Telangana_regions_clustering (1).ipynb

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        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px"
      ]
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      "source": [
        "df=pd.read_csv('clustering_data.csv')\n",
        "df.shape\n",
        "df.head()"
      ],
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          "text": [
            "<ipython-input-124-fc2ba8385cdb>:1: DtypeWarning:\n",
            "\n",
            "Columns (9) have mixed types. Specify dtype option on import or set low_memory=False.\n",
            "\n"
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              "              CircleName      RegionName       DivisionName  \\\n",
              "0  Andhra Pradesh Circle  Kurnool Region  Hindupur Division   \n",
              "1  Andhra Pradesh Circle  Kurnool Region  Hindupur Division   \n",
              "2  Andhra Pradesh Circle  Kurnool Region  Hindupur Division   \n",
              "3  Andhra Pradesh Circle  Kurnool Region  Hindupur Division   \n",
              "4  Andhra Pradesh Circle  Kurnool Region  Hindupur Division   \n",
              "\n",
              "            OfficeName  Pincode OfficeType  Delivery   District  \\\n",
              "0       Peddakotla B.O   515631         BO  Delivery  ANANTAPUR   \n",
              "1       Pinnadhari B.O   515631         BO  Delivery  ANANTAPUR   \n",
              "2  Yerraguntapalle B.O   515631         BO  Delivery  ANANTAPUR   \n",
              "3  Obulareddipalli B.O   515581         BO  Delivery  ANANTAPUR   \n",
              "4       Odulapalli B.O   515581         BO  Delivery  ANANTAPUR   \n",
              "\n",
              "        StateName   Latitude  Longitude  \n",
              "0  ANDHRA PRADESH    14.5689   77.85624  \n",
              "1  ANDHRA PRADESH    14.5281  77.857014  \n",
              "2  ANDHRA PRADESH  14.561111   77.85715  \n",
              "3  ANDHRA PRADESH    14.2488    78.2588  \n",
              "4  ANDHRA PRADESH   14.24555    78.2477  "
            ],
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              "      <th>CircleName</th>\n",
              "      <th>RegionName</th>\n",
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              "      <th>0</th>\n",
              "      <td>Andhra Pradesh Circle</td>\n",
              "      <td>Kurnool Region</td>\n",
              "      <td>Hindupur Division</td>\n",
              "      <td>Peddakotla B.O</td>\n",
              "      <td>515631</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>ANANTAPUR</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>14.5689</td>\n",
              "      <td>77.85624</td>\n",
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              "      <th>1</th>\n",
              "      <td>Andhra Pradesh Circle</td>\n",
              "      <td>Kurnool Region</td>\n",
              "      <td>Hindupur Division</td>\n",
              "      <td>Pinnadhari B.O</td>\n",
              "      <td>515631</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>ANANTAPUR</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>14.5281</td>\n",
              "      <td>77.857014</td>\n",
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              "      <th>2</th>\n",
              "      <td>Andhra Pradesh Circle</td>\n",
              "      <td>Kurnool Region</td>\n",
              "      <td>Hindupur Division</td>\n",
              "      <td>Yerraguntapalle B.O</td>\n",
              "      <td>515631</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>ANANTAPUR</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>14.561111</td>\n",
              "      <td>77.85715</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Andhra Pradesh Circle</td>\n",
              "      <td>Kurnool Region</td>\n",
              "      <td>Hindupur Division</td>\n",
              "      <td>Obulareddipalli B.O</td>\n",
              "      <td>515581</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>ANANTAPUR</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>14.2488</td>\n",
              "      <td>78.2588</td>\n",
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              "      <td>Andhra Pradesh Circle</td>\n",
              "      <td>Kurnool Region</td>\n",
              "      <td>Hindupur Division</td>\n",
              "      <td>Odulapalli B.O</td>\n",
              "      <td>515581</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>ANANTAPUR</td>\n",
              "      <td>ANDHRA PRADESH</td>\n",
              "      <td>14.24555</td>\n",
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      "source": [
        "#Extracting Home State i.e., Telangana State's data\n",
        "hs_data=df[df.StateName=='TELANGANA']\n",
        "\n",
        "hs_data.head()"
      ],
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            "text/plain": [
              "           CircleName        RegionName        DivisionName  \\\n",
              "138  Telangana Circle  Hyderabad Region  Nizamabad Division   \n",
              "139  Telangana Circle  Hyderabad Region  Nizamabad Division   \n",
              "140  Telangana Circle  Hyderabad Region  Nizamabad Division   \n",
              "141  Telangana Circle  Hyderabad Region  Nizamabad Division   \n",
              "142  Telangana Circle  Hyderabad Region  Nizamabad Division   \n",
              "\n",
              "            OfficeName  Pincode OfficeType  Delivery   District  StateName  \\\n",
              "138      Arsapalli B.O   503186         BO  Delivery  NIZAMABAD  TELANGANA   \n",
              "139  Camp Ootpalli B.O   503180         BO  Delivery  NIZAMABAD  TELANGANA   \n",
              "140      Eraspalli B.O   503180         BO  Delivery  NIZAMABAD  TELANGANA   \n",
              "141      Singitham B.O   503187         BO  Delivery  KAMAREDDY  TELANGANA   \n",
              "142      Mahmadpur B.O   503101         BO  Delivery  KAMAREDDY  TELANGANA   \n",
              "\n",
              "       Latitude   Longitude  \n",
              "138  18.6845544  78.0773742  \n",
              "139  18.5329923   77.618717  \n",
              "140  18.5329923   77.618717  \n",
              "141  18.3096558  77.9466324  \n",
              "142  18.2108201  78.4853495  "
            ],
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              "      <th>CircleName</th>\n",
              "      <th>RegionName</th>\n",
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              "      <th>138</th>\n",
              "      <td>Telangana Circle</td>\n",
              "      <td>Hyderabad Region</td>\n",
              "      <td>Nizamabad Division</td>\n",
              "      <td>Arsapalli B.O</td>\n",
              "      <td>503186</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>NIZAMABAD</td>\n",
              "      <td>TELANGANA</td>\n",
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              "      <td>78.0773742</td>\n",
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              "      <th>139</th>\n",
              "      <td>Telangana Circle</td>\n",
              "      <td>Hyderabad Region</td>\n",
              "      <td>Nizamabad Division</td>\n",
              "      <td>Camp Ootpalli B.O</td>\n",
              "      <td>503180</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>NIZAMABAD</td>\n",
              "      <td>TELANGANA</td>\n",
              "      <td>18.5329923</td>\n",
              "      <td>77.618717</td>\n",
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              "      <th>140</th>\n",
              "      <td>Telangana Circle</td>\n",
              "      <td>Hyderabad Region</td>\n",
              "      <td>Nizamabad Division</td>\n",
              "      <td>Eraspalli B.O</td>\n",
              "      <td>503180</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>NIZAMABAD</td>\n",
              "      <td>TELANGANA</td>\n",
              "      <td>18.5329923</td>\n",
              "      <td>77.618717</td>\n",
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              "      <th>141</th>\n",
              "      <td>Telangana Circle</td>\n",
              "      <td>Hyderabad Region</td>\n",
              "      <td>Nizamabad Division</td>\n",
              "      <td>Singitham B.O</td>\n",
              "      <td>503187</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>KAMAREDDY</td>\n",
              "      <td>TELANGANA</td>\n",
              "      <td>18.3096558</td>\n",
              "      <td>77.9466324</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>142</th>\n",
              "      <td>Telangana Circle</td>\n",
              "      <td>Hyderabad Region</td>\n",
              "      <td>Nizamabad Division</td>\n",
              "      <td>Mahmadpur B.O</td>\n",
              "      <td>503101</td>\n",
              "      <td>BO</td>\n",
              "      <td>Delivery</td>\n",
              "      <td>KAMAREDDY</td>\n",
              "      <td>TELANGANA</td>\n",
              "      <td>18.2108201</td>\n",
              "      <td>78.4853495</td>\n",
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              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "hs_data",
              "summary": "{\n  \"name\": \"hs_data\",\n  \"rows\": 5816,\n  \"fields\": [\n    {\n      \"column\": \"CircleName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Tamilnadu Circle\",\n          \"Telangana Circle\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RegionName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Hyderabad Region\",\n          \"Hyderabad City Region\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DivisionName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 18,\n        \"samples\": [\n          \"Nizamabad Division\",\n          \"Peddapalli Division\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OfficeName\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5048,\n        \"samples\": [\n          \"Mustabad S.O\",\n          \"Kothapeta B.O\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Pincode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7016,\n        \"min\": 500001,\n        \"max\": 999999,\n        \"num_unique_values\": 676,\n        \"samples\": [\n          500010,\n          505524\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OfficeType\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"BO\",\n          \"PO\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Delivery\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Non Delivery\",\n          \"Delivery\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"District\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 33,\n        \"samples\": [\n          \"NAGARKURNOOL\",\n          \"Jagitial\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"StateName\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"TELANGANA\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3709,\n        \"samples\": [\n          \"18.7060356\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3655,\n        \"samples\": [\n          \"79.188245\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hs_data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "KH1K_a0mj33A",
        "outputId": "e52ff4c5-d8ce-4eed-ebc9-50c17fc6193c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "              CircleName             RegionName       DivisionName  \\\n",
              "150614  Telangana Circle       Hyderabad Region  Nalgonda Division   \n",
              "151046  Telangana Circle  Hyderabad City Region     Medak Division   \n",
              "151047  Telangana Circle  Hyderabad City Region     Medak Division   \n",
              "151048  Telangana Circle  Hyderabad City Region     Medak Division   \n",
              "151049  Telangana Circle  Hyderabad City Region     Medak Division   \n",
              "\n",
              "             OfficeName  Pincode OfficeType  Delivery             District  \\\n",
              "150614    Valigonda S.O   508112         PO  Delivery  YADADRI BHUVANAGIRI   \n",
              "151046  Makkarajpet S.O   502247         PO  Delivery                MEDAK   \n",
              "151047     Rajpally S.O   502117         PO  Delivery                MEDAK   \n",
              "151048      Toopran S.O   502334         PO  Delivery                MEDAK   \n",
              "151049    Yousufpet S.O   502331         PO  Delivery                MEDAK   \n",
              "\n",
              "        StateName   Latitude   Longitude  \n",
              "150614  TELANGANA  17.377222  79.0245556  \n",
              "151046  TELANGANA  17.949361  78.5200556  \n",
              "151047  TELANGANA  18.019889  78.2949444  \n",
              "151048  TELANGANA  17.905167  78.4610833  \n",
              "151049  TELANGANA   18.04325  78.1363056  "
            ],
            "text/html": [
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              "      <th></th>\n",
              "      <th>CircleName</th>\n",
              "      <th>RegionName</th>\n",
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              "      <td>Nalgonda Division</td>\n",
              "      <td>Valigonda S.O</td>\n",
              "      <td>508112</td>\n",
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              "      <td>Telangana Circle</td>\n",
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              "      <td>Makkarajpet S.O</td>\n",
              "      <td>502247</td>\n",
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              "      <td>17.905167</td>\n",
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              "      <td>Yousufpet S.O</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-f24306bc-f625-4cc8-af93-df29d92732da');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-93e19050-2952-476c-ac35-d9fb2efc3ac2\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-93e19050-2952-476c-ac35-d9fb2efc3ac2')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-93e19050-2952-476c-ac35-d9fb2efc3ac2 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hs_data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3whjhFEFuVK2",
        "outputId": "a1bc1ade-0cf7-40b3-8b1f-8fe501e0e46b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5816, 11)"
            ]
          },
          "metadata": {},
          "execution_count": 127
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Visualising the geographical location using longitude, latitude data\n",
        "\n",
        "fig=px.scatter_geo(hs_data,lat='Latitude',lon='Longitude',hover_name=\"StateName\")\n",
        "fig.update_layout(title='Telangana Area',title_x=0.5)\n",
        "fig.show()\n",
        "#Leave alone Telanagana, many of the coordinates do not even lie in India."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "WmAqNgsoucLD",
        "outputId": "fd3476fe-fd21-4e79-ae17-ce6920a35c0d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "markdown",
      "source": [
        "DATA PREPROCESSING"
      ],
      "metadata": {
        "id": "58eSWRnyQk6b"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#data preprocessing\n",
        "hs_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UEN1El6o0A9N",
        "outputId": "36656d0b-d53c-45cb-861c-ceb6248d22ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5816 entries, 138 to 151049\n",
            "Data columns (total 11 columns):\n",
            " #   Column        Non-Null Count  Dtype \n",
            "---  ------        --------------  ----- \n",
            " 0   CircleName    5816 non-null   object\n",
            " 1   RegionName    5816 non-null   object\n",
            " 2   DivisionName  5816 non-null   object\n",
            " 3   OfficeName    5816 non-null   object\n",
            " 4   Pincode       5816 non-null   int64 \n",
            " 5   OfficeType    5816 non-null   object\n",
            " 6   Delivery      5816 non-null   object\n",
            " 7   District      5816 non-null   object\n",
            " 8   StateName     5816 non-null   object\n",
            " 9   Latitude      4281 non-null   object\n",
            " 10  Longitude     4281 non-null   object\n",
            "dtypes: int64(1), object(10)\n",
            "memory usage: 545.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values=hs_data.isnull().sum()\n",
        "missing_values[0:11]\n",
        "#There are a lot of missing values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "drhIEWGqiZvz",
        "outputId": "310a1e48-813e-464d-f1bd-852134c8850b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CircleName         0\n",
              "RegionName         0\n",
              "DivisionName       0\n",
              "OfficeName         0\n",
              "Pincode            0\n",
              "OfficeType         0\n",
              "Delivery           0\n",
              "District           0\n",
              "StateName          0\n",
              "Latitude        1535\n",
              "Longitude       1535\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#convert datatype of lat, long to float\n",
        "hs_data['Latitude']=hs_data['Latitude'].astype(float)\n",
        "hs_data['Longitude']=hs_data['Longitude'].astype(float)\n",
        "hs_data.info()"
      ],
      "metadata": {
        "id": "t9zMTFZ8oK9o",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcff62c2-9f45-4150-f95a-d35f5b569819"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 5816 entries, 138 to 151049\n",
            "Data columns (total 11 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   CircleName    5816 non-null   object \n",
            " 1   RegionName    5816 non-null   object \n",
            " 2   DivisionName  5816 non-null   object \n",
            " 3   OfficeName    5816 non-null   object \n",
            " 4   Pincode       5816 non-null   int64  \n",
            " 5   OfficeType    5816 non-null   object \n",
            " 6   Delivery      5816 non-null   object \n",
            " 7   District      5816 non-null   object \n",
            " 8   StateName     5816 non-null   object \n",
            " 9   Latitude      4281 non-null   float64\n",
            " 10  Longitude     4281 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(8)\n",
            "memory usage: 545.2+ KB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-131-0f8703a692e0>:2: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n",
            "<ipython-input-131-0f8703a692e0>:3: SettingWithCopyWarning:\n",
            "\n",
            "\n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "hs_data=hs_data.dropna()\n",
        "missing_values=hs_data.isnull().sum()\n",
        "missing_values[0:11]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S0Qqv1TrQKt6",
        "outputId": "89531c34-72db-402b-b1c9-87a51c9de613"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CircleName      0\n",
              "RegionName      0\n",
              "DivisionName    0\n",
              "OfficeName      0\n",
              "Pincode         0\n",
              "OfficeType      0\n",
              "Delivery        0\n",
              "District        0\n",
              "StateName       0\n",
              "Latitude        0\n",
              "Longitude       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We have used interpolation to handle missing values\n",
        "#let us remove outliers\n",
        "hs_data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lM0u4ExMQbF9",
        "outputId": "b948370f-fba8-45eb-b28e-b83755ad6c66"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Pincode     Latitude    Longitude\n",
              "count    4281.000000  4281.000000  4281.000000\n",
              "mean   506039.947909    17.985249    78.783889\n",
              "std      7897.336871     2.812220     3.885304\n",
              "min    500001.000000     0.000000     0.000000\n",
              "25%    504295.000000    17.162942    78.355944\n",
              "50%    506168.000000    17.861810    79.060369\n",
              "75%    508115.000000    18.594514    79.628502\n",
              "max    999999.000000    80.540170    82.598000"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Pincode</th>\n",
              "      <th>Latitude</th>\n",
              "      <th>Longitude</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4281.000000</td>\n",
              "      <td>4281.000000</td>\n",
              "      <td>4281.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>506039.947909</td>\n",
              "      <td>17.985249</td>\n",
              "      <td>78.783889</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>7897.336871</td>\n",
              "      <td>2.812220</td>\n",
              "      <td>3.885304</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>500001.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>504295.000000</td>\n",
              "      <td>17.162942</td>\n",
              "      <td>78.355944</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>506168.000000</td>\n",
              "      <td>17.861810</td>\n",
              "      <td>79.060369</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>508115.000000</td>\n",
              "      <td>18.594514</td>\n",
              "      <td>79.628502</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>999999.000000</td>\n",
              "      <td>80.540170</td>\n",
              "      <td>82.598000</td>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"hs_data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Pincode\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 318756.61858507816,\n        \"min\": 4281.0,\n        \"max\": 999999.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          506039.947909367,\n          506168.0,\n          4281.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Latitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1505.9417677739636,\n        \"min\": 0.0,\n        \"max\": 4281.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          17.985249303377714,\n          17.86181,\n          4281.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Longitude\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1493.6561039926391,\n        \"min\": 0.0,\n        \"max\": 4281.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          78.7838889840925,\n          79.0603689,\n          4281.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "max_lat=19.783  #These can be set through domain knowledge"
      ],
      "metadata": {
        "id": "wLPaRIJKSyxi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_lat=15.767"
      ],
      "metadata": {
        "id": "qF2yybhOWgUX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "max_long=81.717"
      ],
      "metadata": {
        "id": "PedSsQl0WyHC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "min_long=77.267"
      ],
      "metadata": {
        "id": "M8Ula3QHYSD6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hs_data=hs_data[(hs_data['Latitude']<=max_lat)&(hs_data['Latitude']>=min_lat)&(hs_data['Longitude']<=max_long)&(hs_data['Longitude']>=min_long)]\n",
        "hs_data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WVSQmAQAZEct",
        "outputId": "0f91a918-caff-4600-f081-52ce81e410d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Index: 4200 entries, 138 to 151049\n",
            "Data columns (total 11 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   CircleName    4200 non-null   object \n",
            " 1   RegionName    4200 non-null   object \n",
            " 2   DivisionName  4200 non-null   object \n",
            " 3   OfficeName    4200 non-null   object \n",
            " 4   Pincode       4200 non-null   int64  \n",
            " 5   OfficeType    4200 non-null   object \n",
            " 6   Delivery      4200 non-null   object \n",
            " 7   District      4200 non-null   object \n",
            " 8   StateName     4200 non-null   object \n",
            " 9   Latitude      4200 non-null   float64\n",
            " 10  Longitude     4200 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(8)\n",
            "memory usage: 393.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(hs_data['Longitude'],hs_data['Latitude'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
        "id": "V4pKo3QGaFab",
        "outputId": "4125bf7a-5895-47f7-e982-88cf16d4a604"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x79d46e3d9ba0>"
            ]
          },
          "metadata": {},
          "execution_count": 139
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig=px.scatter_geo(hs_data,lat='Latitude',lon='Longitude',hover_name=\"StateName\")\n",
        "fig.update_layout(title='Telangana Area',title_x=0.5)\n",
        "fig.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "LHVe-N7TdKEw",
        "outputId": "76a39484-e978-49be-9c47-0d9d404d5643"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X=np.array(hs_data[['Longitude','Latitude']])\n",
        "def kmeans(data, k):\n",
        "  centroids = X[np.random.choice(X.shape[0], k, replace=False)]\n",
        "  for _ in range(100):\n",
        "    labels = np.argmin(np.linalg.norm(X[:, np.newaxis] - centroids, axis=2), axis=1)\n",
        "    centroids = np.array([np.mean(X[labels == i], axis=0) for i in range(k)])\n",
        "  return labels, centroids"
      ],
      "metadata": {
        "id": "fef41MXZjrez"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "CLUSTERING BASED ON NUMBER OF DISTRICTS"
      ],
      "metadata": {
        "id": "ibu1W7cRuNMP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#To find number of districts\n",
        "num=len(pd.unique(hs_data['District']))\n",
        "print(\"Number of districts:\", num)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ShXllKgfugrm",
        "outputId": "1bdd5bfe-e7e2-4161-cd59-106df9b67d95"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of districts: 33\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels, centroids = kmeans(X, 33)\n",
        "plt.scatter(X[:,0],X[:,1],c = labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 448
        },
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        "outputId": "0fa14ca1-8bbe-4d27-dad2-8c372661ea0a"
      },
      "execution_count": null,
      "outputs": [
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          "metadata": {},
          "execution_count": 145
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          "data": {
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              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#From here it is clear that many pincodes in Hyderabad city region, and in districts near Khammam are not availble"
      ],
      "metadata": {
        "id": "1QuvJxDQy2iC"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}