K_Means_Cluster.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8OQfbkF2qf5W"
      },
      "source": [
        " #  <p style=\"text-align: center;\">Technical Support Data Analysis"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1CyfJ0yqf5Z"
      },
      "source": [
        "Technical support data can often be a rich source of information on opportunities for improving customer experience. The lesser the trouble customers have with the product the better. Even better when the customers are able to overcome\n",
        "technical challenge quickly with minimal effort. Let us analyze the tech support data and do some basic analysis on problem types, time to resolve the problem and channel of suppor that is most suitable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ozpdWvkQqf5b"
      },
      "source": [
        "## Loading the Dataset"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-S9w2oBpqf5d",
        "outputId": "96890025-d354-46e2-e60d-337624533679",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "PROBLEM_TYPE              object\n",
              "no_of_cases                int64\n",
              "Avg_pending_calls        float64\n",
              "Avg_resol_time             int64\n",
              "recurrence_freq          float64\n",
              "Replace_percent          float64\n",
              "In_warranty_percent        int64\n",
              "Post_warranty_percent      int64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "import matplotlib.pylab as plt\n",
        "%matplotlib inline\n",
        "\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection  import train_test_split\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "from scipy.stats import zscore\n",
        "\n",
        "#import sklearn.metrics\n",
        "\n",
        "tech_supp_df = pd.read_csv(\"technical_support_data-2.csv\")\n",
        "tech_supp_df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eW88bjuvqf5o"
      },
      "source": [
        "The dataset contains one record for each unique problem type. It has metrics for each type like count, average calls to resolve, average resolution time etc."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "h2uCBL2ANTtN",
        "outputId": "ad364e27-2baa-456e-8daf-36751a42fbc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(23, 8)"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "tech_supp_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DFBxs9Syqf5q",
        "outputId": "38952b86-c96b-4d0d-ae67-3805107cc3a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 PROBLEM_TYPE  no_of_cases  Avg_pending_calls  \\\n",
              "0             Temperature control not working          170                1.3   \n",
              "1            power chord does not tightly fit           12                2.0   \n",
              "2                       Fan swing not working            5                1.0   \n",
              "3                     Main switch does not on            3                2.0   \n",
              "4                  Forgot mobile app password           45                2.3   \n",
              "5                           AC stops abruptly           47                3.1   \n",
              "6            App hangs while entring commands           12                4.0   \n",
              "7         App takes a long time to initialize          165                1.2   \n",
              "8     Unable to connect the App to the device          321                1.0   \n",
              "9        Reinstalling the app hangs the phone           22                3.3   \n",
              "10       Mobile screen blanks on use of phone           23                4.3   \n",
              "11              Cluttered with popup messages          230                1.3   \n",
              "12              AC controller sofware missing          193                1.2   \n",
              "13                   AC remote does not  work           24                2.8   \n",
              "14                     Remote Keys get stuck           450                1.0   \n",
              "15         Unable to operate menu from remote          520                1.0   \n",
              "16               Motor noise from the system           390                1.0   \n",
              "17                     Freshner holder stuck           140                1.7   \n",
              "18               Factory settings not working           72                2.3   \n",
              "19                  WIFI connectivity breaks           290                1.1   \n",
              "20  Battery backup does not get fully charged           29                2.2   \n",
              "21           Battery does not last full 8 hrs           43                2.1   \n",
              "22             Fan speed varies at on its own           78                2.2   \n",
              "\n",
              "    Avg_resol_time  recurrence_freq  Replace_percent  In_warranty_percent  \\\n",
              "0               32             0.04             0.00                   75   \n",
              "1              150             0.01             0.50                    5   \n",
              "2               35             0.02             0.20                   90   \n",
              "3                8             0.01             0.70                    5   \n",
              "4               54             0.15             0.00                   99   \n",
              "5              132             0.30             0.03                  100   \n",
              "6              154             0.02             0.05                  100   \n",
              "7               32             0.03             0.00                   95   \n",
              "8                5             0.21             0.00                   60   \n",
              "9              140             0.14             0.01                   95   \n",
              "10             143             0.21             0.06                  100   \n",
              "11              23             0.02             0.00                   10   \n",
              "12              33             0.03             0.00                  100   \n",
              "13             180             0.04             0.00                  100   \n",
              "14               8             0.25             0.00                  100   \n",
              "15               7             0.28             0.00                  100   \n",
              "16               9             0.27             0.00                    5   \n",
              "17              23             0.05             0.04                   60   \n",
              "18             125             0.02             0.00                  100   \n",
              "19              11             0.22             0.00                   90   \n",
              "20              45             0.35             0.22                   50   \n",
              "21              56             0.31             0.28                   60   \n",
              "22              44             0.19             0.21                   10   \n",
              "\n",
              "    Post_warranty_percent  \n",
              "0                      25  \n",
              "1                      95  \n",
              "2                      10  \n",
              "3                      95  \n",
              "4                       1  \n",
              "5                       0  \n",
              "6                       0  \n",
              "7                       5  \n",
              "8                      40  \n",
              "9                       5  \n",
              "10                      0  \n",
              "11                     90  \n",
              "12                      0  \n",
              "13                      0  \n",
              "14                      0  \n",
              "15                      0  \n",
              "16                     95  \n",
              "17                     40  \n",
              "18                      0  \n",
              "19                     10  \n",
              "20                     50  \n",
              "21                     40  \n",
              "22                     90  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PROBLEM_TYPE</th>\n",
              "      <th>no_of_cases</th>\n",
              "      <th>Avg_pending_calls</th>\n",
              "      <th>Avg_resol_time</th>\n",
              "      <th>recurrence_freq</th>\n",
              "      <th>Replace_percent</th>\n",
              "      <th>In_warranty_percent</th>\n",
              "      <th>Post_warranty_percent</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Temperature control not working</td>\n",
              "      <td>170</td>\n",
              "      <td>1.3</td>\n",
              "      <td>32</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>75</td>\n",
              "      <td>25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>power chord does not tightly fit</td>\n",
              "      <td>12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.50</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan swing not working</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.20</td>\n",
              "      <td>90</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Main switch does not on</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.70</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Forgot mobile app password</td>\n",
              "      <td>45</td>\n",
              "      <td>2.3</td>\n",
              "      <td>54</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.00</td>\n",
              "      <td>99</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>AC stops abruptly</td>\n",
              "      <td>47</td>\n",
              "      <td>3.1</td>\n",
              "      <td>132</td>\n",
              "      <td>0.30</td>\n",
              "      <td>0.03</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>App hangs while entring commands</td>\n",
              "      <td>12</td>\n",
              "      <td>4.0</td>\n",
              "      <td>154</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.05</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>App takes a long time to initialize</td>\n",
              "      <td>165</td>\n",
              "      <td>1.2</td>\n",
              "      <td>32</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>95</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Unable to connect the App to the device</td>\n",
              "      <td>321</td>\n",
              "      <td>1.0</td>\n",
              "      <td>5</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.00</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Reinstalling the app hangs the phone</td>\n",
              "      <td>22</td>\n",
              "      <td>3.3</td>\n",
              "      <td>140</td>\n",
              "      <td>0.14</td>\n",
              "      <td>0.01</td>\n",
              "      <td>95</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Mobile screen blanks on use of phone</td>\n",
              "      <td>23</td>\n",
              "      <td>4.3</td>\n",
              "      <td>143</td>\n",
              "      <td>0.21</td>\n",
              "      <td>0.06</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Cluttered with popup messages</td>\n",
              "      <td>230</td>\n",
              "      <td>1.3</td>\n",
              "      <td>23</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>10</td>\n",
              "      <td>90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>AC controller sofware missing</td>\n",
              "      <td>193</td>\n",
              "      <td>1.2</td>\n",
              "      <td>33</td>\n",
              "      <td>0.03</td>\n",
              "      <td>0.00</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>AC remote does not  work</td>\n",
              "      <td>24</td>\n",
              "      <td>2.8</td>\n",
              "      <td>180</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.00</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Remote Keys get stuck</td>\n",
              "      <td>450</td>\n",
              "      <td>1.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.25</td>\n",
              "      <td>0.00</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Unable to operate menu from remote</td>\n",
              "      <td>520</td>\n",
              "      <td>1.0</td>\n",
              "      <td>7</td>\n",
              "      <td>0.28</td>\n",
              "      <td>0.00</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Motor noise from the system</td>\n",
              "      <td>390</td>\n",
              "      <td>1.0</td>\n",
              "      <td>9</td>\n",
              "      <td>0.27</td>\n",
              "      <td>0.00</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Freshner holder stuck</td>\n",
              "      <td>140</td>\n",
              "      <td>1.7</td>\n",
              "      <td>23</td>\n",
              "      <td>0.05</td>\n",
              "      <td>0.04</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Factory settings not working</td>\n",
              "      <td>72</td>\n",
              "      <td>2.3</td>\n",
              "      <td>125</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.00</td>\n",
              "      <td>100</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>WIFI connectivity breaks</td>\n",
              "      <td>290</td>\n",
              "      <td>1.1</td>\n",
              "      <td>11</td>\n",
              "      <td>0.22</td>\n",
              "      <td>0.00</td>\n",
              "      <td>90</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>Battery backup does not get fully charged</td>\n",
              "      <td>29</td>\n",
              "      <td>2.2</td>\n",
              "      <td>45</td>\n",
              "      <td>0.35</td>\n",
              "      <td>0.22</td>\n",
              "      <td>50</td>\n",
              "      <td>50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>Battery does not last full 8 hrs</td>\n",
              "      <td>43</td>\n",
              "      <td>2.1</td>\n",
              "      <td>56</td>\n",
              "      <td>0.31</td>\n",
              "      <td>0.28</td>\n",
              "      <td>60</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Fan speed varies at on its own</td>\n",
              "      <td>78</td>\n",
              "      <td>2.2</td>\n",
              "      <td>44</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.21</td>\n",
              "      <td>10</td>\n",
              "      <td>90</td>\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",
              "          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": [
        "tech_supp_df\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xovoozXUNTtQ",
        "outputId": "4299f8b0-7d96-4208-d73b-11c2eaa3802c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f262ad59c50>"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1260x1260 with 56 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "techSuppAttr=tech_supp_df.iloc[:,1:]\n",
        "techSuppScaled=techSuppAttr.apply(zscore)\n",
        "sns.pairplot(techSuppScaled,diag_kind='kde')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dbn46iRuqf5v"
      },
      "source": [
        "## Group Data into similar clusters\n",
        "\n",
        "Now, we will use K-Means clustering to group data based on their attribute. First, we need to determine the optimal number of groups. For that we conduct the knee test to see where the knee happens."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GJ_z7Buwqf5x",
        "outputId": "3a2fff34-0193-47e9-b09f-3517cc57283f",
        "scrolled": true,
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 312
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'Selecting k with the Elbow Method')"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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S//FmZnlzgmgiEgwaBMsum5qaFi3KOyIzs7o5QTShddaB66+Hp56Ca67JOxozs7o5QTSxQw6BPn3gvPPg5ZfzjsbMrHZOEE1MgoEDYaWVUr/EwoV5R2RmVjMniBysuWaaOPfcc3DllXlHY2ZWMyeInBx0EBx4IFxwAUxtVqtMmVlr4QSREwn+8AdYZRU48kj48su8IzIz+yYniBxVVMBNN8HEiakSnZlZOXGCyNmPfgT9+sHFF8PkyXlHY2a2mBNEGbj+elhjjdTU9MUXeUdjZpY4QZSBNdZIQ1+nTIHLLss7GjOzxAmiTPTpA4cdlhLEpEl5R2Nm5gRRVq69Ns2ROPxw+PzzvKMxs9bOCaKMrLZaqkD34otw0UV5R2NmrZ0TRJnZZx+orEzDXp97bvH2MWPg8svzi8vMWp+SJQhJXSSNkfSSpBclnVzDMZJ0naRXJf1L0rYF+46Q9J/sdkSp4ixH55+fJtIdeCB89llKDgcdBN265R2ZmbUm7eo7QFIP4EJg/ex4ARERG9Zz6kLg9IiYJKkDMFHS6Ih4qeCY3sDG2W174CZge0mrAxcAlUBk5z4QER806N01U/vvD7/5DZx1FvToAW++CffcAz175h2ZmbUmxVxBDAauBnYGupH+aNf7XTYiZkfEpOz+fGAasG61w/oAd0TyDLCqpLWBvYHREfF+lhRGA72KfE8twplnwk47pRFNW2zh5GBmTa+YBPFRRIyMiDkR8V7VrSEvIqkrsA3wbLVd6wIzCh7PzLbVtr2m5+4vaYKkCXPnzm1IWGVtzJhUmnTLLeGxx+AXv8g7IjNrbYpJEGMkXSFpR0nbVt2KfQFJKwHDgVMiYt4SR1qLiBgUEZURUVlRUdHYT5+Lqj6He+5J6zR16wZXXQWXXpp3ZGbWmtTbB0HqG4DUtFQlgN3rO1FSe1JyGBoR99VwyCygS8Hjztm2WcBu1bY/VkSsLcL48d/sc3j00TSy6aKL0rYePfKNz8xaB0VEaZ5YEnA78H5EnFLLMfsCJwL7kBLRdRHRPeuknghUXalMAraLiPfres3KysqYMGFCY72FsjJ3bkoM774Ljz8Om2+ed0Rm1hJImhgRlTXtq7eJSdIqkq6uaueXdJWkVYp43R7AYcDukiZnt30kDZA0IDvmIWA68CpwC3A8QJYILgHGZ7eL60sOLV1FBTzyCCy7LPTqBTNm1H+OmdnSqPcKQtJwYCrpagDSH/2tIqJviWNrsJZ8BVFlyhTYZRfo3DldSay+et4RmVlztlRXEMBGEXFBREzPbhcB9c2BsBLZaiu4/3549dU0X+LTT/OOyMxaqmISxAJJO1c9yCbOLShdSFaf3XaDoUPh6afh4INh4cK8IzKzlqiYBHEccKOk1yW9AdwADKjnHCuxAw+EG26Av/0NBgyAEo01MLNWrN5hrhExGdhK0srZ40afy2BL5vjjYfbsND9i7bXhkkvyjsjMWpJaE4SkQyPiT5JOq7YdgIi4usSxWREuvhjefntxkjj++LwjMrOWoq4riBWznx1q2OcGjTIhwU03wZw5cOKJ0KlTan4yM1tatSaIiBiY3f1HRDxZuC/rqLYy0a4dDBsGe+4JhxwCHTumjmwzs6VRTCf19UVusxytsELqsP7Wt1J96ylT8o7IzJq7uvogdgR2Aiqq9UOsDLQtdWDWcKuvDg8/nJYJ79UrDYPt2jXvqMysuarrCmIZYCVSEulQcJsHuJW7THXpkpLEZ5/B3nunNZzMzJZEXX0QYyU9AXw3mz1tzcTmm8Pf/w7f/z7su29aDXallfKOysyamzr7ICJiEbBOE8VijahHD/jzn1M9iQMPhC+/zDsiM2tuiumknizpAUmHSepbdSt5ZLbUDjgABg5Mq8Aecwx89VXeEZlZc1JMwaDlgPf4ZoGgAGoqAGRl5thj00S6X/0K1loLLr8874jMrLkoZqmNo5oiECud885LSeKKK9Js61NPzTsiM2sOiikY1FnSCElzsttwSZ2bIjhrHBJce23qizjtNLjrrrwjMrPmoJg+iNuAB0id1esAf8u2WTPSti3ceSfsuisceSSMHp13RGZW7opJEBURcVtELMxuQ4CKEsdlJbDccqnY0KabQt++aYSTmVltikkQ70k6VFLb7HYoqdPamqFVVoGRI2GNNaB3b/jPf/KOyMzKVTEJ4mjgIOBtYDZpFvWR9Z0k6dasz2JqLfvPkDQ5u02VtEjS6tm+1yW9kO1r2UWmc7DOOmno61dfpdnWb7+dd0RmVo6KSRCdI+KAiKiIiE4R8QNgvSLOGwL0qm1nRFwREVtHxNbAOcDYiHi/4JCe2f4ai2nb0vnOd+DBB+Gdd9KVxDyXgTKzakq2mmtEjAPer++4TD9gWJHHWiPZfnu4916YOhV++EP4/PO8IzKzcpL7aq6SViBdaZxYsDmAUZICGBgRg+o4vz/QH2C99Yq5sLFCvXvD4MFwxBFw+OGprkSbYr42mFmLV9dEueqruVZp7NVc9weerNa8tHNEzJLUCRgt6eXsiuR/ZMljEEBlZaUr3S2Bww9PTU1nnplmW19zTZo7YWatW52ruQJjJQ2JiDcAJLUBVoqIxmyxPphqzUsRMSv7OUfSCKA7UGOCsMbxi1/A7Nnw+9+n2dZnn513RGaWt2IaE34jaWVJKwJTgZckndEYLy5pFWBX4P6CbStK6lB1H9gre10rIQmuvBJ++lM45xwYMiTviMwsb8Us1rdZRMyTdAgwEjgbmAhcUddJkoYBuwEdJc0ELgDaA0TEzdlhPwRGRcQnBaeuCYxQauNoB9wVEQ8X/Y5sibVpA7fdlooMHXssVFSkehJm1joVkyDaS2oP/AC4ISK+zDqP6xQR/Yo4ZghpOGzhtunAVkXEZSWwzDIwfDj07Ak//nEqNrTDDnlHZWZ5KKaJaSDwOrAiME7S+qSOamuhOnSAhx5KE+r23RemTcs7IjPLQ70JIiKui4h1I2KfSN4AejZBbJajTp3SbOt27dJs61mz8o7IzJpaXfMgDo2IP1WbA1Ho6hLFZGVio43Suk277gq9esG4cbDaanlHZWZNpa4riBWznx1quVkrsO22MGIE/Pvf0KcPLFiQd0Rm1lTqmgcxMPt5UdOFY+Xo+9+HO+6Afv1gr73gscdSfQmAMWNg/Pg0yc7MWpa6mpiuq+vEiPi/xg/HytXBB8OTT8INN8APfgAPPJASxUEHwT335B2dmZVCXcNcq8rJ9AA2A/6cPf4x8FIpg7LydP318N57ab2mHXaA6dNTcujpIQtmLVJdTUy3A0g6jrQ20sLs8c3A400TnpWboUPhtdfguefSMNj11887IjMrlWLmQaxGWsG1ykrZNmuFHnssXTkceGBau2mLLdIVhZm1PMUkiN8Cz0saIul2YBLw69KGZeVozJjFfQ5/+Uu6mvjyy7R+09FHwyef1P8cZtZ8FDNR7jZge2AEcB+wY1Xzk7Uu48d/s8+hX780T2L33dPiftttB5Mn5xqimTUiRbScEgqVlZUxYYJLWOfh0Ufh0ENTJ/ZVV8EJJ7imhFlzIGlibaWdXTvMGsXuu8OUKbDnnnDSSWko7Hvv5R2VmS0NJwhrNBUV8Le/paJDI0fCVlvB2LF5R2VmS6qoBCFpZ0lHZfcrJG1Q2rCsuZLglFPgmWdghRXSlcWFF8LChXlHZmYNVW+CkHQBcBZwTrapPfCnUgZlzd+228LEialf4qKLUqKYMSPvqMysIYq5gvghcADwCUBEvIUX67MidOgAt98Od94Jzz+fmpzuv7/+88ysPBSTIL6INNQp4Os60WZFO/RQmDQJNtwwdV6feCJ89lneUZlZfYpJEPdIGgisKulnwD+AW0oblrU0G28MTz0Fp50GN94I22/vSnVm5a6YiXJXAvcCw4HvAOdHxPX1nSfpVklzJE2tZf9ukj6SNDm7nV+wr5ekf0t6VdLZxb8dK2fLLJPmSDz4ILz1FlRWwuDB0IKm4pi1KEWNYoqI0RFxRkT8IiJGF/ncQ4Be9RzzeERsnd0uBpDUFrgR6E1aRbafpM2KfE1rBvbZJ82Z2GEHOPbYNCP7o4/yjsrMqitmFNN8SfOq3WZIGiFpw9rOi4hxwPtLEFN34NWImB4RXwB3A32W4HmsjK2zDowaBb/+Ndx7L2yzTVoh1szKRzFXENcAZwDrAp2BXwB3kf5w37qUr7+jpCmSRkraPNu2LlA4IHJmtq1GkvpLmiBpwty5c5cyHGtKbdvCOefA44/DV19Bjx5w+eXpvpnlr5gEcUBEDIyI+RExLyIGAXtHxJ9ZumW/JwHrR8RWwPXAX5fkSSJiUERURkRlRUXFUoRjedlxx7TI3w9/CGedBb16wdtv5x2VmRWTID6VdJCkNtntIKBqkOISdy9myebj7P5DQHtJHYFZQJeCQztn26wFW3VV+POfYeDAdEWx1VapCcrM8lNMgjgEOAyYA7yT3T9U0vLAiUv6wpLWktJ6n5K6Z7G8B4wHNpa0gaRlgIOBB5b0daz5kKB/f5gwATp1gr33hjPPhC++yDsys9aprprUAETEdGD/WnY/Udt5koYBuwEdJc0ELiAt00FE3AwcCBwnaSGwADg4m5C3UNKJwCNAW+DWiHix6Hdkzd7mm6cO69NOgyuuSFXs7r47TbQzs6ZTbz0IScsBxwCbA8tVbY+Io0sbWsO5HkTLM3x4Ggq7aFFqfurXL++IzFqWpa0HcSewFrA3MJbUJzC/8cIzq92PfpQ6sL/7XZc2NWtqxSSIb0XEr4BPslKj+5JKkJo1ifXXT81Mv/ylS5uaNaViEsSX2c8PJW0BrAJ0Kl1IZv+rXTu45BL45z9h/vy0ltMNN3iZDrNSKiZBDJK0GvBL0miil4DflTQqs1r07JmW6dhrL5c2NSu1OhOEpDbAvIj4ICLGRcSGEdEpIgY2UXxm/6NjR3jgAbjmGnj44TS66ZprvnnMmDFpVraZLbk6E0REfAWc2USxmBVNgpNPhqefToWJTj0VjjgilTYdMwYOOgi6dcs7SrPmrd55EMA/JP0C+DNZVTmAiFiShfjMGtW226a6EgceCHfcka4oFiyAESNSc5SZLbli+iB+ApwAjAMmZjdPNrCy0aEDPPJIGhI7Z07qxD7pJBg6NF1RmNmSKaZg0AY13Dyn1crKmDEwdiycd15KGAsWpFKnm24Kt94KX35Z/3OY2TcVUw9iBUm/lDQoe7yxpP1KH5pZcar6HO65By69FO6/Hz7+GC66CFZeGY45JpU8vflm+PzzvKM1az6KaWK6DfgC2Cl7PAu4tGQRmTXQ+PEpOVT1OfTsmR4vt1xa+O/BB2HtteG449KIp2uvhU8/zTdms+agmLWYJkREpaTnI2KbbNuUrI5DWfFaTFabCHj00TTZbuzYtFrs6aenpNGhQ97RmeVnaddi+iJb2juyJ9sI8IW6NSsS7LFHWrJj3DjYeutUnKhr15Q0Pvww7wjNyk8xCeJC4GGgi6ShwD/x3Ahrxr73vTTq6dlnU5nT889P6z398pfw7rt5R2dWPooZxTQK6AscCQwDKiPisdKGZVZ63bunGdnPP5+W7vj1r9MVxRlnuOSpGRQ3iulvwF7AYxHx94jwdyxrUbbeGv7yF5g6Na3tdPXVsMEG8H//BzNn5h2dWX6KaWK6Evge8JKkeyUdmBURMmtRNtsM/vQnePnlVHvippvSqKef/xz++9+8ozNresU0MY2NiOOBDYGBwEGk+tRmLdLGG8PgwfCf/6Q5FEOGpG1HHQWvvJJ3dGZNp5grCLJRTD8CBgDdgNtLGZRZOejaNV1FTJ8OJ56Y6mJvumm6upg6Ne/ozEqvmD6Ie4BpwO7ADcBGEXFSEefdKmmOpBr/K0k6RNK/JL0g6SlJWxXsez3bPlmSJzZYrtZdNy0n/vrr8ItfpI7tLbdMaz89/3ze0ZmVTjFXEINJSWFARIwBdpJ0YxHnDQF61bH/v8CuEbElcAkwqNr+nhGxdW0TOMya2pprwu9+B2+8Ab/6Vaput+22sN9+acisWUtTTB/EI8B3JV0u6XXSH/OXizhvHFDrkuAR8VREfJA9fAboXFTEZjlbYw24+OJ0RXHppakmxQ47wJ57pkl4Zi1FrQlC0rclXSDpZeB6YAZpaaU9m1oAABAfSURBVI6eEXF9I8dxDDCy4HEAoyRNlNS/kV/LrFGsumpaPfaNN1L1un/9C3bdFXbZBUaPTlcbY8Z88xxXurPmpK4riJdJ/Q77RcTOWVJY1NgBSOpJShBnFWzeOSK2BXoDJ0japY7z+0uaIGnC3LlzGzs8s3qttFKaXPf662khwOnT08S7IUPSvIpHH03HudKdNTd1JYi+wGxgjKRbJO0BqDFfXNJ3gT8CfSLi69LzETEr+zkHGAF0r+05ImJQRFRGRGVFRUVjhmfWIMsvnybXvfZaWlr8s89g3ryULH7848VLkrvSnTUXtSaIiPhrRBwMbAKMAU4BOkm6SdJeS/vCktYD7gMOi4hXCravKKlD1X3SLG4PKrRmY9ll0+S6V16B226DVVaBe+9N+956y1XurPkoppP6k4i4KyL2J3UkP883m4NqJGkY8DTwHUkzJR0jaYCkAdkh5wNrAH+oNpx1TeAJSVOA54AHI+Lhhr81s3y1b58WAWzTBvr2hQ8+SFXuNtkkTcT74ou8IzSrW731IJoT14OwclJY6a5nzzQstm9fWGutdHXRpUtacvzoo1PzlFkelrYehJktgeqV7vbYA/7615QQRo6E9dZLM7Q33BCuuiqVSTUrJ76CMMtJRJo3ceml8I9/pPkVp54KJ5yQhtCaNQVfQZiVISnNmxg9Ok2223HHVLTIxYusXDhBmJWBHXaAv/0NJk1aXLxo/fXT2k+zZ+cdnbVWThBmZWSbbRYXL+rbF37/+1S86MQT4c03847OWhsnCLMytNlmcOedabTT4YfDoEGw0UZw7LHw6qt5R2ethROEWRnbaKOUHF57DY47DoYOhe98Bw45BF58Me/orKVzgjBrBrp0geuuS6VPTz8d7r8fttgi1aSYNCnv6KylcoIwa0bWWiutBltYk2K77WDffdNIKLPG5ARh1gxV1aR44w247DJ47jnYaac0GW/MmDTHwmxpOUGYNWOrrALnnpuWGr/6apg2DXbfHXbeOc3WdqKwpeEEYdYCrLhimoU9fTr84Q8wcybssw9UVsKIEfDVV3lHaM2RE4RZC7Lccmm006uvwq23pnoUffvCd78Ld93lpcatYZwgzFqg9u3hqKPg5ZdTYpDS0NhNN03Fi0aN+ubxLoVqNXGCMGvB2raFfv1gypTU1FRVvKh3bzjllFT1zqVQrTZOEGatQJs2qT72+PGp83rTTVP97JVXhl690mztb30r7yit3DhBmLUiUkoIL7yQksKXX6ZiRVdfnepTbLllKmL02GNpn7VuThBmrdBjj8FDD6XJdu3bpw7tK6+ETp3SAoE9e0LHjmmm9uDBqZa2tT7t8g7AzJpW9VKoPXsufnz66TB/fpqh/dBDqTnqvvvSeVttlYbO9u6dale081+PFs8V5cxamcsvTx3SVaVQISWN8ePhzDO/eWxEWnp85MiUMJ54AhYtShXv9torJYtevdISINY81VVRrqQJQtKtwH7AnIjYoob9Aq4F9gE+BY6MiEnZviOAX2aHXhoRt9f3ek4QZqX10UepPGrV1UVVMaNtt118dbH99mn0lDUPeSaIXYCPgTtqSRD7ACeREsT2wLURsb2k1YEJQCUQwERgu4j4oK7Xc4IwazoRafhsVbJ46qk0Y3v11dPVxT77wN57p34NK191JYiStiJGxDhJXes4pA8peQTwjKRVJa0N7AaMjoj3ASSNBnoBw0oZr5kVT4Ktt063c8+FDz5IE/BGjky3u+9Ox1RWLr66qKz01UVzkvcopnWBGQWPZ2bbatv+PyT1lzRB0oS5c+eWLFAzq9tqq8FPfgJDhqSmpwkT4KKLUkK4+OJUd3utteCww9Ls7vfeyztiq0/eCWKpRcSgiKiMiMqKioq8wzEz0sS87bZLw2iffhrmzEnV8Hr1gocfTst+dOqURkNdcklKJr/7XeosL+QlQPKVd4KYBXQpeNw521bbdjNrhjp2hJ/+NNXZfvtteOaZlDwWLYLzz0+jqn7729QMdf758P77XgKkHOSdIB4ADleyA/BRRMwGHgH2krSapNWAvbJtZtbMtW2bRjpdeGEqdPTOO3DHHenqon37dEWxxhqpo3uPPdKM7k8/zTvq1qnUo5iGkTqcOwLvABcA7QEi4uZsmOsNpA7oT4GjImJCdu7RwLnZU10WEbfV93oexWTWvC1cCD/7WerH6NIl9WUsXAjLLJOao/bYI926dUvJxJZebsNcm5oThFnzVtWsdNxxcNNNcPvtaSTUP/+ZbpMnp+M6dIBddknJ4vvfhy22SMdZw+U2zNXMrFh1LQFy5ZXpmHffTcdVJYwHH0zbO3VKpVarrjA22CC/99GS+ArCzMpCQ5YAqfLmm4uTxT//mTrAISWIqmSx++6erFcXNzGZWYsXAdOmpUTxj3+kFWvnzUv7ttxyccLYddfURGWJE4SZtToLF8LEiYuvLp58Ej7/fPEoqqqEscMOsOyyeUebHycIM2v1FixI60VVJYwJE9LaUcsvD9/73uKEsfXWrWs5ECcIM7NqPvwQxo5dnDBeeiltX2211A9SlTD++lfo3r1hfSPNiUcxmZlVs+qq0KdPukGac/Hoo4v7MKoKJXXsmIoonXoqnHxy6ueoGl3V0vkKwsysmgh49dXFVxejRi3u8G7fPiWKc89NVxvNXV1XEHkvtWFmVnYk2HhjGDAA/vKXtJT5z36W9q20UpqXsdZa8MMfpv0LFuQbb6k4QZiZ1WPsWBgxIi0w2LYt/OEPcPzx8OyzqbmpUyc4/PC0Uu2XX+YdbeNxgjAzq0PhDO+LL04/zz8fDjgAZsxITVA/+Qk88EBajXbddeGEE9Kw2q++yjv6peMEYWZWh/HjFy//AennPfek7W3bppnaf/xjWpV2xIi0/9ZbYeedYcMN4Zxz4IUX8n0PS8qd1GZmjWz+/DQ89q67YPToVPdi881TTYx+/cprrSh3UpuZNaEOHVJp1ZEj0/DZG29Mw2rPOy9dVey0E9xwQ7rqKGdOEGZmJVRRkTq0n3gCXn89Vc775BM46aTUX7H33mlZ86phtOXECcLMrImsvz6cdRZMmZL6Jc46C/7zHzjyyDQS6sc/ThP0Pvss70gTJwgzsxxssQVcdhm89ho8/TT07w/jxsGPfgRrrglHH51mdC9alF+MThBmZjmS0oqy110Hs2alWdt9+8K998Kee6ZmqJNPTnMumnpMkROEmVmZaNcuJYXbbksd2PfeCz16wMCBKYl861tpst5LL6UCS2PGfPP8MWPS9sZS0gQhqZekf0t6VdLZNez/vaTJ2e0VSR8W7FtUsO+BUsZpZlZull8+NTcNH56SxW23wUYbwa9/nYbMDhoE++8Pd9+djq+a0NetW+PFULJ5EJLaAq8AewIzgfFAv4h4qZbjTwK2iYijs8cfR8RKDXlNz4Mws5bu7bfT+k933QXPPJO2rbcefPrpNyf0FSuveRDdgVcjYnpEfAHcDfSp4/h+wLASxmNm1uyttVYaIvv006mDe7fdUm3u445reHKoTykTxLrAjILHM7Nt/0PS+sAGwKMFm5eTNEHSM5J+UNuLSOqfHTdh7ty5jRG3mVmz8MYbMHVq6pe46ab/7ZNYWuXSSX0wcG9EFA7oWj+77PkpcI2kjWo6MSIGRURlRFRWVFQ0RaxmZrmraRHBgw5q3CRRygQxC+hS8Lhztq0mB1OteSkiZmU/pwOPAds0fohmZs1TXYsINpZSdlK3I3VS70FKDOOBn0bEi9WO2wR4GNggsmAkrQZ8GhGfS+oIPA30qa2Du4o7qc3MGiaXmtQRsVDSicAjQFvg1oh4UdLFwISIqBq6ejBwd3wzU20KDJT0Fekq57f1JQczM2tcXu7bzKwV83LfZmbWYE4QZmZWIycIMzOrUYvqg5A0F3hjCU/vCLzbiOE0FsfVMI6rYRxXw7TEuNaPiBonkbWoBLE0JE2oraMmT46rYRxXwziuhmltcbmJyczMauQEYWZmNXKCWGxQ3gHUwnE1jONqGMfVMK0qLvdBmJlZjXwFYWZmNXKCMDOzGrX6BCHpVklzJE3NO5YqkrpIGiPpJUkvSjo575gAJC0n6TlJU7K4Lso7pkKS2kp6XtLf846lkKTXJb2Q1Vcvm8XCJK0q6V5JL0uaJmnHMojpOwW16CdLmifplLzjApB0avbvfqqkYZKWyzsmAEknZzG92NifVavvg5C0C/AxcEdEbJF3PACS1gbWjohJkjoAE4Ef5L2irSQBK0bEx5LaA08AJ0fEM3nGVUXSaUAlsHJE7Jd3PFUkvQ5URkRZTbCSdDvweET8UdIywAoR8WHecVXJ6trPAraPiCWdANtYsaxL+ve+WUQskHQP8FBEDMk5ri1I5Zy7A1+QSicMiIhXG+P5W/0VRESMA97PO45CETE7IiZl9+cD06ilXGtTiuTj7GH77FYW3zAkdQb2Bf6YdyzNgaRVgF2AwQAR8UU5JYfMHsBreSeHAu2A5bNaNysAb+UcD6TSCM9GxKcRsRAYC/RtrCdv9Qmi3EnqSqqm92y+kSRZM85kYA4wOiLKIi7gGuBM4Ku8A6lBAKMkTZTUP+9gMhsAc4Hbsma5P0paMe+gqvmfSpN5ySpcXgm8CcwGPoqIUflGBcBU4HuS1pC0ArAP36zkuVScIMqYpJWA4cApETEv73gAImJRRGxNKiHbPbvEzZWk/YA5ETEx71hqsXNEbAv0Bk7ImjXz1g7YFrgpIrYBPgHOzjekxbImrwOAv+QdC3xd5bIPKbGuA6wo6dB8o4KImAb8DhhFal6aDCxqrOd3gihTWRv/cGBoRNyXdzzVZc0RY4BeeccC9AAOyNr67wZ2l/SnfENarKC++hxgBKm9OG8zgZkFV4D3khJGuegNTIqId/IOJPN94L8RMTcivgTuA3bKOSYAImJwRGwXEbsAH5BKPTcKJ4gylHUGDwamRcTVecdTRVKFpFWz+8sDewIv5xsVRMQ5EdE5IrqSmiUejYjcv90BSFoxG2hA1oSzF6lZIFcR8TYwQ9J3sk17AOVU1rcfZdK8lHkT2EHSCtn/zz1IfYO5k9Qp+7keqf/hrsZ67pLVpG4uJA0DdgM6SpoJXBARg/ONih7AYcALWXs/wLkR8VCOMQGsDdyejS5pA9wTEWU1pLQMrQmMSH9TaAfcFREP5xvS104ChmbNOdOBo3KOB/g6ke4J/DzvWKpExLOS7gUmAQuB5ymfZTeGS1oD+BI4oTEHG7T6Ya5mZlYzNzGZmVmNnCDMzKxGThBmZlYjJwgzM6uRE4SZmdXICcKshCR1LaeVgs0awgnCzMxq5ARh1kQkbZgtjNct71jMitHqZ1KbNYVsSYu7gSMjYkre8ZgVwwnCrPQqgPuBvnkXfTJrCDcxmZXeR6TF3nbOOxCzhvAVhFnpfQH8EHhE0scR0WirbZqVkhOEWROIiE+ywkajsyTxQN4xmdXHq7mamVmN3AdhZmY1coIwM7MaOUGYmVmNnCDMzKxGThBmZlYjJwgzM6uRE4SZmdXo/wGXOw8/u+LYQAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "#Finding optimal no. of clusters\n",
        "from scipy.spatial.distance import cdist #compute distance between each of points\n",
        "clusters=range(1,10)\n",
        "meanDistortions=[]\n",
        "\n",
        "for k in clusters:\n",
        "    model=KMeans(n_clusters=k)\n",
        "    model.fit(techSuppScaled)\n",
        "    prediction=model.predict(techSuppScaled)\n",
        "    meanDistortions.append(sum(np.min(cdist(techSuppScaled, model.cluster_centers_, 'euclidean'), axis=1)) / techSuppScaled.shape[0])\n",
        "\n",
        "\n",
        "plt.plot(clusters, meanDistortions, 'bx-')\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('Average distortion')\n",
        "plt.title('Selecting k with the Elbow Method')\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LeWqzIRNqf54"
      },
      "source": [
        "Though the bend is not coming out clearly as there are many bends, let us look at 3 clusters and 5 clusters"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "EsP8_LBKqf56",
        "outputId": "1a8bd8e4-b729-46c8-dc27-3755c010ad56"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Groups Assigned : \n",
            "\n"
          ]
        },
        {
          "data": {
            "text/html": [
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              "\n",
              "    .dataframe tbody tr th {\n",
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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>PROBLEM_TYPE</th>\n",
              "      <th>no_of_cases</th>\n",
              "      <th>Avg_pending_calls</th>\n",
              "      <th>Avg_resol_time</th>\n",
              "      <th>recurrence_freq</th>\n",
              "      <th>Replace_percent</th>\n",
              "      <th>In_warranty_percent</th>\n",
              "      <th>Post_warranty_percent</th>\n",
              "      <th>GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Temperature control not working</td>\n",
              "      <td>170</td>\n",
              "      <td>1.3</td>\n",
              "      <td>32</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75</td>\n",
              "      <td>25</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>power chord does not tightly fit</td>\n",
              "      <td>12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.5</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan swing not working</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.2</td>\n",
              "      <td>90</td>\n",
              "      <td>10</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Main switch does not on</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.7</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Forgot mobile app password</td>\n",
              "      <td>45</td>\n",
              "      <td>2.3</td>\n",
              "      <td>54</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>99</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       PROBLEM_TYPE  no_of_cases  Avg_pending_calls  \\\n",
              "0   Temperature control not working          170                1.3   \n",
              "1  power chord does not tightly fit           12                2.0   \n",
              "2             Fan swing not working            5                1.0   \n",
              "3           Main switch does not on            3                2.0   \n",
              "4        Forgot mobile app password           45                2.3   \n",
              "\n",
              "   Avg_resol_time  recurrence_freq  Replace_percent  In_warranty_percent  \\\n",
              "0              32             0.04              0.0                   75   \n",
              "1             150             0.01              0.5                    5   \n",
              "2              35             0.02              0.2                   90   \n",
              "3               8             0.01              0.7                    5   \n",
              "4              54             0.15              0.0                   99   \n",
              "\n",
              "   Post_warranty_percent  GROUP  \n",
              "0                     25      0  \n",
              "1                     95      2  \n",
              "2                     10      0  \n",
              "3                     95      2  \n",
              "4                      1      1  "
            ]
          },
          "execution_count": 23,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Let us first start with K = 3\n",
        "final_model=KMeans(3)\n",
        "final_model.fit(techSuppScaled)\n",
        "prediction=final_model.predict(techSuppScaled)\n",
        "\n",
        "#Append the prediction \n",
        "tech_supp_df[\"GROUP\"] = prediction\n",
        "techSuppScaled[\"GROUP\"] = prediction\n",
        "print(\"Groups Assigned : \\n\")\n",
        "tech_supp_df.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ck4XGXSjqf6C"
      },
      "source": [
        "### Analyze the distribution of the data among the two groups (K = 3). One of the most informative visual tool is boxplot.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dch9BWddNTtV",
        "outputId": "45b28ad2-c8e3-439d-8fa8-ed86b0f2f951"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>no_of_cases</th>\n",
              "      <th>Avg_pending_calls</th>\n",
              "      <th>Avg_resol_time</th>\n",
              "      <th>recurrence_freq</th>\n",
              "      <th>Replace_percent</th>\n",
              "      <th>In_warranty_percent</th>\n",
              "      <th>Post_warranty_percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GROUP</th>\n",
              "      <th></th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>250.444444</td>\n",
              "      <td>1.166667</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35.000000</td>\n",
              "      <td>3.157143</td>\n",
              "      <td>132.571429</td>\n",
              "      <td>0.125714</td>\n",
              "      <td>0.021429</td>\n",
              "      <td>99.142857</td>\n",
              "      <td>0.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>112.142857</td>\n",
              "      <td>1.828571</td>\n",
              "      <td>47.857143</td>\n",
              "      <td>0.165714</td>\n",
              "      <td>0.272857</td>\n",
              "      <td>20.714286</td>\n",
              "      <td>79.285714</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       no_of_cases  Avg_pending_calls  Avg_resol_time  recurrence_freq  \\\n",
              "GROUP                                                                    \n",
              "0       250.444444           1.166667       20.666667         0.125556   \n",
              "1        35.000000           3.157143      132.571429         0.125714   \n",
              "2       112.142857           1.828571       47.857143         0.165714   \n",
              "\n",
              "       Replace_percent  In_warranty_percent  Post_warranty_percent  \n",
              "GROUP                                                               \n",
              "0             0.026667            85.555556              14.444444  \n",
              "1             0.021429            99.142857               0.857143  \n",
              "2             0.272857            20.714286              79.285714  "
            ]
          },
          "execution_count": 25,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "techSuppClust = tech_supp_df.groupby(['GROUP'])\n",
        "techSuppClust.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "QzlZMEEaqf6E",
        "outputId": "920d7ccb-5c96-4c35-e601-94e64e55e1da"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a23c7bf60>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2431fa90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2428ea58>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a242b9a20>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a243c6a20>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a243f1cf8>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24417fd0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24447320>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "techSuppScaled.boxplot(by='GROUP', layout = (2,4),figsize=(15,10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CqpJwcybqf6M"
      },
      "outputs": [],
      "source": [
        "#  Let us next try with K = 5, the next elbow point"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HsmQzYLzqf6T",
        "outputId": "d8faa25b-43a2-4783-db03-159c30f90cbd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Groups Assigned : \n",
            "\n"
          ]
        },
        {
          "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>PROBLEM_TYPE</th>\n",
              "      <th>no_of_cases</th>\n",
              "      <th>Avg_pending_calls</th>\n",
              "      <th>Avg_resol_time</th>\n",
              "      <th>recurrence_freq</th>\n",
              "      <th>Replace_percent</th>\n",
              "      <th>In_warranty_percent</th>\n",
              "      <th>Post_warranty_percent</th>\n",
              "      <th>GROUP</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Temperature control not working</td>\n",
              "      <td>170</td>\n",
              "      <td>1.3</td>\n",
              "      <td>32</td>\n",
              "      <td>0.04</td>\n",
              "      <td>0.0</td>\n",
              "      <td>75</td>\n",
              "      <td>25</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>power chord does not tightly fit</td>\n",
              "      <td>12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>150</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.5</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fan swing not working</td>\n",
              "      <td>5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>35</td>\n",
              "      <td>0.02</td>\n",
              "      <td>0.2</td>\n",
              "      <td>90</td>\n",
              "      <td>10</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Main switch does not on</td>\n",
              "      <td>3</td>\n",
              "      <td>2.0</td>\n",
              "      <td>8</td>\n",
              "      <td>0.01</td>\n",
              "      <td>0.7</td>\n",
              "      <td>5</td>\n",
              "      <td>95</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Forgot mobile app password</td>\n",
              "      <td>45</td>\n",
              "      <td>2.3</td>\n",
              "      <td>54</td>\n",
              "      <td>0.15</td>\n",
              "      <td>0.0</td>\n",
              "      <td>99</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                       PROBLEM_TYPE  no_of_cases  Avg_pending_calls  \\\n",
              "0   Temperature control not working          170                1.3   \n",
              "1  power chord does not tightly fit           12                2.0   \n",
              "2             Fan swing not working            5                1.0   \n",
              "3           Main switch does not on            3                2.0   \n",
              "4        Forgot mobile app password           45                2.3   \n",
              "\n",
              "   Avg_resol_time  recurrence_freq  Replace_percent  In_warranty_percent  \\\n",
              "0              32             0.04              0.0                   75   \n",
              "1             150             0.01              0.5                    5   \n",
              "2              35             0.02              0.2                   90   \n",
              "3               8             0.01              0.7                    5   \n",
              "4              54             0.15              0.0                   99   \n",
              "\n",
              "   Post_warranty_percent  GROUP  \n",
              "0                     25      4  \n",
              "1                     95      0  \n",
              "2                     10      4  \n",
              "3                     95      0  \n",
              "4                      1      1  "
            ]
          },
          "execution_count": 27,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Let us first start with K = 5\n",
        "final_model=KMeans(5)\n",
        "final_model.fit(techSuppScaled)\n",
        "prediction=final_model.predict(techSuppScaled)\n",
        "\n",
        "#Append the prediction \n",
        "tech_supp_df[\"GROUP\"] = prediction\n",
        "techSuppScaled[\"GROUP\"] = prediction\n",
        "print(\"Groups Assigned : \\n\")\n",
        "tech_supp_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "4bqP4PRJNTtX",
        "outputId": "8bd997da-bbf6-403d-bf37-201b8f005585"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>no_of_cases</th>\n",
              "      <th>Avg_pending_calls</th>\n",
              "      <th>Avg_resol_time</th>\n",
              "      <th>recurrence_freq</th>\n",
              "      <th>Replace_percent</th>\n",
              "      <th>In_warranty_percent</th>\n",
              "      <th>Post_warranty_percent</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GROUP</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.50</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>79.000000</td>\n",
              "      <td>0.010000</td>\n",
              "      <td>0.600000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>95.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>35.00</td>\n",
              "      <td>3.157143</td>\n",
              "      <td>132.571429</td>\n",
              "      <td>0.125714</td>\n",
              "      <td>0.021429</td>\n",
              "      <td>99.142857</td>\n",
              "      <td>0.857143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>395.25</td>\n",
              "      <td>1.025000</td>\n",
              "      <td>7.750000</td>\n",
              "      <td>0.240000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>87.500000</td>\n",
              "      <td>12.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>154.00</td>\n",
              "      <td>1.760000</td>\n",
              "      <td>35.400000</td>\n",
              "      <td>0.228000</td>\n",
              "      <td>0.142000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>134.60</td>\n",
              "      <td>1.280000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>0.034000</td>\n",
              "      <td>0.048000</td>\n",
              "      <td>84.000000</td>\n",
              "      <td>16.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       no_of_cases  Avg_pending_calls  Avg_resol_time  recurrence_freq  \\\n",
              "GROUP                                                                    \n",
              "0             7.50           2.000000       79.000000         0.010000   \n",
              "1            35.00           3.157143      132.571429         0.125714   \n",
              "2           395.25           1.025000        7.750000         0.240000   \n",
              "3           154.00           1.760000       35.400000         0.228000   \n",
              "4           134.60           1.280000       31.000000         0.034000   \n",
              "\n",
              "       Replace_percent  In_warranty_percent  Post_warranty_percent  \n",
              "GROUP                                                               \n",
              "0             0.600000             5.000000              95.000000  \n",
              "1             0.021429            99.142857               0.857143  \n",
              "2             0.000000            87.500000              12.500000  \n",
              "3             0.142000            27.000000              73.000000  \n",
              "4             0.048000            84.000000              16.000000  "
            ]
          },
          "execution_count": 29,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "techSuppClust = tech_supp_df.groupby(['GROUP'])\n",
        "techSuppClust.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "86Ypg_PHqf6a",
        "outputId": "2753099c-25b9-4e1d-be5f-fe9685ca6b7a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1a24579b70>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a2481c7f0>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24a6aa90>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24a91d68>],\n",
              "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1a24ac2080>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24ae9358>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24b12630>,\n",
              "        <matplotlib.axes._subplots.AxesSubplot object at 0x1a24b39940>]],\n",
              "      dtype=object)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
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\n",
            "text/plain": [
              "<Figure size 1080x720 with 8 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "techSuppScaled.boxplot(by='GROUP', layout = (2,4),figsize=(15,10))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "WECp4ZEmNTtY"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}