"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "pl5oFewg580E",
        "outputId": "1c009ada-c108-4770-a94d-ac6cf50006f1"
      },
      "source": [
        "std_imp_data = pd.DataFrame(StandardScaler().fit_transform(imp_data), columns=imp_data.columns)\n",
        "std_imp_data.describe()"
      ],
      "execution_count": 9,
      "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>BALANCE</th>\n",
              "      <th>BALANCE_FREQUENCY</th>\n",
              "      <th>PURCHASES</th>\n",
              "      <th>ONEOFF_PURCHASES</th>\n",
              "      <th>INSTALLMENTS_PURCHASES</th>\n",
              "      <th>CASH_ADVANCE</th>\n",
              "      <th>PURCHASES_FREQUENCY</th>\n",
              "      <th>ONEOFF_PURCHASES_FREQUENCY</th>\n",
              "      <th>PURCHASES_INSTALLMENTS_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_FREQUENCY</th>\n",
              "      <th>CASH_ADVANCE_TRX</th>\n",
              "      <th>PURCHASES_TRX</th>\n",
              "      <th>CREDIT_LIMIT</th>\n",
              "      <th>PAYMENTS</th>\n",
              "      <th>MINIMUM_PAYMENTS</th>\n",
              "      <th>PRC_FULL_PAYMENT</th>\n",
              "      <th>TENURE</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8.950000e+03</td>\n",
              "      <td>8950.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>1.459292e-16</td>\n",
              "      <td>3.641780e-15</td>\n",
              "      <td>-3.845664e-15</td>\n",
              "      <td>8.330072e-15</td>\n",
              "      <td>9.068103e-16</td>\n",
              "      <td>8.933760e-16</td>\n",
              "      <td>-3.716084e-16</td>\n",
              "      <td>-7.758809e-15</td>\n",
              "      <td>2.353921e-16</td>\n",
              "      <td>-9.233334e-16</td>\n",
              "      <td>-7.427330e-17</td>\n",
              "      <td>9.962980e-16</td>\n",
              "      <td>4.909878e-15</td>\n",
              "      <td>4.842805e-16</td>\n",
              "      <td>-2.008573e-16</td>\n",
              "      <td>-2.921344e-15</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>1.000056e+00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-9.711973e-01</td>\n",
              "      <td>-4.155613e+00</td>\n",
              "      <td>-9.108875e-01</td>\n",
              "      <td>-6.679045e-01</td>\n",
              "      <td>-7.876755e-01</td>\n",
              "      <td>-6.489418e-01</td>\n",
              "      <td>-1.221758e+00</td>\n",
              "      <td>-7.446281e-01</td>\n",
              "      <td>-9.169952e-01</td>\n",
              "      <td>-7.472686e-01</td>\n",
              "      <td>-6.819606e-01</td>\n",
              "      <td>-9.664084e-01</td>\n",
              "      <td>-1.365191e+00</td>\n",
              "      <td>-1.191542e+00</td>\n",
              "      <td>-1.158882e+00</td>\n",
              "      <td>-4.495377e-01</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-8.704482e-01</td>\n",
              "      <td>3.667368e-01</td>\n",
              "      <td>-8.520982e-01</td>\n",
              "      <td>-6.679045e-01</td>\n",
              "      <td>-7.876755e-01</td>\n",
              "      <td>-6.489418e-01</td>\n",
              "      <td>-1.014125e+00</td>\n",
              "      <td>-7.446281e-01</td>\n",
              "      <td>-9.169952e-01</td>\n",
              "      <td>-7.472686e-01</td>\n",
              "      <td>-6.819606e-01</td>\n",
              "      <td>-8.736091e-01</td>\n",
              "      <td>-8.652204e-01</td>\n",
              "      <td>-8.088997e-01</td>\n",
              "      <td>-7.803523e-01</td>\n",
              "      <td>-4.495377e-01</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-2.883274e-01</td>\n",
              "      <td>3.667368e-01</td>\n",
              "      <td>-3.757635e-01</td>\n",
              "      <td>-5.868463e-01</td>\n",
              "      <td>-4.754304e-01</td>\n",
              "      <td>-6.489418e-01</td>\n",
              "      <td>2.404259e-02</td>\n",
              "      <td>-3.780223e-01</td>\n",
              "      <td>-4.976286e-01</td>\n",
              "      <td>-7.472686e-01</td>\n",
              "      <td>-6.819606e-01</td>\n",
              "      <td>-3.168136e-01</td>\n",
              "      <td>-4.136338e-01</td>\n",
              "      <td>-3.375007e-01</td>\n",
              "      <td>-4.237788e-01</td>\n",
              "      <td>-4.495377e-01</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>5.646491e-01</td>\n",
              "      <td>3.667368e-01</td>\n",
              "      <td>6.126365e-01</td>\n",
              "      <td>3.671317e-01</td>\n",
              "      <td>5.319986e-01</td>\n",
              "      <td>5.416827e-01</td>\n",
              "      <td>1.062211e+00</td>\n",
              "      <td>4.485112e-01</td>\n",
              "      <td>9.701506e-01</td>\n",
              "      <td>4.368733e-01</td>\n",
              "      <td>4.783950e-01</td>\n",
              "      <td>6.111790e-01</td>\n",
              "      <td>6.508204e-01</td>\n",
              "      <td>6.738027e-01</td>\n",
              "      <td>6.386496e-01</td>\n",
              "      <td>-2.118108e-01</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2.911266e+00</td>\n",
              "      <td>3.667368e-01</td>\n",
              "      <td>3.120231e+00</td>\n",
              "      <td>3.512681e+00</td>\n",
              "      <td>3.318839e+00</td>\n",
              "      <td>3.337571e+00</td>\n",
              "      <td>1.269843e+00</td>\n",
              "      <td>2.554838e+00</td>\n",
              "      <td>1.599199e+00</td>\n",
              "      <td>3.128100e+00</td>\n",
              "      <td>3.185891e+00</td>\n",
              "      <td>2.838361e+00</td>\n",
              "      <td>3.005522e+00</td>\n",
              "      <td>2.978873e+00</td>\n",
              "      <td>2.852750e+00</td>\n",
              "      <td>4.305014e+00</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            BALANCE  BALANCE_FREQUENCY  ...  PRC_FULL_PAYMENT  TENURE\n",
              "count  8.950000e+03       8.950000e+03  ...      8.950000e+03  8950.0\n",
              "mean   1.459292e-16       3.641780e-15  ...     -2.921344e-15     0.0\n",
              "std    1.000056e+00       1.000056e+00  ...      1.000056e+00     0.0\n",
              "min   -9.711973e-01      -4.155613e+00  ...     -4.495377e-01     0.0\n",
              "25%   -8.704482e-01       3.667368e-01  ...     -4.495377e-01     0.0\n",
              "50%   -2.883274e-01       3.667368e-01  ...     -4.495377e-01     0.0\n",
              "75%    5.646491e-01       3.667368e-01  ...     -2.118108e-01     0.0\n",
              "max    2.911266e+00       3.667368e-01  ...      4.305014e+00     0.0\n",
              "\n",
              "[8 rows x 17 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "StIvBbp96NtJ"
      },
      "source": [
        "## Dimention Reduction using PCA"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 964
        },
        "id": "Ju5OIZO16Bzn",
        "outputId": "a723a1e2-b198-4329-dbc1-0d35f3021a9b"
      },
      "source": [
        "pca = PCA(n_components=2, random_state=42)\n",
        "pca.fit(std_imp_data)\n",
        "PC_names = ['PC'+str(x) for x in range(1,len(pca.components_)+1)]\n",
        "\n",
        "\n",
        "pca_data = pd.DataFrame(pca.transform(std_imp_data), columns=PC_names)\n",
        "\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(24, 16))\n",
        "plt.imshow(pca.components_.T,\n",
        "           cmap=\"Spectral\",\n",
        "           vmin=-1,\n",
        "           vmax=1,\n",
        "          )\n",
        "plt.yticks(range(len(std_imp_data.columns)), std_imp_data.columns)\n",
        "plt.xticks(range(len(pca_data.columns)), pca_data.columns)\n",
        "plt.xlabel(\"Principal Component\")\n",
        "plt.ylabel(\"Contribution\")\n",
        "plt.title(\"Contribution of Features to Components\")\n",
        "plt.colorbar()"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.colorbar.Colorbar at 0x7f520f0510d0>"
            ]
          },
          "metadata": {},
          "execution_count": 10
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1728x1152 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qa-WF4l3Ai80"
      },
      "source": [
        "# Number of clusters? "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5VTEwiYkAurD"
      },
      "source": [
        "### 1) Optimize n_clusters with Elbow:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzXrsFrmApPj"
      },
      "source": [
        "wcss = []\n",
        "\n",
        "for i in range(1, 11): \n",
        "    kmeans = KMeans(n_clusters=i, init = 'k-means++', max_iter=300,\n",
        "                    n_init=10,\n",
        "                    random_state=0)\n",
        "    kmeans.fit(pca_data)\n",
        "    wcss.append(kmeans.inertia_)"
      ],
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
        },
        "id": "RxTAVvGgGrIs",
        "outputId": "fff91680-6146-4574-dbe5-340c1c88cba8"
      },
      "source": [
        "fig = plt.figure(figsize = (10,3))\n",
        "plt.plot(range(1,11), wcss,\n",
        "         linewidth=4, markersize=12, marker='o', color = 'red')\n",
        "plt.xticks(np.arange(11))\n",
        "plt.xlabel(\"Number of clusters\")\n",
        "plt.ylabel(\"WCSS\")\n",
        "plt.show()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x216 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hI_kXaniHRs2"
      },
      "source": [
        "Here n_clusters is 3!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IjjVRbjEKEUt"
      },
      "source": [
        "### Optimize n_clusters with Silhouette_score :"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HTcrUPZMKLmQ"
      },
      "source": [
        "def visualize_silhouette(cluster_lists, X_features): \n",
        "\n",
        "    n_cols = len(cluster_lists)\n",
        "    fig, axs = plt.subplots(figsize=(4*n_cols, 4), nrows=1, ncols=n_cols)\n",
        "\n",
        "    for ind, n_cluster in enumerate(cluster_lists):\n",
        "        clusterer = KMeans(n_clusters = n_cluster, max_iter=500, random_state=0, init = 'k-means++')\n",
        "        cluster_labels = clusterer.fit_predict(X_features)\n",
        "\n",
        "        sil_avg = silhouette_score(X_features, cluster_labels)\n",
        "        sil_values = silhouette_samples(X_features, cluster_labels)\n",
        "\n",
        "        y_lower = 10\n",
        "        axs[ind].set_title('Number of Cluster : '+ str(n_cluster)+'\\n' \\\n",
        "                          'Silhouette Score :' + str(round(sil_avg,3)) )\n",
        "        axs[ind].set_xlabel(\"The silhouette coefficient values\")\n",
        "        axs[ind].set_ylabel(\"Cluster label\")\n",
        "        axs[ind].set_xlim([-0.1, 1])\n",
        "        axs[ind].set_ylim([0, len(X_features) + (n_cluster + 1) * 10])\n",
        "        axs[ind].set_yticks([])  # Clear the yaxis labels / ticks\n",
        "        axs[ind].set_xticks([0, 0.2, 0.4, 0.6, 0.8, 1])\n",
        "   \n",
        "        for i in range(n_cluster):\n",
        "            ith_cluster_sil_values = sil_values[cluster_labels==i]\n",
        "            ith_cluster_sil_values.sort()\n",
        "\n",
        "            size_cluster_i = ith_cluster_sil_values.shape[0]\n",
        "            y_upper = y_lower + size_cluster_i\n",
        "\n",
        "            color = cm.nipy_spectral(float(i) / n_cluster)\n",
        "            axs[ind].fill_betweenx(np.arange(y_lower, y_upper), 0, ith_cluster_sil_values, \\\n",
        "                                facecolor=color, edgecolor=color, alpha=0.7)\n",
        "            axs[ind].text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))\n",
        "            y_lower = y_upper + 10\n",
        "\n",
        "        axs[ind].axvline(x=sil_avg, color=\"red\", linestyle=\"--\")"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        },
        "id": "HCzNbnReK4if",
        "outputId": "7be50f35-1c0c-4ab0-f549-a2a7532a6faf"
      },
      "source": [
        "visualize_silhouette([2, 3, 4, 5, 6, 7, 8, 9, 10], pca_data)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 2592x288 with 9 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MBU4yqbk6Yji"
      },
      "source": [
        "# Train the Model: "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ueGjzDosLtWt"
      },
      "source": [
        "So, I set n_clusters = 3."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QWL_l8oZ6VPB"
      },
      "source": [
        "model = Birch(threshold=1.7, n_clusters=3)\n",
        "y_birch = model.fit_predict(pca_data)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 294
        },
        "id": "2V7ll6_97AMX",
        "outputId": "87983e0b-9883-4862-9819-c7645fd3a85b"
      },
      "source": [
        "plt.scatter(pca_data['PC1'], pca_data['PC2'], c=y_birch,\n",
        "            cmap='brg')\n",
        "plt.title('results')\n",
        "plt.xlabel('feature 1')\n",
        "plt.ylabel('feature 2')\n",
        "plt.show()"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7NLMwLSf9dgw"
      },
      "source": [
        ""
      ],
      "execution_count": 16,
      "outputs": []
    }
  ]
}