Project_customer_segmentation.ipynb

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        "<a href=\"https://colab.research.google.com/github/Kshitijasharma/celebal_internship/blob/main/Project_customer_segmentation.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "#import all the required lib\n",
        "import pandas as pd\n",
        "import numpy as np"
      ]
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      "cell_type": "code",
      "source": [
        "#loading the dataset\n",
        "df=pd.read_csv('Mall_Customers.csv')"
      ],
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      "execution_count": 8,
      "outputs": []
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        "## Analysing the dataset"
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      "source": [
        "df.shape  #No. of rows=200, no.of columns=5"
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              "(200, 5)"
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          "execution_count": 9
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      "source": [
        "df.columns #will display the column names"
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      "execution_count": 10,
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          "data": {
            "text/plain": [
              "Index(['CustomerID', 'Gender', 'Age', 'Annual Income (k$)',\n",
              "       'Spending Score (1-100)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 10
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        "df.dtypes  #return the datatypes of variables in the dataset"
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              "CustomerID                 int64\n",
              "Gender                    object\n",
              "Age                        int64\n",
              "Annual Income (k$)         int64\n",
              "Spending Score (1-100)     int64\n",
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            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
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              "     CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "195         196  Female   35                 120                      79\n",
              "196         197  Female   45                 126                      28\n",
              "197         198    Male   32                 126                      74\n",
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              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>199</th>\n",
              "      <td>200</td>\n",
              "      <td>Male</td>\n",
              "      <td>30</td>\n",
              "      <td>137</td>\n",
              "      <td>83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-2aa62cc6-267a-41ed-a1c8-74cb3d71f22e')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2aa62cc6-267a-41ed-a1c8-74cb3d71f22e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2aa62cc6-267a-41ed-a1c8-74cb3d71f22e');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-38753655-2217-4454-8ee7-17fdfbffa645\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-38753655-2217-4454-8ee7-17fdfbffa645')\"\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-38753655-2217-4454-8ee7-17fdfbffa645 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 196,\n        \"max\": 200,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          197,\n          200,\n          198\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Male\",\n          \"Female\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 30,\n        \"max\": 45,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          45,\n          30\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7,\n        \"min\": 120,\n        \"max\": 137,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          120,\n          126\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30,\n        \"min\": 18,\n        \"max\": 83,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          28,\n          83\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.info()    #it tells the data is okay, no null values existing"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C-zfYxyCAuUO",
        "outputId": "c5002d6f-a613-4769-94c0-508127ec4424"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 200 entries, 0 to 199\n",
            "Data columns (total 5 columns):\n",
            " #   Column                  Non-Null Count  Dtype \n",
            "---  ------                  --------------  ----- \n",
            " 0   CustomerID              200 non-null    int64 \n",
            " 1   Gender                  200 non-null    object\n",
            " 2   Age                     200 non-null    int64 \n",
            " 3   Annual Income (k$)      200 non-null    int64 \n",
            " 4   Spending Score (1-100)  200 non-null    int64 \n",
            "dtypes: int64(4), object(1)\n",
            "memory usage: 7.9+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.describe().T       #return complete statistical information column wise( numerical columns only )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 174
        },
        "id": "fOmxPATkA8Ya",
        "outputId": "7d08def5-87ea-4fb2-cab1-53f9f398922a"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        count    mean        std   min    25%    50%     75%  \\\n",
              "CustomerID              200.0  100.50  57.879185   1.0  50.75  100.5  150.25   \n",
              "Age                     200.0   38.85  13.969007  18.0  28.75   36.0   49.00   \n",
              "Annual Income (k$)      200.0   60.56  26.264721  15.0  41.50   61.5   78.00   \n",
              "Spending Score (1-100)  200.0   50.20  25.823522   1.0  34.75   50.0   73.00   \n",
              "\n",
              "                          max  \n",
              "CustomerID              200.0  \n",
              "Age                      70.0  \n",
              "Annual Income (k$)      137.0  \n",
              "Spending Score (1-100)   99.0  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-95f3ec40-d160-4c66-9ee6-cadce4a9eef9\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
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              "      <th>max</th>\n",
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              "  </thead>\n",
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              "    <tr>\n",
              "      <th>CustomerID</th>\n",
              "      <td>200.0</td>\n",
              "      <td>100.50</td>\n",
              "      <td>57.879185</td>\n",
              "      <td>1.0</td>\n",
              "      <td>50.75</td>\n",
              "      <td>100.5</td>\n",
              "      <td>150.25</td>\n",
              "      <td>200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Age</th>\n",
              "      <td>200.0</td>\n",
              "      <td>38.85</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>18.0</td>\n",
              "      <td>28.75</td>\n",
              "      <td>36.0</td>\n",
              "      <td>49.00</td>\n",
              "      <td>70.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <td>200.0</td>\n",
              "      <td>60.56</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>15.0</td>\n",
              "      <td>41.50</td>\n",
              "      <td>61.5</td>\n",
              "      <td>78.00</td>\n",
              "      <td>137.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <td>200.0</td>\n",
              "      <td>50.20</td>\n",
              "      <td>25.823522</td>\n",
              "      <td>1.0</td>\n",
              "      <td>34.75</td>\n",
              "      <td>50.0</td>\n",
              "      <td>73.00</td>\n",
              "      <td>99.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      display: none;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-95f3ec40-d160-4c66-9ee6-cadce4a9eef9 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-95f3ec40-d160-4c66-9ee6-cadce4a9eef9');\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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              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-193a490e-6df2-4eed-91d9-20f262b71109')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
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              "    </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-193a490e-6df2-4eed-91d9-20f262b71109 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",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 200.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26.822708507282904,\n        \"min\": 38.85,\n        \"max\": 100.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          38.85\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18.81278868952788,\n        \"min\": 13.96900733155888,\n        \"max\": 57.879184513951124,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          13.96900733155888\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.03234926989282,\n        \"min\": 1.0,\n        \"max\": 18.0,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.441431300390846,\n        \"min\": 28.75,\n        \"max\": 50.75,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          28.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 27.703790354390136,\n        \"min\": 36.0,\n        \"max\": 100.5,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          36.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43.66652751250092,\n        \"min\": 49.0,\n        \"max\": 150.25,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          49.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 56.15751656427363,\n        \"min\": 70.0,\n        \"max\": 200.0,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          70.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA CLEANING**"
      ],
      "metadata": {
        "id": "M_KKtAbiBbVj"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#create a copy of dataset, so that we can retrive to the original one if something went wrong\n",
        "df_copy=df.copy(deep=True)\n",
        "df_copy.head(5)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "k692tHLgBV6S",
        "outputId": "9a86963d-0c98-4509-d01a-4eae27658f13"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  Spending Score (1-100)\n",
              "0           1    Male   19                  15                      39\n",
              "1           2    Male   21                  15                      81\n",
              "2           3  Female   20                  16                       6\n",
              "3           4  Female   23                  16                      77\n",
              "4           5  Female   31                  17                      40"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-24737b3c-6bf6-481f-b955-61bb8362d9bb\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-24737b3c-6bf6-481f-b955-61bb8362d9bb')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
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              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-24737b3c-6bf6-481f-b955-61bb8362d9bb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-24737b3c-6bf6-481f-b955-61bb8362d9bb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-c8f52220-8619-4397-8bcf-6a9a2fd0c154\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-c8f52220-8619-4397-8bcf-6a9a2fd0c154')\"\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-c8f52220-8619-4397-8bcf-6a9a2fd0c154 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",
              "variable_name": "df_copy",
              "summary": "{\n  \"name\": \"df_copy\",\n  \"rows\": 200,\n  \"fields\": [\n    {\n      \"column\": \"CustomerID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57,\n        \"min\": 1,\n        \"max\": 200,\n        \"num_unique_values\": 200,\n        \"samples\": [\n          96,\n          16,\n          31\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gender\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2,\n        \"samples\": [\n          \"Female\",\n          \"Male\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 13,\n        \"min\": 18,\n        \"max\": 70,\n        \"num_unique_values\": 51,\n        \"samples\": [\n          55,\n          26\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Annual Income (k$)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 26,\n        \"min\": 15,\n        \"max\": 137,\n        \"num_unique_values\": 64,\n        \"samples\": [\n          87,\n          101\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Spending Score (1-100)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 25,\n        \"min\": 1,\n        \"max\": 99,\n        \"num_unique_values\": 84,\n        \"samples\": [\n          83,\n          39\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Dropping the column of 'CustomerID' as it does not provide any value\n",
        "df_copy.drop('CustomerID', axis=1, inplace=True)\n",
        "df_copy.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HQ4xWDiTB5dR",
        "outputId": "3a0e143c-bf2a-44c5-a696-77a69c997f27"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Gender', 'Age', 'Annual Income (k$)', 'Spending Score (1-100)'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**DATA VISUALISATION**"
      ],
      "metadata": {
        "id": "qNebrmrrDvLm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Loading essential libraries\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "metadata": {
        "id": "lbl8BJ7dDuY2"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_copy.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cCVr8gaZD9r2",
        "outputId": "fa590005-753d-4aa0-f1ca-dea71ff7be94"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Gender', 'Age', 'Annual Income (k$)', 'Spending Score (1-100)'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**GENDER ANALYSIS**"
      ],
      "metadata": {
        "id": "EjUkcZpNECsn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#understanding matplotlib\n",
        "\"\"\"\"import matplotlib.pyplot as plt\n",
        "\n",
        "# Simple line plot\n",
        "plt.plot([1, 2, 3, 4], [10, 20, 25, 30])\n",
        "plt.xlabel('X-axis Label')\n",
        "plt.ylabel('Y-axis Label')\n",
        "plt.title('Simple Line Plot')\n",
        "plt.show()\n",
        "\"\"\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "8vNyIQaZEB7i",
        "outputId": "2de9323a-d9ad-4a5d-9f10-103e0bebe9d6"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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JLyRm4xHeX3uIjP//i67oiABe6BNBWIC3KZnK8vlt+iBoERERqToMw+B/u5N5Y/kBTqZdAKBVsC8v9ovgpqZ1TE5XeipAIiIiUio7jp7n1e/2E3c8DYAgXw/G9WrBgOvr4+RkMTdcGakAiYiIyBUdPZfFGysOsGzP7xcV9nJz5olbm/LoLU3wdLO/Ac6loQIkIiIiJbJm5zNrbQILNh0hv9DAyQIDO4QwtkdzAnyr9o+LVIBERESkmLwCG59vPsp7axNIy84H4JZmdXixXwThQdXjx0EqQCIiIgL8PsB55a+pvL58P0fOZQPQPNCbiX0j6NoiwOR05UsFSERERNh9Io2psfvZmnQegDrebozt0YKBHRrg4mx3N464ZipAIiIiDuxk2gXeWnGAb+JOAeDu4sSjtzThia5N8XavvjWh+m6ZiIiIXFZmbgFz1h3i4x+TyC2wATDg+vqM69WCYH9Pk9NVPBUgERERB1JQaOPL7ceZueogZzPzAOgYWotJ/SJo08Df3HCVSAVIRETEARiGwbqDZ3gtdj8JpzMBCK1Tgxf6hNOzZSAWS9W6kOG1UgESERGp5vYnp/Pasv38mHAWAH8vV57u3owHOjXCzaX6DXAuDRUgERGRaup0eg7//P4gX+04js0AV2cLQ29qzFO3NcPPy9XseKZSARIREalmsvMK+GhDEh9uSCQ7rxCAfq3r8XzvcBrW9jI5nX1QARIREakmbDaDJbtO8tbKA6Sm5wLQLsSfSf0i6NC4lsnp7IsKkIiISDWwMfEs02L38+updADq+3vyfJ9wbm9Tz+EGOJeGCpCIiEgVduh0Jq8v38/q/acB8HF3YWS3MIbe1BgP16p5p/bKoAIkIiJSBZ3LzOXdNQks3HKMQpuBs5OFBzo15Onuzajt7W52PLunAiQiIlKF5OQXErPxCO+vPURGbgEA0REBvNAngrAAb5PTVR0qQCIiIlWAYRj8b3cyb644wInfLgDQKtiXF/tFcFPTOianq3pUgEREROzcjqPnefW7/cQdTwMgyNeD53q14K7r6+PkpAHOV0MFSERExE4dPZfFGysOsGxPCgBebs48cWtTHr2lCZ5uGuB8LVSARERE7Iw1O59ZaxNYsOkI+YUGThYY2CGEsT2aE+DrYXa8akEFSERExE7kFdj4fPNR3lubQFp2PgC3NKvDxL4RRNTzNTld9aICJCIiYjLDMPh+XyqvLz9A0tksAJoFeDOxXwRdm9fVhQwrgAqQiIiIiXafSGNq7H62Jp0HoI63G8/0aM69HUJwcXbMO7VXBhUgERERE5xKu8BbK+NZuuskAO4uTjxySyhP3NoUHw/HvlN7ZVABEhERqUSZuQXMWXeIj39MIrfABsCA6+vzXK8W1Pf3NDmd41ABEhERqQQFhTa+3H6cmasOcjYzD4COobWY1C+CNg38zQ3ngFSAREREKti6+NO8tmw/B1MzAWhc24sJfSPo2TJQA5xNogIkIiJSQQ6kpDMtdj8/JpwFwN/LldHdmvHgjY1wc9EAZzOZuvenT59OZGQkPj4+BAQE0L9/f+Lj44teP3LkCBaLpcTHV199ddn1Dh069JL5e/fuXRmbJCIiwumMHF74z276vvsjPyacxdXZwiOdQ1n/3G0M6xyq8mMHTD0DtH79ekaOHElkZCQFBQVMnDiRnj17sm/fPmrUqEFISAjJycnFlvnXv/7FW2+9RZ8+fa647t69ezN//vyi5+7u7hWyDSIiIhddyCvkox8PM3d9Itl5hQD0bR3E873DaVS7hsnp5I9MLUArVqwo9jwmJoaAgAB27NhBly5dcHZ2JigoqNg8S5cuZeDAgXh7e19x3e7u7pcsKyIiUhFsNoMlu04yY2U8Kek5ALQL8WdSvwg6NK5lcjopiV2NAbJarQDUqlXywbJjxw7i4uJ4//33/3Jd69atIyAggJo1a9KtWzemTp1K7dq1S5w3NzeX3Nzcoufp6elXkV5ERBzRxsSzTIvdz6+nfv/sqO/vyfN9wrm9TT0NcLZjFsMwDLNDANhsNu644w7S0tL46aefSpznySefZN26dezbt++K6/riiy/w8vIiNDSUxMREJk6ciLe3N5s2bcLZ+dK7506ePJkpU6ZcMt1qteLrq3uviIjIpQ6dzuT15ftZvf80AD7uLozsFsbQmxrj4ao7tZshPT0dPz+/Un1+200BGjFiBMuXL+enn36iQYMGl7x+4cIF6tWrx0svvcSzzz5bpnUfPnyYpk2bsnr1arp3737J6yWdAQoJCVEBEhGRS5zLzOXdNQks3HKMQpuBs5OFBzo15OnuzajtrfGmZipLAbKLr8CeeuopvvvuOzZs2FBi+QH4+uuvyc7OZvDgwWVef5MmTahTpw6HDh0qsQC5u7trkLSIiFxRTn4hMRuP8P7aQ2TkFgAQHRHAC30iCAu48rhUsT+mFiDDMBg1ahRLly5l3bp1hIaGXnbeTz75hDvuuIO6deuW+X1OnDjBuXPnqFev3rXEFRERB2QYBv/bncybKw5w4rcLALSs58ukfhHcFFbH5HRytUwtQCNHjmTRokV8++23+Pj4kJKSAoCfnx+env93P5RDhw6xYcMGli1bVuJ6wsPDmT59OgMGDCAzM5MpU6Zw9913ExQURGJiIuPHjycsLIxevXpVynaJiEj1sOPoeabG7mfXsTQAAn3dea5nC+66oQHOThrgXJWZWoDmzJkDQNeuXYtNnz9/PkOHDi16Pm/ePBo0aEDPnj1LXE98fHzRL8icnZ3ZvXs3CxYsIC0tjeDgYHr27Mmrr76qr7lERKRUjp3L5o0VB4jd8/u16LzcnHm8S1Me7RKKl5tdjB6Ra2Q3g6DtSVkGUYmISPVhzc5n9g8JLNh4lLxCGxYLDGwfwrM9mxPg62F2PPkLVW4QtIiIiJnyC218vvko765JIC07H4BbmtVhYt8IIurpD+HqSAVIREQclmEYfL8vldeXHyDpbBYAzQK8mdgvgq7N6+pChtWYCpCIiDikPSesTI3dx5ak8wDU8XbjmR7NubdDCC7OullpdacCJCIiDuVU2gVmrIxnya6TALi7ODG8cygjujbFx8PV5HRSWVSARETEIWTmFjB3XSIf/XiY3AIbAP3bBTOudzj1/T3/YmmpblSARESkWisotLF4+wneXnWQs5m/3/aoY+NavNgvgrYh/uaGE9OoAImISLW1Lv40ry3bz8HUTAAa1/ZiQt8IerYM1ABnB6cCJCIi1c6BlHSmxe7nx4SzAPh7uTK6WzMevLERbi4a4CwqQCIiUo2czsjh7e8Psnj7cWwGuDpbGBLVmFHdmuHnpQHO8n9UgEREpMq7kFfIRz8eZu76RLLzCgHo2zqI53uH06h2DZPTiT1SARIRkSrLZjNYsuskM1bGk5KeA0C7EH8m9YugQ+NaJqcTe6YCJCIiVdLGxLNMi93Pr6fSAajv78nzfcK5vU09DXCWv6QCJCIiVUrimUymL9vP6v2nAfBxd2FktzCG3tQYD1dnk9NJVaECJCIiVcL5rDzeXX2QhVuOUWAzcHaycH/HhoyJbkZtb3ez40kVowIkIiJ2LSe/kAUbjzD7h0Nk5BQA0D08gAl9wwkL8DE5nVRVKkAiImKXDMPgu93JvLHiACd+uwBAy3q+TOoXwU1hdUxOJ1WdCpCIiNidHUd/Y2rsPnYdSwMg0Ned53q24K4bGuDspAHOcu1UgERExG4cO5fNGysOELsnGQBPV2eeuLUpj3YJxctNH1lSfnQ0iYiI6awX8pm9NoEFG4+SV2jDYoGB7UN4tmdzAnw9zI4n1ZAKkIiImCa/0MbCzUd5d00Cv2XnA9A5rA4T+0bQMtjX5HRSnakAiYhIpTMMg1X7Unl9+QEOn80CICzAmxf7RtC1RV1dyFAqnAqQiIhUqj0nrEyN3ceWpPMA1K7hxjM9mnNfZAguzrpTu1QOFSAREakUp9IuMGNlPEt2nQTA3cWJ4Z1DGdG1KT4eulO7VC4VIBERqVCZuQXMXZfIRz8eJrfABkD/dsGM6x1OfX9Pk9OJo1IBEhGRClFQaGPx9hO8veogZzNzAejYuBYv9ougbYi/ueHE4akAiYhIuVsXf5rXlu3nYGomAI1re/FCnwh6tQrUAGexCypAIiJSbg6kpDMtdj8/JpwFwM/Tlae7N+PBGxvh5qIBzmI/VIBEROSanc7I4e3vD7J4+3FsBrg6WxgS1ZhR3Zrh56UBzmJ/VIBEROSqXcgr5OMfDzNnfSLZeYUA9LkuiBf6hNOodg2T04lcngqQiIiUmc1msHTXSd5aGU9Keg4AbUP8mdQvgsjGtUxOJ/LXVIBERKRMNiWeY9qyfew9mQ5AfX9Pxvduwe1tgnHSndqlijB1RNr06dOJjIzEx8eHgIAA+vfvT3x8fLF5unbtisViKfZ44oknrrhewzB4+eWXqVevHp6enkRHR5OQkFCRmyIiUu0lnsnkkQXbGfTRZvaeTMfH3YXne4ez5tlbubNdfZUfqVJMLUDr169n5MiRbN68mVWrVpGfn0/Pnj3JysoqNt+jjz5KcnJy0ePNN9+84nrffPNN3nvvPebOncuWLVuoUaMGvXr1IicnpyI3R0SkWjqflccr3+6l18wNrN6firOThYdubMS6cV0Z0bUpHq7OZkcUKTNTvwJbsWJFsecxMTEEBASwY8cOunTpUjTdy8uLoKCgUq3TMAzeeecdJk2axJ133gnAp59+SmBgIN988w333Xdf+W2AiEg1lltQSMzPR5j9wyEycgoA6B4ewIS+4YQF+JicTuTa2NVFGaxWKwC1ahUfQLdw4ULq1KnDddddx4QJE8jOzr7sOpKSkkhJSSE6Orpomp+fH506dWLTpk0lLpObm0t6enqxh4iIozIMg+92nyL67fVMX36AjJwCIur5svCRTnwyNFLlR6oFuxkEbbPZGDNmDDfffDPXXXdd0fT777+fRo0aERwczO7du3n++eeJj49nyZIlJa4nJSUFgMDAwGLTAwMDi177s+nTpzNlypRy2hIRkaprx9HfmBa7j53H0gAI8HHnuV4tuPuGBjhrjI9UI3ZTgEaOHMnevXv56aefik1/7LHHiv67devW1KtXj+7du5OYmEjTpk3L5b0nTJjA2LFji56np6cTEhJSLusWEakKjp/P5vUVB4jdnQyAp6szT9zalEe7hOLlZjcfFSLlxi6O6qeeeorvvvuODRs20KBBgyvO26lTJwAOHTpUYgG6OFYoNTWVevXqFU1PTU2lXbt2Ja7T3d0dd3f3q0wvIlJ1WS/k8/4Ph4j5+Qh5hTYsFhjYPoRnezYnwNfD7HgiFcbUAmQYBqNGjWLp0qWsW7eO0NDQv1wmLi4OoFi5+aPQ0FCCgoJYs2ZNUeFJT09ny5YtjBgxoryii4hUafmFNhZuPsq7axL4LTsfgM5hdZjYN4KWwb4mpxOpeKYWoJEjR7Jo0SK+/fZbfHx8isbo+Pn54enpSWJiIosWLaJv377Url2b3bt388wzz9ClSxfatGlTtJ7w8HCmT5/OgAEDsFgsjBkzhqlTp9KsWTNCQ0N56aWXCA4Opn///iZtqYiIfTAMg1X7Unl9+QEOn/39kiNhAd682DeCri3q6k7t4jBMLUBz5swBfr/Y4R/Nnz+foUOH4ubmxurVq3nnnXfIysoiJCSEu+++m0mTJhWbPz4+vugXZADjx48nKyuLxx57jLS0NDp37syKFSvw8NDpXBFxXHtOWJkau48tSecBqF3DjWd6NOe+yBBcnO3qR8EiFc5iGIZhdgh7k56ejp+fH1arFV9fnQoWkartVNoFZqyMZ8mukwC4uTjxSOdQRnRtio+H7tQu1UdZPr/tYhC0iIiUv8zcAuauS+SjHw+TW2ADoH+7YMb1Dqe+v6fJ6UTMpQIkIlLNFBTaWLz9BG+vOsjZzFwAOjauxYv9Imgb4m9uOBE7oQIkIlKNrD94htdi9xOfmgFA49pevNAnnF6tgjTAWeQPVIBERKqB+JQMpi3bz4aDZwDw83RldPdmPHRjI9xcNMBZ5M9UgEREqrDTGTnMXHWQL7cdx2aAq7OFwVGNGdUtDH8vN7PjidgtFSARkSroQl4hH/94mLnrE8nKKwSgz3VBPN87nMZ1apicTsT+qQCJiFQhNpvB0l0nmfF9PMnWHADahvgzqV8EkY1rmZxOpOpQARIRqSI2JZ5j2rJ97D2ZDkB9f0/G927B7W2CcdKd2kXKRAVIRMTOHT6TyfTlB1i1LxUAH3cXnrwtjIdvboyHq7PJ6USqJhUgERE7dT4rj/fWJPD55qMU2AycnSzc37EhY6KbUdvb3ex4IlWaCpCIiJ3JLShkwcYjzFp7iIycAgC6hwcwoW84YQE+JqcTqR5UgERE7IRhGMTuSeaNFQc4fv4CABH1fJnUL4Kbw+qYnE6kelEBEhGxAzuO/sa02H3sPJYGQICPO8/1asHdNzTAWQOcRcqdCpCIiImOn8/m9RUHiN2dDICnqzOP39qEx7o0wctN/0SLVBT9v0tExATWC/m8/8MhYn4+Ql6hDYsF/t6+Ac/2bEGgr4fZ8USqPRUgEZFKlF9oY+Hmo7y7JoHfsvMB6BxWh4l9I2gZ7GtyOhHHoQIkIlIJDMNg1b5UXl9+gMNnswAIC/Dmxb4RdG1RV3dqF6lkKkAiIhVs70krU2P3sfnweQBq13DjmR7NuS8yBBdn3aldxAwqQCIiFSTZeoG3VsazdNdJDAPcXJwY3jmUJ7s2xcfD1ex4Ig5NBUhEpJxl5hbw4fpEPvrxMDn5NgDubBfMuF4taFDTy+R0IgIqQCIi5abQZrB4+3H++f1BzmbmAhDZuCYv9mtJuxB/c8OJSDEqQCIi5WD9wTO8Fruf+NQMABrX9uKFPuH0ahWkAc4idkgFSETkGsSnZDBt2X42HDwDgJ+nK6O7N+OhGxvh5qIBziL2SgVIROQqnM7IYeaqg3y57Tg2A1ydLQyOasyobmH4e7mZHU9E/oIKkIhIGVzIK+STnw4zZ10iWXmFAPS5Lojne4fTuE4Nk9OJSGmpAImIlILNZvBN3EneWhlPsjUHgLYh/kzqF0Fk41ompxORslIBEhH5C5sSzzFt2T72nkwHoL6/J+N7t+D2NsE46U7tIlWSCpCIyGUcPpPJ9OUHWLUvFQBvdxeevK0pw24OxcPV2eR0InItVIBERP7kfFYe761J4PPNRymwGTg7WRjUMYQx0c2p4+1udjwRKQcqQCIi/19uQSELNh5h1tpDZOQUANAtPICJfcMJC/AxOZ2IlCcVIBFxeIZhELsnmTdWHOD4+QsARNTzZVK/CG4Oq2NyOhGpCKZepWv69OlERkbi4+NDQEAA/fv3Jz4+vuj18+fPM2rUKFq0aIGnpycNGzZk9OjRWK3WK6536NChWCyWYo/evXtX9OaISBW04+hv3D1nI08t2sXx8xcI8HHnzXva8N2ozio/ItWYqWeA1q9fz8iRI4mMjKSgoICJEyfSs2dP9u3bR40aNTh16hSnTp1ixowZtGzZkqNHj/LEE09w6tQpvv766yuuu3fv3syfP7/oubu7vrcXkf9z/Hw2r684QOzuZAA8XZ15/NYmPNalCV5uOjkuUt1ZDMMwzA5x0ZkzZwgICGD9+vV06dKlxHm++uorHnzwQbKysnBxKfkfqaFDh5KWlsY333xzVTnS09Px8/PDarXi6+t7VesQEftkvZDPBz8cYv7PR8grtGGxwD03NOC5Xi0I9PUwO56IXIOyfH7b1Z85F7/aqlXr8hcVu7hRlys/F61bt46AgABq1qxJt27dmDp1KrVr1y5x3tzcXHJzc4uep6enX0V6EbFn+YU2Fm05xjurD/Jbdj4AN4fVZmLfCFoF+5mcTkQqm92cAbLZbNxxxx2kpaXx008/lTjP2bNnad++PQ8++CDTpk277Lq++OILvLy8CA0NJTExkYkTJ+Lt7c2mTZtwdr702h2TJ09mypQpl0zXGSCRqs8wDFbvP8305fs5fCYLgLAAb17sG0HXFnV1p3aRaqQsZ4DspgCNGDGC5cuX89NPP9GgQYNLXk9PT6dHjx7UqlWL//73v7i6upZ63YcPH6Zp06asXr2a7t27X/J6SWeAQkJCVIBEqri9J61Mjd3H5sPnAahdw41nejTnvsgQXJx1p3aR6qbKfQX21FNP8d1337Fhw4YSy09GRga9e/fGx8eHpUuXlqn8ADRp0oQ6depw6NChEguQu7u7BkmLVCPJ1gu8tTKepbtOYhjg5uLE8M6hPNm1KT4eZfv3Q0Sqp1IVoN27d5d6hW3atCn1vIZhMGrUKJYuXcq6desIDQ29ZJ709HR69eqFu7s7//3vf/HwKPsgxRMnTnDu3Dnq1atX5mVFpOrIyi1g7vpEPvrxMDn5NgDubBfMuF4taFDTy+R0ImJPSlWA2rVrh8Vi4XLfll18zWKxUFhYWOo3HzlyJIsWLeLbb7/Fx8eHlJQUAPz8/PD09CQ9PZ2ePXuSnZ3N559/Tnp6etEA5bp16xaN5wkPD2f69OkMGDCAzMxMpkyZwt13301QUBCJiYmMHz+esLAwevXqVepsIlJ1FNoMFm8/zj+/P8jZzN+/zo5sXJMX+7WkXYi/ueFExC6VqgAlJSVVyJvPmTMHgK5duxabPn/+fIYOHcrOnTvZsmULAGFhYZdkaty4MQDx8fFFvyBzdnZm9+7dLFiwgLS0NIKDg+nZsyevvvqqvuYSqYbWHzzDa7H7iU/NAKBRbS8m9AmnV6sgDXAWkcuym0HQ9kTXARKxf/EpGUxbtp8NB88A4OfpyujuzXjoxka4uWiAs4gjqvBB0J999hlz584lKSmJTZs20ahRI9555x1CQ0O58847ryq0iEhpnM7IYeaqg3y57Tg2A1ydLQyOasyobmH4e7mZHU9Eqogy/5k0Z84cxo4dS9++fUlLSysa8+Pv788777xT3vlERAC4kFfI7LUJ3PbWOv699ffy07tVEKueuZWX/tZS5UdEyqTMBWjWrFl89NFHvPjii8UuKtihQwf27NlTruFERGw2gyU7T9Dtn+uY8f1BsvIKadvAj8WPRzH3ofY0rlPD7IgiUgWV+SuwpKQkrr/++kumu7u7k5WVVS6hREQANh8+x7TY/ew5+fuPHOr7ezK+dwtubxOMk5MGOIvI1StzAQoNDSUuLo5GjRoVm75ixQoiIiLKLZiIOK7DZzKZvvwAq/alAuDt7sKTtzVl2M2heLheejsbEZGyKnMBGjt2LCNHjiQnJwfDMNi6dSv//ve/mT59Oh9//HFFZBQRB/FbVh7vrkng881HKbAZODtZGNQxhDHRzanjrctYiEj5KXMBeuSRR/D09GTSpElkZ2dz//33ExwczLvvvst9991XERlFpJrLLShkwcYjzFp7iIycAgC6hQcwsW84YQE+JqcTkeromq4DlJ2dTWZmJgEBAeWZyXS6DpBI5TAMg2V7Unh9xX6On78AQEQ9Xyb1i+DmsDompxORqqZSboZ6+vRp4uPjgd9vhVG3bt2rXZWIOKCdx35jWux+dhz9DYAAH3ee69WCu29ogLMGOItIBStzAcrIyODJJ5/k3//+Nzbb7zcbdHZ25t577+X999/Hz8+v3EOKSPVx/Hw2b6w4wHe7kwHwdHXm8Vub8FiXJni5XfXfZCIiZVLm6wA98sgjbNmyhdjYWNLS0khLS+O7775j+/btPP744xWRUUSqAeuFfKYv20/3f67nu93JWCzw9/YNWDeuK2Oim6v8iEilKvMYoBo1arBy5Uo6d+5cbPqPP/5I7969q8W1gDQGSKT85BfaWLTlGO+sPshv2fkA3BxWm4l9I2gVrDPGIlJ+KnQMUO3atUv8msvPz4+aNWuWdXUiUk0ZhsHq/aeZvnw/h8/8/odRWIA3E/uGc1uLAN2pXURMVeYCNGnSJMaOHctnn31GUFAQACkpKYwbN46XXnqp3AOKSNWz96SVqbH72Hz4PAC1a7gxpkdzBkWG4OKsO7WLiPlKVYCuv/76Yn+tJSQk0LBhQxo2bAjAsWPHcHd358yZMxoHJOLAkq0XeGtlPEt3ncQwwM3FieGdQxnRtSm+Hq5mxxMRKVKqAtS/f/8KjiEiVVlWbgFz1yfy0Y+Hycn//dehd7YLZlyvFjSo6WVyOhGRS13ThRCrKw2CFimdQpvBV9uPM+P7g5zNzAWgQ6OaTPpbS9qF+JsbTkQcTqVcCFFEHNuGg2d4bdl+DqRkANCothcv9A6n93VBGuAsInavzAWosLCQmTNnsnjxYo4dO0ZeXl6x18+fP19u4UTE/hxMzWBa7H7WHzwDgJ+nK6O6hTE4qjFuLhrgLCJVQ5n/tZoyZQpvv/029957L1arlbFjx3LXXXfh5OTE5MmTKyCiiNiDMxm5TFiyh97vbGD9wTO4OlsY3jmU9eO68sgtTVR+RKRKKfMYoKZNm/Lee+/Rr18/fHx8iIuLK5q2efNmFi1aVFFZK43GAIn8n5z8Qj7+8TBz1iWSlVcIQO9WQbzQJ5zGdWqYnE5E5P9U6BiglJQUWrduDYC3tzdWqxWAv/3tb7oOkEg1YrMZfBN3krdWxpNszQGgbQM/XuzXko6htUxOJyJybcpcgBo0aEBycjINGzakadOmfP/999xwww1s27YNd3f3isgoIpVs8+FzTIvdz56Tv/+BU9/fk/G9W3B7m2CcdKd2EakGylyABgwYwJo1a+jUqROjRo3iwQcf5JNPPuHYsWM888wzFZFRRCrJ4TOZvL78AN/vSwXA292FJ29ryrCbQ/FwdTY5nYhI+bnm6wBt3ryZjRs30qxZM26//fbyymUqjQESR/NbVh7vrkng881HKbAZODtZGNQxhDHRzanjrTO7IlI1lOXzu9wuhHj69Gk+/vhjJk6cWB6rM5UKkDiK3IJCPt14lFlrE0jPKQCgW3gAE/qE0yzQx+R0IiJlY0oB+uWXX7jhhhsoLCwsj9WZSgVIqjvDMFi2J4XXV+zn+PkLAIQH+TCpX0s6N6tjcjoRkaujK0GLyGXtPPYb02L3s+PobwAE+LjzXM8W3N2+Ac4a4CwiDkIFSMRBHD+fzRsrDvDd7mQAPF2deaxLEx7r0oQa7vqnQEQci/7VE6nmrBfy+eCHQ8z/+Qh5hTYsFrjnhgY827MFQX4eZscTETFFqQvQ2LFjr/j6mTNnrjmMiJSf/EIbi7Yc453VB/ktOx+Am8NqM7FvBK2C/UxOJyJirlIXoF27dv3lPF26dCnTm0+fPp0lS5Zw4MABPD09uemmm3jjjTdo0aJF0Tw5OTk8++yzfPHFF+Tm5tKrVy8++OADAgMDL7tewzB45ZVX+Oijj0hLS+Pmm29mzpw5NGvWrEz5RKoiwzBYvf8005fv5/CZLADCAryZ2Dec21oE6E7tIiKU46/Arkbv3r257777iIyMpKCggIkTJ7J371727dtHjRq/32NoxIgRxMbGEhMTg5+fH0899RROTk78/PPPl13vG2+8wfTp01mwYAGhoaG89NJL7Nmzh3379uHh8den/PUrMKmq9p60Mi12P5sOnwOgdg03xvRozqDIEFycdbNSEaneTPkZfHk4c+YMAQEBrF+/ni5dumC1Wqlbty6LFi3innvuAeDAgQNERESwadMmbrzxxkvWYRgGwcHBPPvsszz33HMAWK1WAgMDiYmJ4b777vvLHCpAUtWkWHN4a2U8S3adwDDAzcWJ4Z1DGdG1Kb4ermbHExGpFFX2Z/AXb6xaq9bvN1rcsWMH+fn5REdHF80THh5Ow4YNL1uAkpKSSElJKbaMn58fnTp1YtOmTSUWoNzcXHJzc4uep6enl9s2iVS0PSes3P/RZjJyf7+Q4Z3tghnXqwUNanqZnExExH7ZTQGy2WyMGTOGm2++meuuuw74/c7zbm5u+Pv7F5s3MDCQlJSUEtdzcfqfxwhdaZnp06czZcqUa9wCkcp39FwWD8dsJSO3gDYN/PjHndfRLsTf7FgiInbPbgYFjBw5kr179/LFF19U+ntPmDABq9Va9Dh+/HilZxApq7OZuQyZt5WzmXm0rOfLwkc6qfyIiJSSXRSgp556iu+++44ffviBBg0aFE0PCgoiLy+PtLS0YvOnpqYSFBRU4rouTk9NTS31Mu7u7vj6+hZ7iNizrNwChsds48i5bBrU9CRmWCQ+GusjIlJqZS5AK1as4Keffip6/v7779OuXTvuv/9+fvvttzKtyzAMnnrqKZYuXcratWsJDQ0t9nr79u1xdXVlzZo1RdPi4+M5duwYUVFRJa4zNDSUoKCgYsukp6ezZcuWyy4jUpXkF9oYuWgnv5ywUtPLlU+HdSTARxc0FBEpizIXoHHjxhUNEt6zZw/PPvssffv2JSkp6S8vlvhnI0eO5PPPP2fRokX4+PiQkpJCSkoKFy78fnNGPz8/hg8fztixY/nhhx/YsWMHDz/8MFFRUcUGQIeHh7N06VIALBYLY8aMYerUqfz3v/9lz549DB48mODgYPr371/WzRWxK4ZhMGHJHtbFn8HD1Yl5QyNpUtfb7FgiIlVOmQdBJyUl0bJlSwD+85//8Le//Y3XXnuNnTt30rdv3zKta86cOQB07dq12PT58+czdOhQAGbOnImTkxN33313sQsh/lF8fHzRL8gAxo8fT1ZWFo899hhpaWl07tyZFStWlOoaQCL27J/fH+TrHSdwdrLw/v03cH3DmmZHEhGpksp8HaBatWrx008/0bJlSzp37szgwYN57LHHOHLkCC1btiQ7O7uislYaXQdI7NFnm4/y0jd7AXj9rtbc17GhyYlEROxLhV4HqHPnzowdO5abb76ZrVu38uWXXwJw8ODBYgOYRaT8rNibwsvf/l5+nolurvIjInKNyjwGaPbs2bi4uPD1118zZ84c6tevD8Dy5cvp3bt3uQcUcXTbjpxn9Be7MAwY1LEho7uHmR1JRKTKs6tbYdgLfQUm9iIhNYO752wkPaeA6IhA5j54g+7pJSJyGeX+FVh6enrRiv7qNhEqDCLlI9l6gSHztpKeU8ANDf2ZNeh6lR8RkXJSqgJUs2ZNkpOTCQgIwN/fH4vFcsk8hmFgsVgoLCws95AijsZ6IZ+h87ZxyppD07o1+GRIJJ5uzmbHEhGpNkpVgNauXVt0g9K1a9eWWIBEpHzk5Bfy2KfbiU/NIMDHnQXDOlKzhpvZsUREqpVSFaBbb7216L//fM0eESk/NpvBs4t/YUvSeXzcXYh5uKPu6i4iUgHKPKBg8uTJ2Gy2S6ZbrVYGDRpULqFEHJFhGPzju33E7knG1dnChw+1p2WwxtSJiFSEMhegTz75hM6dO3P48OGiaevWraN169YkJiaWazgRR/LhhsPEbDwCwD8HtuOmsDrmBhIRqcbKXIB2795NgwYNaNeuHR999BHjxo2jZ8+ePPTQQ2zcuLEiMopUe0t2nuD15QcAmNQvgjvaBpucSESkeivzlaBr1qzJ4sWLmThxIo8//jguLi4sX76c7t27V0Q+kWpvw8EzjP96NwCP3hLKI7c0MTmRiEj1d1UXFZk1axbvvvsugwYNokmTJowePZpffvmlvLOJVHt7T1oZ8fkOCmwGd7YLZkKfCLMjiYg4hDIXoN69ezNlyhQWLFjAwoUL2bVrF126dOHGG2/kzTffrIiMItXSsXPZDJ2/lay8Qm4Oq81b97TFyUmXmBARqQxlLkCFhYXs3r2be+65BwBPT0/mzJnD119/zcyZM8s9oEh1dC4zl8HztnA2M4+W9XyZ+2B73Fx0lWcRkcpSrvcCO3v2LHXqVP1fruheYFKRsvMKGPSvzfxywkqDmp4sGXETAb4eZscSEanyyvL5Xa5/claH8iNSkfILbYxcuJNfTlip6eXKgmEdVX5ERExQ5l+BFRYWMnPmTBYvXsyxY8fIy8sr9vr58+fLLZxIdWIYBi8u3cMP8WfwcHXik6GRNK3rbXYsERGHVOYzQFOmTOHtt9/m3nvvxWq1MnbsWO666y6cnJyYPHlyBUQUqR7eXnWQxdtP4GSB2YNu4IaGNc2OJCLisMpcgBYuXMhHH33Es88+i4uLC4MGDeLjjz/m5ZdfZvPmzRWRUaTK+2zzUWatPQTAawNaE90y0OREIiKOrcwFKCUlhdatWwPg7e2N1WoF4G9/+xuxsbHlm06kGlixN4WXv90LwJjoZtzXsaHJiUREpMwFqEGDBiQnJwPQtGlTvv/+ewC2bduGu7t7+aYTqeK2HTnP6C92YRgwqGMIT3dvZnYkERHhKgrQgAEDWLNmDQCjRo3ipZdeolmzZgwePJhhw4aVe0CRqiohNYPhMdvIK7ARHRHIq3deh8WiCx2KiNiDa74O0KZNm9i0aRPNmjXj9ttvL69cptJ1gORaJVsvcPcHGzllzeGGhv4sfORGPN2czY4lIlKtleXzu8w/g/+zqKgooqKirnU1ItWG9UI+Q+dt45Q1hyZ1a/DJkEiVHxERO3NNF0L09fXl8OHD5ZVFpMrLyS/ksU+3E5+aQV0fdxY83JGaNdzMjiUiIn9S6gJ06tSpS6aV4100RKo8m83g2cW/sCXpPD7uLix4uCMhtbzMjiUiIiUodQFq1aoVixYtqsgsIlWWYRj847t9xO5JxtXZwocPtadlsMaPiYjYq1IXoGnTpvH444/z97//veh2Fw8++KAGCYsAH244TMzGIwD8c2A7bgrTffFEROxZqQvQk08+ye7duzl37hwtW7bkf//7H3PmzNENUMXhLdl5gteXHwBgUr8I7mgbbHIiERH5K2X6FVhoaChr165l9uzZ3HXXXURERODiUnwVO3fuLNeAIvZsw8EzjP96NwCP3hLKI7c0MTmRiIiURpl/Bn/06FGWLFlCzZo1ufPOOy8pQCKOYu9JKyM+30GBzeDOdsFM6BNhdiQRESmlMrWXizdBjY6O5tdff6Vu3brX9OYbNmzgrbfeYseOHSQnJ7N06VL69+9f9Prlrpr75ptvMm7cuBJfmzx5MlOmTCk2rUWLFhw4cOCasor80bFz2Qydv5WsvEJuDqvNW/e0xclJV3kWEakqSl2AevfuzdatW5k9ezaDBw8ulzfPysqibdu2DBs2jLvuuuuS1y/ec+yi5cuXM3z4cO6+++4rrrdVq1asXr266LnOUkl5OpeZy+B5WzibmUfLer7MfbA9bi7XdEktERGpZKVuBoWFhezevZsGDRqU25v36dOHPn36XPb1oKCgYs+//fZbbrvtNpo0ufI4CxcXl0uWFSkP2XkFDIvZxpFz2TSo6UnMw5H4eLiaHUtERMqo1AVo1apVFZnjL6WmphIbG8uCBQv+ct6EhASCg4Px8PAgKiqK6dOn07Bhw8vOn5ubS25ubtHz9PT0csks1Ut+oY2RC3fyywkrNb1cWTCsIwG+HmbHEhGRq1BlztsvWLAAHx+fEr8q+6NOnToRExPDihUrmDNnDklJSdxyyy1kZGRcdpnp06fj5+dX9AgJCSnv+FLFGYbBi0v38EP8GTxcnfhkaCRN63qbHUtERK7SNd8NvrxYLJZLBkH/UXh4OD169GDWrFllWm9aWhqNGjXi7bffZvjw4SXOU9IZoJCQEN0NXor88/t4Zq09hJMF/vVQB6JbBpodSURE/qRS7wZfGX788Ufi4+P58ssvy7ysv78/zZs359ChQ5edx93dHXd392uJKNXY55uPMmvt78fPawNaq/yIiFQDVeIrsE8++YT27dvTtm3bMi+bmZlJYmIi9erVq4BkUt2t/DWFl7/dC8CY6Gbc1/HyY8lERKTqMLUAZWZmEhcXR1xcHABJSUnExcVx7NixonnS09P56quveOSRR0pcR/fu3Zk9e3bR8+eee47169dz5MgRNm7cyIABA3B2dmbQoEEVui1S/Ww/cp7R/96FzYBBHUN4unszsyOJiEg5MfUrsO3bt3PbbbcVPR87diwAQ4YMISYmBoAvvvgCwzAuW2ASExM5e/Zs0fMTJ04waNAgzp07R926dencuTObN2++5os2imNJSM1g+ILt5BbYiI4I5NU7r7vshTlFRKTqsZtB0PakLIOopPpJseZw1wc/c8qaww0N/Vn4yI14ujmbHUtERP5CWT6/q8QYIJHKYr2Qz9D5WzllzaFJ3Rp8MiRS5UdEpBpSARL5/3ILCnn8s+0cSMmgro87Cx7uSM0abmbHEhGRCqACJALYbAZjF//C5sPn8XZ3IebhSEJqeZkdS0REKogKkDg8wzD4x3f7iN2djKuzhX891J5WwX5mxxIRkQqkAiQO78MNh4nZeASAfw5sx01hdcwNJCIiFU4FSBzakp0neH35AQAm9YvgjrbBJicSEZHKoAIkDmvDwTOM/3o3AI90DuWRW5qYnEhERCqLCpA4pL0nrYz4fAcFNoM72gYzsW+E2ZFERKQSqQCJwzl2Lpuh87eSlVfITU1r89bf2+DkpKs8i4g4EhUgcSjnMnMZPG8LZzPziKjny4cPtcfdRRc6FBFxNCpA4jCy8woYFrONI+eyqe/vyYKHI/HxcDU7loiImEAFSBxCfqGNkQt38ssJK/5ernw6vCMBvh5mxxIREZOoAEm1ZxgGLy7dww/xZ/BwdeKTIZE0rettdiwRETGRCpBUe2+vOsji7SdwssCsQTfQvlFNsyOJiIjJVICkWvt881FmrT0EwLQBrenRMtDkRCIiYg9UgKTaWvlrCi9/uxeAp7s3Y1DHhiYnEhERe6ECJNXS9iPnGf3vXdgMGNQxhDHRzcyOJCIidkQFSKqdhNQMhi/YTm6BjeiIAF698zosFl3oUERE/o8KkFQrKdYchszbivVCPtc39GfWoBtwcdZhLiIixemTQaoN64V8hs7fyilrDk3q1OCTIZF4uukqzyIicikVIKkWcgsKefyz7RxIyaCujzsLhnWkVg03s2OJiIidUgGSKs9mMxi7+Bc2Hz6Pt7sLMQ9HElLLy+xYIiJix1SApEozDINXY/cRuzsZV2cL/3qoPa2C/cyOJSIidk4FSKq0f204zPyfjwDwz4HtuCmsjrmBRESkSlABkipr6a4TTF9+AIBJ/SK4o22wyYlERKSqUAGSKunHhDOM+2o3AI90DuWRW5qYnEhERKoSFSCpcvaetPLEZzsosBnc0TaYiX0jzI4kIiJVjAqQVCnHzmUzdP42svIKualpbd76exucnHSVZxERKRsVIKkyzmXmMmT+Vs5m5hJRz5cPH2qPu4sudCgiImWnAiRVQnZeAcMWbCfpbBb1/T1Z8HAkPh6uZscSEZEqSgVI7F5BoY2nFu3il+Np+Hu58unwjgT4epgdS0REqjBTC9CGDRu4/fbbCQ4OxmKx8M033xR7fejQoVgslmKP3r17/+V633//fRo3boyHhwedOnVi69atFbQFUtEMw2Di0j2sPXAaD1cnPhkSSdO63mbHEhGRKs7UApSVlUXbtm15//33LztP7969SU5OLnr8+9//vuI6v/zyS8aOHcsrr7zCzp07adu2Lb169eL06dPlHV8qwdurDrJ4+wmcLDBr0A20b1TT7EgiIlINuJj55n369KFPnz5XnMfd3Z2goKBSr/Ptt9/m0Ucf5eGHHwZg7ty5xMbGMm/ePF544YVryiuV6/PNR5m19hAA0wa0pkfLQJMTiYhIdWH3Y4DWrVtHQEAALVq0YMSIEZw7d+6y8+bl5bFjxw6io6OLpjk5OREdHc2mTZsuu1xubi7p6enFHmKulb+m8PK3ewF4unszBnVsaHIiERGpTuy6APXu3ZtPP/2UNWvW8MYbb7B+/Xr69OlDYWFhifOfPXuWwsJCAgOLnykIDAwkJSXlsu8zffp0/Pz8ih4hISHluh1SNtuPnGf0v3dhM2BQxxDGRDczO5KIiFQzpn4F9lfuu+++ov9u3bo1bdq0oWnTpqxbt47u3buX2/tMmDCBsWPHFj1PT09XCTJJQmoGwxdsJ7fARnREAK/eeR0Wiy50KCIi5cuuzwD9WZMmTahTpw6HDh0q8fU6derg7OxMampqsempqalXHEfk7u6Or69vsYdUvhRrDkPmbcV6IZ/rG/oza9ANuDhXqUNURESqiCr16XLixAnOnTtHvXr1Snzdzc2N9u3bs2bNmqJpNpuNNWvWEBUVVVkx5SpYL+QzdP5WTllzaFKnBp8MicTTTVd5FhGRimFqAcrMzCQuLo64uDgAkpKSiIuL49ixY2RmZjJu3Dg2b97MkSNHWLNmDXfeeSdhYWH06tWraB3du3dn9uzZRc/Hjh3LRx99xIIFC9i/fz8jRowgKyur6FdhYn9yCwp5/LPtHEjJoK6POwuGdaRWDTezY4mISDVm6hig7du3c9tttxU9vzgOZ8iQIcyZM4fdu3ezYMEC0tLSCA4OpmfPnrz66qu4u7sXLZOYmMjZs2eLnt97772cOXOGl19+mZSUFNq1a8eKFSsuGRgt9sFmMxi7+Bc2Hz6Pt7sLMQ9HElLLy+xYIiJSzVkMwzDMDmFv0tPT8fPzw2q1ajxQBTIMg398t4/5Px/B1dlCzMMduTmsjtmxRESkiirL53eVGgMk1cu/Nhxm/s9HAJjx97YqPyIiUmlUgMQUS3edYPryAwC82DeCO9vVNzmRiIg4EhUgqXQ/Jpxh3Fe7ARjeOZRHuzQxOZGIiDgaFSCpVHtPWnnisx0U2AxubxvMi30jzI4kIiIOSAVIKs2xc9kMnb+NrLxCoprUZsbf2+DkpKs8i4hI5VMBkkpxLjOXIfO3cjYzl/AgHz4c3B53F13oUEREzKECJBUuO6+AYQu2k3Q2i/r+niwY1hFfD1ezY4mIiANTAZIKVVBo46lFu/jleBr+Xq4sGNaRQF8Ps2OJiIiDUwGSCmMYBhOX7mHtgdO4uzjxyZAOhAV4mx1LREREBUgqzsxVB1m8/QROFph9/w20b1TL7EgiIiKACpBUkIVbjvLe2kMATO3fmh4tdS82ERGxHypAUu5W/prCS9/sBWB092bc36mhyYlERESKUwGScrX9yHlG/3sXNgPuiwzhmehmZkcSERG5hAqQlJtDpzMYvmA7uQU2uocHMLX/dVgsutChiIjYHxUgKRep6TkMmbcN64V82oX4M+v+63Fx1uElIiL2SZ9Qcs3Sc/IZMm8rJ9Mu0KRODeYNjcTLzcXsWCIiIpelAiTXJLegkMc+3c6BlAzq+rizYFhHatVwMzuWiIjIFakAyVWz2QzGLv6FzYfP4+3uQszDkYTU8jI7loiIyF9SAZKrYhgGr8buI3Z3Mq7OFj58qD2tgv3MjiUiIlIqKkByVf614TDzfz4CwIy/t+XmsDrmBhIRESkDFSAps6W7TjB9+QEAXuwbwZ3t6pucSEREpGxUgKRMfkw4w7ivdgMwvHMoj3ZpYnIiERGRslMBklLbe9LKE5/toMBmcHvbYF7sG2F2JBERkauiAiSlcuxcNkPnbyMrr5CoJrWZ8fc2ODnpKs8iIlI1qQDJXzqXmcuQ+Vs5m5lLeJAPHw5uj7uLs9mxRERErpoKkFxRdl4BwxZsJ+lsFvX9PVkwrCO+Hq5mxxIREbkmKkByWQWFNp5atItfjqfh7+XKgmEdCfT1MDuWiIjINVMBkhIZhsHEpXtYe+A07i5OfDKkA2EB3mbHEhERKRcqQFKimasOsnj7CZwsMPv+G2jfqJbZkURERMqNCpBcYuGWo7y39hAAU/u3pkfLQJMTiYiIlC8VIClm5a8pvPTNXgBGd2/G/Z0ampxIRESk/JlagDZs2MDtt99OcHAwFouFb775pui1/Px8nn/+eVq3bk2NGjUIDg5m8ODBnDp16orrnDx5MhaLpdgjPDy8greketh+5Dyj/70LmwH3RYbwTHQzsyOJiIhUCFMLUFZWFm3btuX999+/5LXs7Gx27tzJSy+9xM6dO1myZAnx8fHccccdf7neVq1akZycXPT46aefKiJ+tXLodAbDF2wnt8BG9/AApva/DotFFzoUEZHqycXMN+/Tpw99+vQp8TU/Pz9WrVpVbNrs2bPp2LEjx44do2HDy3814+LiQlBQULlmrc5S03MYMm8b1gv5tAvxZ9b91+PirG9HRUSk+qpSn3JWqxWLxYK/v/8V50tISCA4OJgmTZrwwAMPcOzYsSvOn5ubS3p6erGHo0jPyWfIvK2cTLtAkzo1mDc0Ei83U3uxiIhIhasyBSgnJ4fnn3+eQYMG4evre9n5OnXqRExMDCtWrGDOnDkkJSVxyy23kJGRcdllpk+fjp+fX9EjJCSkIjbB7uQWFPLYp9s5kJJBXR93FgzrSK0abmbHEhERqXAWwzAMs0MAWCwWli5dSv/+/S95LT8/n7vvvpsTJ06wbt26KxagP0tLS6NRo0a8/fbbDB8+vMR5cnNzyc3NLXqenp5OSEgIVqu1TO9VldhsBqO+2EXs7mS83V344rEbua6+n9mxRERErlp6ejp+fn6l+vy2++868vPzGThwIEePHmXt2rVlLiT+/v40b96cQ4cOXXYed3d33N3drzVqlWEYBlNj9xO7OxlXZwtzH2yv8iMiIg7Frr8Cu1h+EhISWL16NbVr1y7zOjIzM0lMTKRevXoVkLBq+ujHw8z7OQmAGX9vS+dmdUxOJCIiUrlMLUCZmZnExcURFxcHQFJSEnFxcRw7doz8/Hzuuecetm/fzsKFCyksLCQlJYWUlBTy8vKK1tG9e3dmz55d9Py5555j/fr1HDlyhI0bNzJgwACcnZ0ZNGhQZW+eXfpm10leW3YAgIl9w7mzXX2TE4mIiFQ+U78C2759O7fddlvR87FjxwIwZMgQJk+ezH//+18A2rVrV2y5H374ga5duwKQmJjI2bNni147ceIEgwYN4ty5c9StW5fOnTuzefNm6tatW7EbUwX8lHCWcV//AsCwm0N59JYmJicSERExh90MgrYnZRlEVVXsPWnl3g83kZVXyN/a1OO9+67HyUkXOhQRkeqjLJ/fdj0GSMrH8fPZDJ2/jay8QqKa1OafA9uq/IiIiENTAarmzmflMXjeVs5m5hIe5MOHg9vj7uJsdiwRERFTqQBVY9l5BQyL2UbS2Szq+3uyYFhHfD1czY4lIiJiOhWgaqqg0MZTi3YRdzwNP09XFgyLJNDXw+xYIiIidkEFqBoyDIOJS/ew9sBp3F2cmDe0A2EBPmbHEhERsRsqQNXQzFUHWbz9BE4WmDXoeto3qmV2JBEREbuiAlTNLNxylPfW/n7bj1f7X0fPVkEmJxIREbE/KkDVyMpfU3jpm70AjO4WxgOdGpmcSERExD6pAFUT24+cZ/S/d2Ez4N4OITzTo7nZkUREROyWClA1cOh0BsMXbCe3wEa38ACmDbgOi0UXOhQREbkcFaAqLjU9hyHztmG9kE/bEH9m3389Ls76n1VERORK9ElZhaXn5DNk3lZOpl2gSZ0azB8aiZebqfe3FRERqRJUgKqo3IJCHvt0OwdSMqjr486CYR2pVcPN7FgiIiJVggpQFWSzGYxd/AubD5/H292F+UMjCanlZXYsERGRKkMFqIoxDIOpsfuJ3Z2Mq7OFuQ+257r6fmbHEhERqVJUgKqYj348zLyfkwCY8fe2dG5Wx+REIiIiVY8KUBXyza6TvLbsAAAT+4ZzZ7v6JicSERGpmlSAqoifEs4y7utfABh2cyiP3tLE5EQiIiJVlwpQFbD3pJXHP9tOfqHB39rUY1K/CF3oUERE5BqoANm54+ezGTp/G1l5hUQ1qc0/B7bFyUnlR0RE5FqoANmx81l5DJ63lbOZuYQH+fDh4Pa4uzibHUtERKTKUwGyU9l5BQyL2UbS2Szq+3uyYFhHfD1czY4lIiJSLagA2aGCQhujFu0i7ngafp6uLBgWSaCvh9mxREREqg0VIDtjGAYvLt3LmgOncXdxYt7QDoQF+JgdS0REpFpRAbIzM1cn8OX24zhZYNag62nfqJbZkURERKodFSA7snDLUd5bkwDAq/2vo2erIJMTiYiIVE8qQHbi+19TeOmbvQCM7hbGA50amZxIRESk+lIBsgM7jp5n1L93YTPg3g4hPNOjudmRREREqjUVIJMdOp3J8AXbyS2w0S08gGkDrtNVnkVERCqYCpCJUtNzGDJvK2nZ+bQN8Wf2/dfj4qz/SURERCqaPm1Nkp6Tz5B5WzmZdoHQOjWYN6QDXm4uZscSERFxCKYWoA0bNnD77bcTHByMxWLhm2++Kfa6YRi8/PLL1KtXD09PT6Kjo0lISPjL9b7//vs0btwYDw8POnXqxNatWytoC65ObkEhj3+6gwMpGdTxdufTYR2p7e1udiwRERGHYWoBysrKom3btrz//vslvv7mm2/y3nvvMXfuXLZs2UKNGjXo1asXOTk5l13nl19+ydixY3nllVfYuXMnbdu2pVevXpw+fbqiNqNMbDaDZxf/wqbD56jh5kzMw5GE1PIyO5aIiIhDsRiGYZgdAsBisbB06VL69+8P/H72Jzg4mGeffZbnnnsOAKvVSmBgIDExMdx3330lrqdTp05ERkYye/ZsAGw2GyEhIYwaNYoXXnihVFnS09Px8/PDarXi6+t77Rv3/xmGwavf7Wfez0m4OFmY/3AktzSrW27rFxERcWRl+fy22zFASUlJpKSkEB0dXTTNz8+PTp06sWnTphKXycvLY8eOHcWWcXJyIjo6+rLLAOTm5pKenl7sURE++vEw835OAmDG39uq/IiIiJjEbgtQSkoKAIGBgcWmBwYGFr32Z2fPnqWwsLBMywBMnz4dPz+/okdISMg1pi9ZHW93XJwsTOgTTv/r61fIe4iIiMhf08+OgAkTJjB27Nii5+np6RVSgu66oQFtGvjTtG6Ncl+3iIiIlJ7dFqCgoN/vg5Wamkq9evWKpqemptKuXbsSl6lTpw7Ozs6kpqYWm56amlq0vpK4u7vj7l45v8IKC/CulPcRERGRy7Pbr8BCQ0MJCgpizZo1RdPS09PZsmULUVFRJS7j5uZG+/btiy1js9lYs2bNZZcRERERx2PqGaDMzEwOHTpU9DwpKYm4uDhq1apFw4YNGTNmDFOnTqVZs2aEhoby0ksvERwcXPRLMYDu3bszYMAAnnrqKQDGjh3LkCFD6NChAx07duSdd94hKyuLhx9+uLI3T0REROyUqQVo+/bt3HbbbUXPL47DGTJkCDExMYwfP56srCwee+wx0tLS6Ny5MytWrMDDw6NomcTERM6ePVv0/N577+XMmTO8/PLLpKSk0K5dO1asWHHJwGgRERFxXHZzHSB7UlHXARIREZGKUy2uAyQiIiJSUVSARERExOGoAImIiIjDUQESERERh6MCJCIiIg5HBUhEREQcjgqQiIiIOBwVIBEREXE4KkAiIiLicOz2bvBmunhx7PT0dJOTiIiISGld/NwuzU0uVIBKkJGRAUBISIjJSURERKSsMjIy8PPzu+I8uhdYCWw2G6dOncLHxweLxVKu605PTyckJITjx4/rPmN/Qfuq9LSvSk/7qvS0r0pP+6r0KnJfGYZBRkYGwcHBODldeZSPzgCVwMnJiQYNGlToe/j6+ur/JKWkfVV62lelp31VetpXpad9VXoVta/+6szPRRoELSIiIg5HBUhEREQcjgpQJXN3d+eVV17B3d3d7Ch2T/uq9LSvSk/7qvS0r0pP+6r07GVfaRC0iIiIOBydARIRERGHowIkIiIiDkcFSERERByOCpCIiIg4HBWgcrRhwwZuv/12goODsVgsfPPNN3+5zLp167jhhhtwd3cnLCyMmJiYCs9pD8q6r9atW4fFYrnkkZKSUjmBTTR9+nQiIyPx8fEhICCA/v37Ex8f/5fLffXVV4SHh+Ph4UHr1q1ZtmxZJaQ119Xsq5iYmEuOKw8Pj0pKbJ45c+bQpk2boovRRUVFsXz58isu44jHFJR9XznqMVWS119/HYvFwpgxY644nxnHlgpQOcrKyqJt27a8//77pZo/KSmJfv36cdtttxEXF8eYMWN45JFHWLlyZQUnNV9Z99VF8fHxJCcnFz0CAgIqKKH9WL9+PSNHjmTz5s2sWrWK/Px8evbsSVZW1mWX2bhxI4MGDWL48OHs2rWL/v37079/f/bu3VuJySvf1ewr+P2KtH88ro4ePVpJic3ToEEDXn/9dXbs2MH27dvp1q0bd955J7/++muJ8zvqMQVl31fgmMfUn23bto0PP/yQNm3aXHE+044tQyoEYCxduvSK84wfP95o1apVsWn33nuv0atXrwpMZn9Ks69++OEHAzB+++23Sslkz06fPm0Axvr16y87z8CBA41+/foVm9apUyfj8ccfr+h4dqU0+2r+/PmGn59f5YWyYzVr1jQ+/vjjEl/TMVXclfaVjinDyMjIMJo1a2asWrXKuPXWW42nn376svOadWzpDJCJNm3aRHR0dLFpvXr1YtOmTSYlsn/t2rWjXr169OjRg59//tnsOKawWq0A1KpV67Lz6Nj6XWn2FUBmZiaNGjUiJCTkL/+yr44KCwv54osvyMrKIioqqsR5dEz9rjT7CnRMjRw5kn79+l1yzJTErGNLN0M1UUpKCoGBgcWmBQYGkp6ezoULF/D09DQpmf2pV68ec+fOpUOHDuTm5vLxxx/TtWtXtmzZwg033GB2vEpjs9kYM2YMN998M9ddd91l57vcseUIY6YuKu2+atGiBfPmzaNNmzZYrVZmzJjBTTfdxK+//lrhN0U22549e4iKiiInJwdvb2+WLl1Ky5YtS5zX0Y+psuwrRz6mAL744gt27tzJtm3bSjW/WceWCpBUCS1atKBFixZFz2+66SYSExOZOXMmn332mYnJKtfIkSPZu3cvP/30k9lR7F5p91VUVFSxv+RvuukmIiIi+PDDD3n11VcrOqapWrRoQVxcHFarla+//pohQ4awfv36y36wO7Ky7CtHPqaOHz/O008/zapVq+x+4LcKkImCgoJITU0tNi01NRVfX1+d/SmFjh07OlQReOqpp/juu+/YsGHDX/4VebljKygoqCIj2o2y7Ks/c3V15frrr+fQoUMVlM5+uLm5ERYWBkD79u3Ztm0b7777Lh9++OEl8zr6MVWWffVnjnRM7dixg9OnTxc7M19YWMiGDRuYPXs2ubm5ODs7F1vGrGNLY4BMFBUVxZo1a4pNW7Vq1RW/V5b/ExcXR7169cyOUeEMw+Cpp55i6dKlrF27ltDQ0L9cxlGPravZV39WWFjInj17HOLY+jObzUZubm6JrznqMXU5V9pXf+ZIx1T37t3Zs2cPcXFxRY8OHTrwwAMPEBcXd0n5AROPrQodYu1gMjIyjF27dhm7du0yAOPtt982du3aZRw9etQwDMN44YUXjIceeqho/sOHDxteXl7GuHHjjP379xvvv/++4ezsbKxYscKsTag0Zd1XM2fONL755hsjISHB2LNnj/H0008bTk5OxurVq83ahEozYsQIw8/Pz1i3bp2RnJxc9MjOzi6a56GHHjJeeOGFouc///yz4eLiYsyYMcPYv3+/8corrxiurq7Gnj17zNiESnM1+2rKlCnGypUrjcTERGPHjh3GfffdZ3h4eBi//vqrGZtQaV544QVj/fr1RlJSkrF7927jhRdeMCwWi/H9998bhqFj6o/Kuq8c9Zi6nD//Csxeji0VoHJ08afaf34MGTLEMAzDGDJkiHHrrbdesky7du0MNzc3o0mTJsb8+fMrPbcZyrqv3njjDaNp06aGh4eHUatWLaNr167G2rVrzQlfyUraT0CxY+XWW28t2ncXLV682GjevLnh5uZmtGrVyoiNja3c4Ca4mn01ZswYo2HDhoabm5sRGBho9O3b19i5c2flh69kw4YNMxo1amS4ubkZdevWNbp37170gW4YOqb+qKz7ylGPqcv5cwGyl2PLYhiGUbHnmERERETsi8YAiYiIiMNRARIRERGHowIkIiIiDkcFSERERByOCpCIiIg4HBUgERERcTgqQCIiIuJwVIBEpFpYt24dFouFtLQ0U95/8uTJtGvX7prWceTIESwWC3FxceWSSUQuTwVIRMpNYWEhN910E3fddVex6VarlZCQEF588cUKe++bbrqJ5ORk/Pz8rmr5rl27MmbMmPINJSJ2SwVIRMqNs7MzMTExrFixgoULFxZNHzVqFLVq1eKVV16psPd2c3MjKCgIi8VSYe8hItWHCpCIlKvmzZvz+uuvM2rUKJKTk/n222/54osv+PTTT3Fzc7vscs8//zzNmzfHy8uLJk2a8NJLL5Gfnw/8fpf36OhoevXqxcW795w/f54GDRrw8ssvA5d+BXb06FFuv/12atasSY0aNWjVqhXLli276u26Ur4/+vDDDwkJCcHLy4uBAwditVqLvf7xxx8TERGBh4cH4eHhfPDBB1edSUSunovZAUSk+hk1ahRLly7loYceYs+ePbz88su0bdv2isv4+PgQExNDcHAwe/bs4dFHH8XHx4fx48djsVhYsGABrVu35r333uPpp5/miSeeoH79+kUF6M9GjhxJXl4eGzZsoEaNGuzbtw9vb++r3qYr5bvo0KFDLF68mP/973+kp6czfPhwnnzyyaKzYQsXLuTll19m9uzZXH/99ezatYtHH32UGjVqMGTIkKvOJiJXocJvtyoiDmn//v0GYLRu3drIz88v8/JvvfWW0b59+2LTFi9ebHh4eBgvvPCCUaNGDePgwYNFr/3www8GYPz222+GYRhG69atjcmTJ5f6/f58x+qy5nvllVcMZ2dn48SJE0XTli9fbjg5ORnJycmGYRhG06ZNjUWLFhVbz6uvvmpERUUZhmEYSUlJBmDs2rWr1DlE5OroDJCIVIh58+bh5eVFUlISJ06coHHjxgA88cQTfP7550XzZWZmAvDll1/y3nvvkZiYSGZmJgUFBfj6+hZb59///neWLl3K66+/zpw5c2jWrNll33/06NGMGDGC77//nujoaO6++27atGlz1dtTmnwNGzakfv36Rc+joqKw2WzEx8fj4+NDYmIiw4cP59FHHy2ap6Cg4KoHbovI1dMYIBEpdxs3bmTmzJl89913dOzYkeHDhxeN3fnHP/5BXFxc0QNg06ZNPPDAA/Tt25fvvvuOXbt28eKLL5KXl1dsvdnZ2ezYsQNnZ2cSEhKumOGRRx7h8OHDRV/DdejQgVmzZl3V9pQ235VcLHofffRRse3fu3cvmzdvvqpcInL1dAZIRMpVdnY2Q4cOZcSIEdx2222EhobSunVr5s6dy4gRIwgICCAgIKDYMhs3bqRRo0bFfiZ/9OjRS9b97LPP4uTkxPLly+nbty/9+vWjW7dul80SEhLCE088wRNPPMGECRP46KOPGDVqVJm3qbT5jh07xqlTpwgODgZg8+bNODk50aJFCwIDAwkODubw4cM88MADZc4gIuVLBUhEytWECRMwDIPXX38dgMaNGzNjxgyee+45+vTpU/RV2B81a9aMY8eO8cUXXxAZGUlsbCxLly4tNk9sbCzz5s1j06ZN3HDDDYwbN44hQ4awe/duataseck6x4wZQ58+fWjevDm//fYbP/zwAxEREVfMfubMmUsuQlivXr1S5QPw8PBgyJAhzJgxg/T0dEaPHs3AgQMJCgoCYMqUKYwePRo/Pz969+5Nbm4u27dv57fffmPs2LFXzCYi5czsQUgiUn2sW7fOcHZ2Nn788cdLXuvZs6fRrVs3w2azlbjsuHHjjNq1axve3t7Gvffea8ycOdPw8/MzDMMwTp8+bQQGBhqvvfZa0fx5eXlG+/btjYEDBxqGcekg6Keeespo2rSp4e7ubtStW9d46KGHjLNnz142+6233moAlzxeffXVv8xnGL8Pgm7btq3xwQcfGMHBwYaHh4dxzz33GOfPny/2PgsXLjTatWtnuLm5GTVr1jS6dOliLFmyxDAMDYIWqUwWw/j/X8yLiIiIOAgNghYRERGHowIkIiIiDkcFSERERByOCpCIiIg4HBUgERERcTgqQCIiIuJwVIBERETE4agAiYiIiMNRARIRERGHowIkIiIiDkcFSERERByOCpCIiIg4nP8Hbhu552cY6PUAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#understanding sns\n",
        "\"\"\"import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "# Example dataset\n",
        "data = pd.DataFrame({\n",
        "    'x': [1, 2, 3, 4],\n",
        "    'y': [10, 20, 25, 30],\n",
        "    'category': ['A', 'B', 'C', 'B']\n",
        "})\n",
        "\n",
        "# Simple scatter plot\n",
        "sns.scatterplot(x='x', y='y', hue='category', data=data)\n",
        "plt.xlabel('X-axis Label')\n",
        "plt.ylabel('Y-axis Label')\n",
        "plt.title('Simple Scatter Plot')\n",
        "plt.show()\n",
        "\"\""
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "5RgNvRjtGNZg",
        "outputId": "7d61f1a4-1e15-4d5b-cc22-6c7c56284b13"
      },
      "execution_count": 36,
      "outputs": [
        {
          "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": [
        "sns.countplot(x='Gender',data=df_copy,hue='Gender')\n",
        "plt.xlabel('Gender')\n",
        "plt.ylabel('Count')\n",
        "plt.title('Gender Distribution')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "b86nYl1lHJv3",
        "outputId": "c197d84c-eadc-4a73-dae8-87dee25c7e44"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Age Plot**"
      ],
      "metadata": {
        "id": "yLInQBqmlkqW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(x=df_copy['Age'], bins=10, orientation='vertical', color='red')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Count')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "dYFUXxExlXuS",
        "outputId": "cdfe66ad-bf52-479b-b53b-1ad06c15d9d8"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Age Vs Spending Score**"
      ],
      "metadata": {
        "id": "byyAp4YWnYYc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualising the columns 'Age', 'Spending Score (1-100)' using Scatterplot and Jointplot\n",
        "sns.scatterplot(data=df_copy, x='Age', y='Spending Score (1-100)', hue='Gender')\n",
        "sns.jointplot(data=df_copy, x='Age', y='Spending Score (1-100)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "T5FzsNCEna15",
        "outputId": "87c835a4-f069-4ca4-e331-4f898545bc51"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7bcef53c89a0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Annual Income Vs Spending Score**\n"
      ],
      "metadata": {
        "id": "ZWAejrrmoOW8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualising the columns 'Annual Income (k$)', 'Spending Score (1-100)' using Scatterplot and Jointplot\n",
        "sns.scatterplot(data=df_copy, x='Annual Income (k$)', y='Spending Score (1-100)', hue='Gender')\n",
        "sns.jointplot(data=df_copy, x='Annual Income (k$)', y='Spending Score (1-100)')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "xhfI3lPXoKrg",
        "outputId": "38df118a-c2f6-43ed-ac8c-d83956937174"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.JointGrid at 0x7bcef5075930>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 600x600 with 3 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Data Preprocessing"
      ],
      "metadata": {
        "id": "6U3wqEF8ohvr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "#selecting 'annual income' and 'spending score' as the features for clustering\n",
        "X = df_copy.iloc[:,[2,3]]"
      ],
      "metadata": {
        "id": "zHYzwaPqojcl"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JqupCMqppDmT",
        "outputId": "9f6e6d4a-ac50-4791-9194-98ddd787861b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Annual Income (k$)', 'Spending Score (1-100)'], dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Finding optimal number of clusters using Elbow Method"
      ],
      "metadata": {
        "id": "_ko1A4GIpG8Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.cluster import KMeans\n",
        "wcss=[]\n",
        "for i in range(1,11):\n",
        "    kmeans_model=KMeans(n_clusters=i,init='k-means++',random_state=42)\n",
        "    kmeans_model.fit(X)\n",
        "    wcss.append(kmeans_model.inertia_)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lmJz5lZpS5t",
        "outputId": "7f1ccd62-7e9c-4541-bfe8-81849311c6ad"
      },
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n",
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(range(1,11),wcss)\n",
        "plt.title('The Elbow Method')\n",
        "plt.xlabel('Number of clusters')\n",
        "plt.ylabel('WCSS')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "x76DGnALrI4W",
        "outputId": "071e51a1-ed72-40c5-eb1c-d725d98a2085"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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IDFWNgIu9CkM7NQEArObzAImIiGqFrKFq/vz5ePTRR+Hk5AQvLy8MHToUSUlJRjV9+/aFQqEwWqZMmWJUk5aWhsGDB8Pe3h5eXl6YNWsWysvLjWpiYmLQpUsXqNVqtGrVCitXrqzSz9KlS9GsWTPY2toiLCwMR48eNdpfXFyMqKgouLu7w9HREc8++yyysrLM88uoZS/enrC+/UwGsnXFMndDRETU8Mgaqvbt24eoqCgcOXIE0dHRKCsrw4ABA1BQYHxPpYkTJyIjI0NaFi1aJO3T6/UYPHgwSktLcfjwYaxatQorV67E3LlzpZqUlBQMHjwY/fr1Q3x8PKZNm4aXX34Zu3btkmrWr1+PGTNmYN68efj999/RsWNHREZGIjs7W6qZPn06tm7dio0bN2Lfvn1IT0/HsGHDavE3ZD7BTZwRGuiKcoPA2qN8HiAREZHZCQuSnZ0tAIh9+/ZJ2/r06SNef/31u75m+/btQqlUiszMTGnbsmXLhEajESUlJUIIId58803RoUMHo9eNHDlSREZGSuvdunUTUVFR0rperxd+fn5i/vz5QgghcnNzhY2Njdi4caNUk5iYKACI2NhYkz6fVqsVAIRWqzWp3tx+OnlVBM7eJrp9FC1Ky/Wy9EBERFTfmPr9bVFzqrRaLQDAzc3NaPsPP/wADw8PBAcHY86cOSgsLJT2xcbGIiQkBN7e3tK2yMhI6HQ6JCQkSDURERFGx4yMjERsbCwAoLS0FCdOnDCqUSqViIiIkGpOnDiBsrIyo5qgoCAEBARINZZuULAvPBzVyNKV4NeE+nHakoiIqL6wlruBSgaDAdOmTUOPHj0QHBwsbR89ejQCAwPh5+eH06dPY/bs2UhKSsKmTZsAAJmZmUaBCoC0npmZec8anU6HoqIi3Lp1C3q9vtqa8+fPS8dQqVRwcXGpUlP5Pn9VUlKCkpISaV2n05n666gVKmslRnfzx5I9F7Aq9jIGP+Iraz9EREQNicWEqqioKJw9exYHDx402j5p0iTp55CQEPj6+qJ///64ePEiWrZsWddt1sj8+fPx3nvvyd2GkdFhgVgacxFHU27ifKYOQT4auVsiIiJqECzi9N/UqVOxbds27N27F02bNr1nbVhYGADgwoULAAAfH58qV+BVrvv4+NyzRqPRwM7ODh4eHrCysqq25s5jlJaWIjc39641fzVnzhxotVppuXJF/gniPs62iOxQMSLH2ysQERGZj6yhSgiBqVOnYvPmzdizZw+aN29+39fEx8cDAHx9K05dhYeH48yZM0ZX6UVHR0Oj0aB9+/ZSze7du42OEx0djfDwcACASqVCaGioUY3BYMDu3bulmtDQUNjY2BjVJCUlIS0tTar5K7VaDY1GY7RYgrHhzQAAm3+/Bm1RmbzNEBERNRR1M2++eq+88opwdnYWMTExIiMjQ1oKCwuFEEJcuHBBvP/+++L48eMiJSVFbNmyRbRo0UL07t1bOkZ5ebkIDg4WAwYMEPHx8WLnzp3C09NTzJkzR6q5dOmSsLe3F7NmzRKJiYli6dKlwsrKSuzcuVOqWbdunVCr1WLlypXi3LlzYtKkScLFxcXoqsIpU6aIgIAAsWfPHnH8+HERHh4uwsPDTf68cl/9V8lgMIgBn+4TgbO3if8cuCRrL0RERJbO1O9vWUMVgGqXFStWCCGESEtLE7179xZubm5CrVaLVq1aiVmzZlX5UJcvXxaDBg0SdnZ2wsPDQ8ycOVOUlZUZ1ezdu1d06tRJqFQq0aJFC+k97vTFF1+IgIAAoVKpRLdu3cSRI0eM9hcVFYlXX31VuLq6Cnt7e/HMM8+IjIwMkz+vpYQqIYT4LvayCJy9TfRdvFfo9Qa52yEiIrJYpn5/K4TgE3brik6ng7OzM7RareynAgtKyvHYx7uRV1KO1X/vht5tPGXth4iIyFKZ+v1tERPVqe45qK3xXNeKiwJWx16WtxkiIqIGgKGqEXvxsYrnAe4+n40rNwvvU01ERET3wlDViLXwdESv1h4QAvg+jrdXICIiehgMVY3cuNu3V1h/7AqKy/TyNkNERFSPMVQ1cv2CvNDExQ65hWX4+VS63O0QERHVWwxVjZyVUoEXwyvmVq2OvQxeDEpERPRgGKoII7v6Q22txNlrOpy8kit3O0RERPUSQxXB1UGFIR39AACrD1+WtxkiIqJ6iqGKAPw5YX37mUxczyuRtxkiIqJ6iKGKAAAhTZ3Ryd8FpXoD1h9Lk7sdIiKieoehiiTjuldMWP8hLg3leoPM3RAREdUvDFUk+VuIL9wdVMjQFuO3xCy52yEiIqpXGKpIora2wvPd/AEAqw7zDutEREQ1wVBFRsaEBUKpAGIv5eCPrDy52yEiIqo3GKrIiJ+LHQa09wEAfBfL0SoiIiJTMVRRFWNv32H9f79fha64TOZuiIiI6geGKqoivKU7Wnk5orBUj00nrsrdDhERUb3AUEVVKBQKjKt8HuCRVD4PkIiIyAQMVVStZ7o0haPaGpeuF+DQhRy52yEiIrJ4DFVULUe1NZ7t0gQAsDr2srzNEBER1QMMVXRXL95+HuBviVm4eqtQ3maIiIgsHEMV3VUrL0f0aOUOg6h4dA0RERHdHUMV3dPY26NV649dQXGZXt5miIiILBhDFd1T/yAvNHGxw82CUvxyOkPudoiIiCwWQxXdk7WVEqPDAgBwwjoREdG9MFTRfT3/qD9UVkqcuqpF/JVcudshIiKySAxVdF/ujmo8+YgvAI5WERER3Q1DFZlkbPdmAIBtpzKQk18ibzNEREQWiKGKTNLJ3wUdmzqjVG/A+uNX5G6HiIjI4jBUkckqbwb6w5E0lOsN8jZDRERkYRiqyGRPPuILV3sbXMstwu7z2XK3Q0REZFEYqshktjZWeL5bxe0VvotNlbkbIiIiy8JQRTUyJiwASgVw8MINXMjOk7sdIiIii8FQRTXS1NUe/dt5A+BoFRER0Z0YqqjGxt2esP6/368hv6Rc3maIiIgsBEMV1ViPVu5o4emA/JJybP79qtztEBERWQSGKqoxhUKBsY8FAgBWxaZCCCFzR0RERPJjqKIH8mxoUziorHAhOx+xl3LkboeIiEh2DFX0QJxsbfBMlyYAgNWHOWGdiIiIoYoe2NjbE9ajE7OQnlskbzNEREQyY6iiB9bG2wnhLdyhNwisiUuTux0iIiJZMVTRQxkbXjFhfe3RNJSU62XuhoiISD4MVfRQnmjvDV9nW+QUlGLHmUy52yEiIpINQxU9FGsrJUbffh7gqtjL8jZDREQkI4YqemjPdwuAjZUCJ9NyceaqVu52iIiIZMFQRQ/N00mNwSG+AIDVHK0iIqJGiqGKzOLF27dX2HIqHbcKSuVthoiISAYMVWQWXQJcENxEg9JyA9YfvyJ3O0RERHWOoYrMQqFQSDcD/f5IKvQGPg+QiIgaF4YqMpunOvrBxd4GV28VYe/5bLnbISIiqlMMVWQ2tjZWGNnVHwBvr0BERI0PQxWZ1QuPBUKhAA4k38Cl6/lyt0NERFRnGKrIrPzd7PF4Wy8AwHdHUmXuhoiIqO4wVJHZje3eDADw4/GrKCgpl7cZIiKiOsJQRWbXq5UHmns4IK+kHD/FX5O7HSIiojrBUEVmp1Qq8MJjgQCA1YdTIQRvr0BERA0fQxXViudCm8LOxgpJWXmIS7kpdztERES1TtZQNX/+fDz66KNwcnKCl5cXhg4diqSkJKOa4uJiREVFwd3dHY6Ojnj22WeRlZVlVJOWlobBgwfD3t4eXl5emDVrFsrLjefyxMTEoEuXLlCr1WjVqhVWrlxZpZ+lS5eiWbNmsLW1RVhYGI4ePVrjXqiCs50NnunSBADwXSwnrBMRUcMna6jat28foqKicOTIEURHR6OsrAwDBgxAQUGBVDN9+nRs3boVGzduxL59+5Ceno5hw4ZJ+/V6PQYPHozS0lIcPnwYq1atwsqVKzF37lypJiUlBYMHD0a/fv0QHx+PadOm4eWXX8auXbukmvXr12PGjBmYN28efv/9d3Ts2BGRkZHIzs42uRcyNja84hTgzoRMZGqLZe6GiIiolgkLkp2dLQCIffv2CSGEyM3NFTY2NmLjxo1STWJiogAgYmNjhRBCbN++XSiVSpGZmSnVLFu2TGg0GlFSUiKEEOLNN98UHTp0MHqvkSNHisjISGm9W7duIioqSlrX6/XCz89PzJ8/3+Re7ker1QoAQqvVmlTfEAz/+rAInL1N/OvXJLlbISIieiCmfn9b1JwqrVYLAHBzcwMAnDhxAmVlZYiIiJBqgoKCEBAQgNjYWABAbGwsQkJC4O3tLdVERkZCp9MhISFBqrnzGJU1lccoLS3FiRMnjGqUSiUiIiKkGlN6+auSkhLodDqjpbGpHK1aE5eG0nKDzN0QERHVHosJVQaDAdOmTUOPHj0QHBwMAMjMzIRKpYKLi4tRrbe3NzIzM6WaOwNV5f7Kffeq0el0KCoqwo0bN6DX66utufMY9+vlr+bPnw9nZ2dp8ff3N/G30XBEdvCBl5MaN/JLsONshtztEBER1RqLCVVRUVE4e/Ys1q1bJ3crZjNnzhxotVppuXLlitwt1TkbKyXGhFWMVnHCOhERNWQWEaqmTp2Kbdu2Ye/evWjatKm03cfHB6WlpcjNzTWqz8rKgo+Pj1Tz1yvwKtfvV6PRaGBnZwcPDw9YWVlVW3PnMe7Xy1+p1WpoNBqjpTEa1c0f1koFjqfewtlrWrnbISIiqhWyhiohBKZOnYrNmzdjz549aN68udH+0NBQ2NjYYPfu3dK2pKQkpKWlITw8HAAQHh6OM2fOGF2lFx0dDY1Gg/bt20s1dx6jsqbyGCqVCqGhoUY1BoMBu3fvlmpM6YWq56WxxaAQXwAcrSIiogasbubNV++VV14Rzs7OIiYmRmRkZEhLYWGhVDNlyhQREBAg9uzZI44fPy7Cw8NFeHi4tL+8vFwEBweLAQMGiPj4eLFz507h6ekp5syZI9VcunRJ2Nvbi1mzZonExESxdOlSYWVlJXbu3CnVrFu3TqjVarFy5Upx7tw5MWnSJOHi4mJ0VeH9ermfxnj1X6VjKTkicPY20fb/bRe3CkrkboeIiMhkpn5/yxqqAFS7rFixQqopKioSr776qnB1dRX29vbimWeeERkZGUbHuXz5shg0aJCws7MTHh4eYubMmaKsrMyoZu/evaJTp05CpVKJFi1aGL1HpS+++EIEBAQIlUolunXrJo4cOWK035Re7qUxhyqDwSAGfrZfBM7eJpbvuyh3O0RERCYz9ftbIQQfzFZXdDodnJ2dodVqG+X8qnVH0/DWpjMIcLNHzBt9oVQq5G6JiIjovkz9/raIierUODzdqQk0ttZIu1mIfX9cl7sdIiIis2Koojpjp7LCiK4V9+paFXtZ3maIiIjMjKGK6tQLjwVCoQBikq7j8o2C+7+AiIionmCoojrVzMMBfdt4AgC+P8LbKxARUcPBUEV1bmx4MwDAhuNXUFhaLm8zREREZsJQRXWuTxtPBLjZQ1dcji3x6XK3Q0REZBYMVVTnlEoFxoZXPA9wdWwqeFcPIiJqCBiqSBbDQ/1ha6NEYoYOx1Nvyd0OERHRQ2OoIlk429tgaKcmAIBVhy/L2wwREZEZMFSRbF68fQpw59lMZOuKZe6GiIjo4TBUkWw6+Dmja6Aryg0Ca46myd0OERHRQ2GoIlmN7d4MALAmLg1leoO8zRARET0EhiqS1cAOPvBwVCM7rwS7EjLlboeIiOiBMVSRrFTWSowOCwAArD7MO6wTEVH9xVBFshsTFgBrpQJHL99EYoZO7naIiIgeCEMVyc5bY4vIDj4AKm4GSkREVB8xVJFFqLzD+k8nr0FbVCZzN0RERDXHUEUWoVtzNwT5OKGoTI+Nx6/I3Q4REVGNMVSRRVAoFBh/+/YK/3fgEorL9PI2REREVEMMVWQxhnVpCj9nW2TpSrD+GEeriIiofmGoIouhslbilX6tAABfxVzgaBUREdUrDFVkUUZ0bQrf26NVGzi3ioiI6hGGKrIoamsrvNq3JQBgWcxFlJRztIqIiOoHhiqyOCMe9YePxhYZ2mJsOH5V7naIiIhMwlBFFkdtbYVXKker9l7gaBUREdULDFVkkUY+6g9vjRrp2mL8eIKjVUREZPkYqsgi2dpYYUqfitGqr/ZeRGm5QeaOiIiI7o2hiizWqG4B8HJS41puEUeriIjI4jFUkcW6c7Rq6d4LHK0iIiKLxlBFFm10WAA8b49Wbfqdo1VERGS5GKrIotnaWGFy7xYAgC/3XkCZnqNVRERkmRiqyOKNCQuEh6MaV28VYfPv1+Ruh4iIqFoMVWTx7FR/jlZ9sTeZo1VERGSRGKqoXhjzWAA8HFW4crMIm09ytIqIiCwPQxXVC/Yqa0y6PVq1dO8FlHO0ioiILAxDFdUbLzwWCHcHFVJzCvFTfLrc7RARERlhqKJ6w15ljYmVVwLuSeZoFRERWRSGKqpXXnwsEG4OKlzOKcTPpzhaRUREloOhiuoVB7U1Jva6fSXgHs6tIiIiy8FQRfXO2PBAuNrbIOVGAbae5mgVERFZBoYqqncc1NZ4+Y7RKr1ByNwRERERQxXVU+O6N4OLvQ0uXS/ANo5WERGRBWCoonrJUW2Nl3s2BwAs2Z3M0SoiIpIdQxXVW+O6N4OznQ0uXi/AL2cy5G6HiIgaOYYqqrecbG04WkVERBaDoYrqtXE9mkFja40L2fnYztEqIiKSEUMV1WsaWxu81LPySsBkGDhaRUREMmGoonpvfI9mcLK1xh9Z+dhxNlPudoiIqJFiqKJ6z9nOBn/v8efcKo5WERGRHB46VKWmpuLcuXMwGPi4EJLP33s0h5PaGklZediVwNEqIiKqeyaHqv/+97/49NNPjbZNmjQJLVq0QEhICIKDg3HlyhWzN0hkCmd7G0y4fSXg5xytIiIiGZgcqpYvXw5XV1dpfefOnVixYgVWr16NY8eOwcXFBe+9916tNElkipduj1adz8zDr+c4WkVERHXL5FCVnJyMrl27SutbtmzB008/jTFjxqBLly74+OOPsXv37lppksgUzvY2GN+jGQDg890XOFpFRER1yuRQVVRUBI1GI60fPnwYvXv3ltZbtGiBzEyODpC8XurZHI5qayRm6BCdmCV3O0RE1IiYHKoCAwNx4sQJAMCNGzeQkJCAHj16SPszMzPh7Oxs/g6JasDFXoVx3QMBVFwJKARHq4iIqG5Ym1o4btw4REVFISEhAXv27EFQUBBCQ0Ol/YcPH0ZwcHCtNElUEy/3bIGVhy4jIV2H3xKz8UR7b7lbIiKiRsDkkao333wTEydOxKZNm2Bra4uNGzca7T906BBGjRpVozffv38/hgwZAj8/PygUCvz0009G+8ePHw+FQmG0DBw40Kjm5s2bGDNmDDQaDVxcXPDSSy8hPz/fqOb06dPo1asXbG1t4e/vj0WLFlXpZePGjQgKCoKtrS1CQkKwfft2o/1CCMydOxe+vr6ws7NDREQEkpOTa/R5qW64OqgwrnszAMBnv/3B0SoiIqoTJocqpVKJ999/HydPnsSOHTvQrl07o/0bN27ESy+9VKM3LygoQMeOHbF06dK71gwcOBAZGRnSsnbtWqP9Y8aMQUJCAqKjo7Ft2zbs378fkyZNkvbrdDoMGDBAOn25ePFivPvuu1i+fLlUc/jwYYwaNQovvfQSTp48iaFDh2Lo0KE4e/asVLNo0SIsWbIEX3/9NeLi4uDg4IDIyEgUFxfX6DNT3Xi5VwvYq6yQkK7D7sRsudshIqLGQDyEoqIisXLlSrF06VKRnJz8MIcSAMTmzZuNto0bN048/fTTd33NuXPnBABx7NgxaduOHTuEQqEQ165dE0II8dVXXwlXV1dRUlIi1cyePVu0bdtWWh8xYoQYPHiw0bHDwsLE5MmThRBCGAwG4ePjIxYvXiztz83NFWq1Wqxdu9bkz6jVagUAodVqTX4NPbj52xNF4Oxt4sklB4TBYJC7HSIiqqdM/f42eaRqxowZ+Mc//iGtl5aWIjw8HBMnTsTbb7+NTp06ITY21uyhLyYmBl5eXmjbti1eeeUV5OTkSPtiY2Ph4uJidKuHiIgIKJVKxMXFSTW9e/eGSqWSaiIjI5GUlIRbt25JNREREUbvGxkZKX2elJQUZGZmGtU4OzsjLCzsnp+5pKQEOp3OaKG6M7FXc9jZWOHMNS32JnG0ioiIapfJoerXX3/FE088Ia3/8MMPSE1NRXJyMm7duoXhw4fjww8/NGtzAwcOxOrVq7F7924sXLgQ+/btw6BBg6DX6wFUXHHo5eVl9Bpra2u4ublJt3fIzMyEt7fxROXK9fvV3Ln/ztdVV1Od+fPnw9nZWVr8/f1r9Pnp4bg7qjE2vOJKwM9/45WARERUu0wOVWlpaWjfvr20/uuvv+K5555DYGAgFAoFXn/9dZw8edKszT3//PN46qmnEBISgqFDh2Lbtm04duwYYmJizPo+tWXOnDnQarXSwsf41L2JvVvAzsYKp65qEZN0Xe52iIioAavRRPU7/5/+kSNH8Nhjj0nrLi4u0um02tKiRQt4eHjgwoULAAAfHx9kZxuf1ikvL8fNmzfh4+Mj1WRlGd8EsnL9fjV37r/zddXVVEetVkOj0RgtVLc8HNV48fZo1We8bxUREdUik0NVu3btsHXrVgBAQkIC0tLS0K9fP2l/ampqldNj5nb16lXk5OTA19cXABAeHo7c3FzppqQAsGfPHhgMBoSFhUk1+/fvR1lZmVQTHR2Ntm3bSs8yDA8Pr/KInejoaISHhwMAmjdvDh8fH6ManU6HuLg4qYYs18ReLWBro8SpK7nY9wdHq4iIqJaYOvN906ZNQqVSiccff1x4e3uLJ5980mj/m2++KYYPH16j2fR5eXni5MmT4uTJkwKA+PTTT8XJkydFamqqyMvLE2+88YaIjY0VKSkp4rfffhNdunQRrVu3FsXFxdIxBg4cKDp37izi4uLEwYMHRevWrcWoUaOk/bm5ucLb21u8+OKL4uzZs2LdunXC3t5efPPNN1LNoUOHhLW1tfjkk09EYmKimDdvnrCxsRFnzpyRahYsWCBcXFzEli1bxOnTp8XTTz8tmjdvLoqKikz+vLz6Tz4fbE0QgbO3iaFLD/JKQCIiqhFTv79rdEuF3377TUybNk0sWLBAFBQUGO179913xd69e2vU5N69ewWAKsu4ceNEYWGhGDBggPD09BQ2NjYiMDBQTJw4UWRmZhodIycnR4waNUo4OjoKjUYjJkyYIPLy8oxqTp06JXr27CnUarVo0qSJWLBgQZVeNmzYINq0aSNUKpXo0KGD+OWXX4z2GwwG8c477whvb2+hVqtF//79RVJSUo0+L0OVfLJ0RaLNP7eLwNnbxL6kbLnbISKiesTU72+FEJxkUld0Oh2cnZ2h1Wo5v0oG7289h/8eSkFooCt+nBIOhUIhd0tERFQPmPr9bfKcquTkZIwaNaraey1ptVqMHj0aly5derBuierAlD4toLZW4kTqLRy8cEPudoiIqIExOVQtXrwY/v7+1Sa0ynswLV682KzNEZmTl8YWo8MCAPC+VUREZH4mh6p9+/Zh+PDhd90/YsQI7NmzxyxNEdWWKX1aQmWtxPHUWzh8Mef+LyAiIjJRjW7++de7l9/Jw8ODN7cki+etscXobhytIiIi8zM5VDk7O+PixYt33X/hwgVOvqZ6YUqfllBZKXH08k3EXuJoFRERmYfJoap379744osv7rp/yZIl6NWrl1maIqpNPs62eL5bxXMYP/8tWeZuiIiooTA5VM2ZMwc7duzAc889h6NHj0rPs4uLi8Ozzz6LXbt2Yc6cObXZK5HZvNK3YrQqLuUmYjm3ioiIzMDkUNW5c2f8+OOP2L9/P8LDw+Hm5gY3Nzd0794dBw4cwIYNG9ClS5fa7JXIbHyd7TDy0dujVbv/kLkbIiJqCKxNLUxJScGTTz6J1NRU7Nq1C8nJFZN827RpgwEDBsDe3r42+yQyu1f6tsS6Y2k4cukm4i7lIKyFu9wtERFRPWZyqGrZsiUCAwPRr18/9OvXD6NGjULTpk1rszeiWuXnYocRXf3xQ1waPt+djDUMVURE9BBMPv23Z88ejBs3DpcuXcKkSZMQGBiI1q1bY/LkyVi3bh2ysrJqs0+iWvFqv1awsVLg8MUcHLt8U+52iIioHnugZ/8VFxfj8OHDiImJQUxMDI4ePYqysjIEBQUhISGhNvpsEPjsP8s0Z9MZrD2ahp6tPPD9y2Fyt0NERBbG1O/vh3qgcmlpKQ4dOoQdO3bgm2++QX5+PvR6/YMersFjqLJMV28Vou/iGJQbBH6cEo6uzdzkbomIiCyI2R+oDFSEqP379+O9995Dv3794OLigilTpuDWrVv48ssvkZKS8tCNE9W1pq72GN61Yn7g57t53yoiInowJk9Uf/zxxxEXF4fmzZujT58+mDx5MtasWQNfX9/a7I+oTrzatxU2Hr+KA8k3cCL1FkIDXeVuiYiI6hmTR6oOHDgAd3d3PP744+jfvz+eeOIJBipqMPzd7PFsF45WERHRgzM5VOXm5mL58uWwt7fHwoUL4efnh5CQEEydOhU//vgjrl+/Xpt9EtW6qH6tYKVUYP8f13Ey7Zbc7RARUT3zwBPV8/LycPDgQezduxcxMTE4deoUWrdujbNnz5q7xwaDE9Ut36yNp7DxxFX0beuJlRO6yd0OERFZgFqZqH4nBwcH6VE1rq6usLa2RmJi4oMejsgiTH28YrQqJuk64q/kyt0OERHVIyaHKoPBgKNHj2LRokUYNGgQXFxc0L17d3z11Vfw8fHB0qVLcenSpdrslajWBbo74JnOTQAAn//GZwISEZHpTL76z8XFBQUFBfDx8UG/fv3w73//G3379kXLli1rsz+iOje1XytsPnkNe5Ou49SVXHT0d5G7JSIiqgdMDlWLFy9Gv3790KZNm9rsh0h2zTwc8HQnP2z6/RqW7E7Gf8Y/KndLRERUD5h8+m/y5MkMVNRo/OPx1lAqgN3ns3HmqlbudoiIqB544InqRA1Zcw8HPN3p9twq3reKiIhMwFBFdBdTH28FpQL4LTELZ69xtIqIiO6NoYroLlp6OuKpjn4AOFpFRET3x1BFdA9TH28NhQKIPpeFhHSOVhER0d0xVBHdQysvRwx5pGK0aglHq4iI6B4Yqoju47X+raBQALsSspCYoZO7HSIislAMVUT30crLCU9ytIqIiO6DoYrIBK89XjFateNsJkeriIioWgxVRCZo7e2Ev4X4AgC+2MPRKiIiqoqhishErz3eGgCw/UwmkjLzZO6GiIgsDUMVkYna+jjhbyE+AIAlHK0iIqK/YKgiqoHX+leOVmXgjyyOVhER0Z8YqohqIMhHg0HBPhCCVwISEZExhiqiGqocrfrlTAaSOVpFRES3MVQR1VA7Xw0iO3hDCOCLPRfkboeIiCwEQxXRA6gcrdp6Oh0XsvNl7oaIiCwBQxXRA+jg54wn2leMVn3JKwGJiAgMVUQP7PXbo1U/n0rHxescrSIiauwYqogeUHATZ0S084ZBAF9ybhURUaPHUEX0ECpHq7bEX8MljlYRETVqDFVEDyGkqTP6B3lVjFbt5WgVEVFjxlBF9JBej6gcrUrH5RsFMndDRERyYagiekiPNHVBv7ae0BsER6uIiBoxhioiM3g9og0AYPPJa0jN4WgVEVFjxFBFZAad/F3Qt3K0ilcCEhE1SgxVRGZSeSXgppPXkJZTKHM3RERU1xiqiMykc4ArerepGK1ayrlVRESNDkMVkRlVjlb97/eruHKTo1VERI0JQxWRGYUGuqJXaw+UGwTmbDqDgpJyuVsiIqI6wlBFZGZvRgbB1kaJgxdu4PnlR5CdVyx3S0REVAcYqojMLKSpM9ZMfAxuDiqcuabFM0sP40J2ntxtERFRLWOoIqoFXQJcsemV7mjmbo9ruUV4dlks4i7lyN0WERHVIoYqolrSzMMBm17tgS4BLtAWleHF/xzFz6fS5W6LiIhqiayhav/+/RgyZAj8/PygUCjw008/Ge0XQmDu3Lnw9fWFnZ0dIiIikJycbFRz8+ZNjBkzBhqNBi4uLnjppZeQn59vVHP69Gn06tULtra28Pf3x6JFi6r0snHjRgQFBcHW1hYhISHYvn17jXsh+is3BxXWTHwMkR28Uao34LW1J/HNvosQQsjdGhERmZmsoaqgoAAdO3bE0qVLq92/aNEiLFmyBF9//TXi4uLg4OCAyMhIFBf/OfF3zJgxSEhIQHR0NLZt24b9+/dj0qRJ0n6dTocBAwYgMDAQJ06cwOLFi/Huu+9i+fLlUs3hw4cxatQovPTSSzh58iSGDh2KoUOH4uzZszXqhag6tjZW+GpMKCb0aAYAmL/jPOZuSYDewGBFRNSgCAsBQGzevFlaNxgMwsfHRyxevFjalpubK9RqtVi7dq0QQohz584JAOLYsWNSzY4dO4RCoRDXrl0TQgjx1VdfCVdXV1FSUiLVzJ49W7Rt21ZaHzFihBg8eLBRP2FhYWLy5Mkm92IKrVYrAAitVmvya6hh+fbAJdHsrW0icPY28dLKo6KgpEzuloiI6D5M/f622DlVKSkpyMzMREREhLTN2dkZYWFhiI2NBQDExsbCxcUFXbt2lWoiIiKgVCoRFxcn1fTu3RsqlUqqiYyMRFJSEm7duiXV3Pk+lTWV72NKL9UpKSmBTqczWqhxe6lnc3w1ugvU1kr8lpiNUcuP4HpeidxtERGRGVhsqMrMzAQAeHt7G2339vaW9mVmZsLLy8tov7W1Ndzc3IxqqjvGne9xt5o799+vl+rMnz8fzs7O0uLv73+fT02NwaAQX6yZGAZXexucuqrFsGWHcPF6/v1fSEREFs1iQ1VDMGfOHGi1Wmm5cuWK3C2RhQgNdMP/XumOADd7XLlZhGeXHcbxyzflbouIiB6CxYYqHx8fAEBWVpbR9qysLGmfj48PsrOzjfaXl5fj5s2bRjXVHePO97hbzZ3779dLddRqNTQajdFCVKmFpyM2vdodHf1dkFtYhtHfxmH7mQy52yIiogdksaGqefPm8PHxwe7du6VtOp0OcXFxCA8PBwCEh4cjNzcXJ06ckGr27NkDg8GAsLAwqWb//v0oKyuTaqKjo9G2bVu4urpKNXe+T2VN5fuY0gvRg/BwVGPdxMfwRHtvlJYbELXmd3x74BJvuUBEVB/Vzbz56uXl5YmTJ0+KkydPCgDi008/FSdPnhSpqalCCCEWLFggXFxcxJYtW8Tp06fF008/LZo3by6KioqkYwwcOFB07txZxMXFiYMHD4rWrVuLUaNGSftzc3OFt7e3ePHFF8XZs2fFunXrhL29vfjmm2+kmkOHDglra2vxySefiMTERDFv3jxhY2Mjzpw5I9WY0sv98Oo/uptyvUG889MZETi74srAeVvOinK9Qe62iIhImP79LWuo2rt3rwBQZRk3bpwQouJWBu+8847w9vYWarVa9O/fXyQlJRkdIycnR4waNUo4OjoKjUYjJkyYIPLy8oxqTp06JXr27CnUarVo0qSJWLBgQZVeNmzYINq0aSNUKpXo0KGD+OWXX4z2m9LL/TBU0b0YDAaxfN9FKVhNWn1MFJaUy90WEVGjZ+r3t0IInmeoKzqdDs7OztBqtZxfRXe17XQ6Zqw/hVK9AZ0DXPDt2K5wd1TL3RYRUaNl6ve3xc6pImqsnnzED9+/HAZnOxucTMvFsGWHkXKjQO62iIjoPhiqiCxQt+YVt1xo6mqH1JxCPLvsME6k3pK7LSIiugeGKiIL1crLEZtf7YFHmjrjZkEpRv/fEew8e/ebzRIRkbwYqogsmKeTGusmPYb+QV4oKTfglR9OYMWhFLnbIiKiajBUEVk4e5U1vnkxFGPCAiAE8N7Wc/hg2zkYDLzGhIjIkjBUEdUD1lZKfDg0GLMHBgEA/nMwBVFrfkdxmV7mzoiIqBJDFVE9oVAo8Erflvj8+U5QWSmx42wmxnwbh5sFpXK3RkREYKgiqnee7tQEq1/qBo2tNU6k3sKzyw4jNYe3XCAikhtDFVE99FgLd/zvle5o4mKHlBsFGPbVYcRfyZW7LSKiRo2hiqieau3thM2vdkcHPw1yCkrx/PJYRJ/LkrstIqJGi6GKqB7z0thiw+Rw9G3rieIyAyZ/dxyrYy/L3RYRUaPEUEVUzzmorfHt2K54/lF/GAQwd0sC5m9P5C0XiIjqGEMVUQNgbaXE/GEhmBXZFgDwzf5LeG3dSd5ygYioDjFUETUQCoUCUf1a4d8jO8LGSoFtpzMw9j9HkVvIWy4QEdUFhiqiBuaZzk2xakI3OKmtcfTyTQxbdhhXbhbK3RYRUYPHUEXUAHVv5YEfX+kOX2dbXLpegGe+OozTV3PlbouIqEFjqCJqoNr6OGHzqz3QzleDG/klGPnNEexO5C0XiIhqC0MVUQPm42yLDZMfQ6/WHigq02Pi6uP4IS5V7raIiBokhiqiBs7J1gb/Hf8ohoc2hUEA/9x8Fgt3nuctF4iIzIyhiqgRsLFSYtFzj2B6RBsAwLKYi5i+IR4l5bzlAhGRuTBUETUSCoUCr0e0xifDO8JaqcCW+HSM/c9RaAvL5G6NiKhBYKgiamSeC22KFRMehaPaGnEpN/Hc14dx9RZvuUBE9LAYqogaoV6tPbFxSjh8NLZIzs7HM18dxtlrWrnbIiKq1xiqiBqpdr4abI7qjiAfJ1zPK8GIb2KxNylb7raIiOothiqiRszX2Q4bpoSjRyt3FJbq8fKq41h3NE3utoiI6iWGKqJGTmNrgxXju2FYlybQGwTe2nQG//o1CULwlgtERDXBUEVEUFkr8a/hHfFa/9YAgC/2XMDMDadQWm6QuTMiovqDoYqIAFTccmHGE22w8NkQWCkV2HTyGsavOApdMW+5QERkCoYqIjIy8tEA/Hf8o3BQWeHwxRw8s/QQLmTnyd0WEZHFY6gioir6tPHEhtu3XLh4vQBPf3kIO85kyN0WEZFFY6giomp18HPGttd64rEWbigo1eOVH37H/O2JKNdznhURUXUYqojorjwc1fj+pTBM6t0CAPDN/ksY+9+juJFfInNnRESWh6GKiO7J2kqJt//WDktHd4H97XlWQ744iPgruXK3RkRkURiqiMgkgx/xxZaoHmjh6YAMbTFGfB2LNXFpvJ8VEdFtDFVEZLLW3k7YEtUDkR28Uao34O3NZzD7f6dRXKaXuzUiItkxVBFRjTjZ2uDrF0Ixe2AQlApgw/GreO7rw7hys1Du1oiIZMVQRUQ1plAo8Erfllj99zC4Oahw9poOQ748iP1/XJe7NSIi2TBUEdED69naA1v/0ROPNHVGbmEZxq04iqV7L8Bg4DwrImp8GKqI6KE0cbHDhsnheP5RfwgBLN6VhMnfn+DjbYio0WGoIqKHZmtjhQXPPoIFw0KgslYi+lwWnv7yEJIy+XgbImo8GKqIyGye7xaAH6eEo4mLHVJuFGDo0kPYeipd7raIiOoEQxURmdUjTV2w9R890bOVB4rK9PjH2pP4YNs5lPHxNkTUwDFUEZHZuTmosOrv3fBq35YAgP8cTMGYb+OQnVcsc2dERLWHoYqIaoWVUoE3Bwbh6xdC4ai2xtGUmxjyxUGcSL0pd2tERLWCoYqIatXAYB9smdoDrb0ckaUrwfPLj2B17GU+3oaIGhyGKiKqdS09HfFTVA8MDvFFmV5g7pYEzNxwCkWlfLwNETUcDFVEVCcc1Nb4cnRn/PNv7WClVGDTyWsYtuww0nL4eBsiahgYqoiozigUCkzs3QLfvxQGdwcVEjN0ePKLA9h7Plvu1oiIHhpDFRHVufCW7tj2Wk90DnCBrrgcf191DJ/99gcfb0NE9RpDFRHJwtfZDusmPYYXHguAEMBnvyXj5dXHoS3k422IqH5iqCIi2aitrfDh0BB8Mrwj1NZK7DmfjSFfHsS5dJ3crRER1RhDFRHJ7rnQpvjfK93R1NUOaTcLMWzZIWw+eVXutoiIaoShiogsQnATZ2z7R0/0aeOJ4jIDpq8/hXlbzqK0nI+3IaL6gaGKiCyGi70K/x3/KF57vBUAYFVsKkb93xFk6fh4GyKyfAxVRGRRrJQKzBjQFt+O7QonW2ucSL2FwUsOIu5SjtytERHdE0MVEVmkiPbe2Dq1J9p6O+FGfglGfxuH/xxM4eNtiMhiWXSoevfdd6FQKIyWoKAgaX9xcTGioqLg7u4OR0dHPPvss8jKyjI6RlpaGgYPHgx7e3t4eXlh1qxZKC8vN6qJiYlBly5doFar0apVK6xcubJKL0uXLkWzZs1ga2uLsLAwHD16tFY+MxH9qZmHAzZHdcfTnfygNwh8sO0cXl8Xj8LS8vu/mIiojll0qAKADh06ICMjQ1oOHjwo7Zs+fTq2bt2KjRs3Yt++fUhPT8ewYcOk/Xq9HoMHD0ZpaSkOHz6MVatWYeXKlZg7d65Uk5KSgsGDB6Nfv36Ij4/HtGnT8PLLL2PXrl1Szfr16zFjxgzMmzcPv//+Ozp27IjIyEhkZ/Mu0ES1zV5ljc9GdsK8Ie1hrVTg51PpeGbpYaTcKJC7NSIiY8KCzZs3T3Ts2LHafbm5ucLGxkZs3LhR2paYmCgAiNjYWCGEENu3bxdKpVJkZmZKNcuWLRMajUaUlJQIIYR48803RYcOHYyOPXLkSBEZGSmtd+vWTURFRUnrer1e+Pn5ifnz59fo82i1WgFAaLXaGr2OiCocTckRXT+MFoGzt4nguTtFdELm/V9ERPSQTP3+tviRquTkZPj5+aFFixYYM2YM0tLSAAAnTpxAWVkZIiIipNqgoCAEBAQgNjYWABAbG4uQkBB4e3tLNZGRkdDpdEhISJBq7jxGZU3lMUpLS3HixAmjGqVSiYiICKmGiOrGo83c8Ms/eqJroCvySsrx8urj+NevSdDz8TZEZAEsOlSFhYVh5cqV2LlzJ5YtW4aUlBT06tULeXl5yMzMhEqlgouLi9FrvL29kZmZCQDIzMw0ClSV+yv33atGp9OhqKgIN27cgF6vr7am8hh3U1JSAp1OZ7QQ0cPx0thi7aTHML57MwDAF3suYMLKY7hVUCpvY0TU6Fl0qBo0aBCGDx+ORx55BJGRkdi+fTtyc3OxYcMGuVszyfz58+Hs7Cwt/v7+crdE1CDYWCnx7lMd8NnITrC1UWL/H9cx5MuDOHtNK3drRNSIWXSo+isXFxe0adMGFy5cgI+PD0pLS5Gbm2tUk5WVBR8fHwCAj49PlasBK9fvV6PRaGBnZwcPDw9YWVlVW1N5jLuZM2cOtFqttFy5cqXGn5mI7m5o5ybY/GoPBLrb4+qtIgxbdhgbjvN/Z0Qkj3oVqvLz83Hx4kX4+voiNDQUNjY22L17t7Q/KSkJaWlpCA8PBwCEh4fjzJkzRlfpRUdHQ6PRoH379lLNnceorKk8hkqlQmhoqFGNwWDA7t27pZq7UavV0Gg0RgsRmVc7Xw1+ntoT/YO8UFpuwJs/nsbbm8+gpFwvd2tE1NjU0cT5BzJz5kwRExMjUlJSxKFDh0RERITw8PAQ2dnZQgghpkyZIgICAsSePXvE8ePHRXh4uAgPD5deX15eLoKDg8WAAQNEfHy82Llzp/D09BRz5syRai5duiTs7e3FrFmzRGJioli6dKmwsrISO3fulGrWrVsn1Gq1WLlypTh37pyYNGmScHFxMbqq0BS8+o+o9uj1BvFZ9B+i2VvbRODsbeLpLw+K9NxCudsiogbA1O9viw5VI0eOFL6+vkKlUokmTZqIkSNHigsXLkj7i4qKxKuvvipcXV2Fvb29eOaZZ0RGRobRMS5fviwGDRok7OzshIeHh5g5c6YoKyszqtm7d6/o1KmTUKlUokWLFmLFihVVevniiy9EQECAUKlUolu3buLIkSM1/jwMVUS1b8/5LPHIu7tE4Oxtosv7v4pDF67L3RIR1XOmfn8rhOAzH+qKTqeDs7MztFotTwUS1aK0nEJM+f4EzmXooFQAbw0KwsReLaBQKORujYjqIVO/vxmq6hBDFVHdKSrV458/ncGm368BAIJ8nNA5wBXBTTTo4OeMIB8n2NpYydwlEdUHpn5/W9dhT0REdcZOZYV/De+IzgGueH9rAs5n5uF8Zp6030qpQCtPR3S4HbKC/TRo76eBk62NjF0TUX3Gkao6xJEqInlkaotxPPUmEtJ1OHtNi4R0HW7e5Wahzdzt0aGJMzr4aRDsV/Gvu6O6jjsmIkvC038WiKGKyDIIIZCpK8bZazokpGtx9poO59K1SNcWV1vv62yLDn63R7RuBy5fZ1vO0SJqJBiqLBBDFZFlu1lQKoWshPSKEa2UGwXV1ro5qO4IWhX/BrrZQ6lk0CJqaBiqLBBDFVH9k1dchsSMPOm0YUK6FsnZ+dU+xNlRbY32vpo/52k10aCVpyOsrerVfZaJ6C8YqiwQQxVRw1BcpkdSZl7FHK10LRKuaZGYmYfSckOVWrW1EkE+TkbztNryykOieoWhygIxVBE1XGV6Ay5ez0fCtdtBK12Hc+k65JeUV6m1UirQ2ssR7W+HrOAmzmjn68QrD4ksFEOVBWKoImpcDAaBtJuFOPuXeVp3u/KwuYeDFLQq5mvxykMiS8BQZYEYqojowa48/HMyPK88JKp7DFUWiKGKiO4mJ7/k9kT4itOH5+5x5aGrvQ3a+2nQ3ldz+19ntPB0gA0nxBPVCoYqC8RQRUQ1UZMrD1XWSrT1dvozaPlpEOTDeVpE5sBQZYEYqojoYRWX6XEhOx/n0nU4l6GT/q1uQjwABLrbVwStO8KWj4anD4lqgqHKAjFUEVFtMBgErt4qwrmMP686PJehQ8Zd5mlVd/qwpacD76dFdBcMVRaIoYqI6tLNglIk3jGadS5dhwvX7376MMjnjtOHvhoE+WrgqLaWoXMiy8JQZYEYqohIbsVleiRn5eNchlYKW4kZeXc9fdjM3b7KqJa3Rs3Th9SoMFRZIIYqIrJEBoPAlVuFVeZp3e30oZuDymhEq72fBi08ePqQGi6GKgvEUEVE9UlNTh+qrZVoy9OH1EAxVFkghioiqu+qO314Ll2HglJ9lVqFAmjm7lBlVMvLiacPqX5hqLJADFVE1BBVPo7n3F9GtTJ11Z8+dHdQoY23E5p52CPQ3QHN3O0R4OaAQHd7OHBkiywQQ5UFYqgiosYkJ78EiRl5RqNaF68XVHv6sJKnk1oKWc3c7RHocftfNwc42/NGpiQPhioLxFBFRI1dcZkeSZl5uHg9H6k5hUjNKcDl2//eKiy752td7G2kka1At9ujXLdHu9wdVDylSLWGocoCMVQREd2dtqgMaTmFuJxTIIWtyvXsvJJ7vtZBZWUUsu4MXd5OtlAqGbjowTFUWSCGKiKiB1NYWn57ZMt4dCs1pxDp2iLc65tMba1EgNuf87cC3St/doCfiy1vBUH3Zer3N2cEEhGRxbNXWaOdrwbtfKt+oZWU63HlZtEdo1t/hq6rt4pQUm5AcnY+krPzq7zWWqmAv5s9AtzsbweuitGtADcH+LvZQW1tVRcfjxoIhioiIqrX1NZWaOXliFZejlX2lesNSM8tNjqlWDnClXqzEKXlBqTcKEDKjQLs+8trFQrAz9nujpGt26cWb4922av4FUrGePqvDvH0HxGR5TAYBDJ1FYGrYu5WodFoV3X33rqTl5MaTV3t4O6ohruDCm4OKulnd8eKdQ9HNVztVVBZ8xRjfcbTf0RERPegVCrg52IHPxc7dG9pvE8IgRv5pVVHt3IqRrV0xeXIziu57wT6Shpba7g7qiuC1+3Q5e5we/2Onz0cVXB1UMGG87zqJYYqIiKiv1AoFPB0UsPTSY2uzdyq7M8tLK2YJJ9bhJyCUuTkl+JmQckdP5cip6AENwtKYRCArrgcuuJypNwoMOn9ne1sjEa87jUa5mav4mR7C8FQRUREVEMu9iq42KvQ0d/lnnUGg4C2qAw5BSXIyS+tCF0FpcjJL7kdvO74Ob8UtworQpi2qAzaojJcMjGEudjfDmFGo1+qP0fH7hgNc7W3YQirJQxVREREtUSpVMDVoeKUXiuv+9frK0NYfonRCNiN26NfNwtKceOOQHarsBRCALmFZcgtLMPF6/cPYQoF4GJnIwUuD8eKgOiotoaDyhoOais4qK0rFlXFz45qa9irrCpq1Naws7Hivb+qwVBFRERkIayUiopTeg4qtDahXm8QyC0slQJY5SnHG7fD2J8/V4yI5RaVQQjgVmHZfe9gfy8KBWBvc0fgUlvBQVX5szUcb69LP0uB7XZou/3zna+1agAhjaGKiIionrJSKirmWDmqAe/715frDcgtKpMCWOX8r1uFpSgoKUdBqb7i35JyFJToUVBajvySchSW3N5eWg6DAIRARW2p3uTJ+vdjZ2P15yjZPUfM/gxq9reD3J217g4q2NrIc38xhioiIqJGwtpKCQ9HNTwc1QCcavx6IQSKywzIrwxepbfDl/RzOfL/sn7n/vwSPQpLKusqQlzlA7aLyvQoKtPjRn7pQ33G957qgHHdmz3UMR4UQxURERGZRKFQwE5lBTuVFTyd1A99PCEESsoNKCgpR2Gp/o6wpv8zeP11XzWhLb+kHIW3A56DWr5ow1BFREREslAoFLC1sYKtjRXczXRMOe9pzmsqiYiIqMFQKOSb8M5QRURERGQGDFVEREREZsBQRURERGQGDFVEREREZsBQRURERGQGDFVEREREZsBQRURERGQGDFVEREREZsBQRURERGQGDFVEREREZsBQRURERGQGDFVEREREZsBQRURERGQG1nI30JgIIQAAOp1O5k6IiIjIVJXf25Xf43fDUFWH8vLyAAD+/v4yd0JEREQ1lZeXB2dn57vuV4j7xS4yG4PBgPT0dDg5OUGhUMjdjkXS6XTw9/fHlStXoNFo5G6n0ePfw7Lw72FZ+PewLLX59xBCIC8vD35+flAq7z5ziiNVdUipVKJp06Zyt1EvaDQa/kfKgvDvYVn497As/HtYltr6e9xrhKoSJ6oTERERmQFDFREREZEZMFSRRVGr1Zg3bx7UarXcrRD497A0/HtYFv49LIsl/D04UZ2IiIjIDDhSRURERGQGDFVEREREZsBQRURERGQGDFVEREREZsBQRbKbP38+Hn30UTg5OcHLywtDhw5FUlKS3G3RbQsWLIBCocC0adPkbqVRu3btGl544QW4u7vDzs4OISEhOH78uNxtNUp6vR7vvPMOmjdvDjs7O7Rs2RIffPDBfZ8LR+axf/9+DBkyBH5+flAoFPjpp5+M9gshMHfuXPj6+sLOzg4RERFITk6uk94Yqkh2+/btQ1RUFI4cOYLo6GiUlZVhwIABKCgokLu1Ru/YsWP45ptv8Mgjj8jdSqN269Yt9OjRAzY2NtixYwfOnTuHf/3rX3B1dZW7tUZp4cKFWLZsGb788kskJiZi4cKFWLRoEb744gu5W2sUCgoK0LFjRyxdurTa/YsWLcKSJUvw9ddfIy4uDg4ODoiMjERxcXGt98ZbKpDFuX79Ory8vLBv3z707t1b7nYarfz8fHTp0gVfffUVPvzwQ3Tq1AmfffaZ3G01Sm+99RYOHTqEAwcOyN0KAXjyySfh7e2N//znP9K2Z599FnZ2dvj+++9l7KzxUSgU2Lx5M4YOHQqgYpTKz88PM2fOxBtvvAEA0Gq18Pb2xsqVK/H888/Xaj8cqSKLo9VqAQBubm4yd9K4RUVFYfDgwYiIiJC7lUbv559/RteuXTF8+HB4eXmhc+fO+L//+z+522q0unfvjt27d+OPP/4AAJw6dQoHDx7EoEGDZO6MUlJSkJmZafTfLWdnZ4SFhSE2NrbW358PVCaLYjAYMG3aNPTo0QPBwcFyt9NorVu3Dr///juOHTsmdysE4NKlS1i2bBlmzJiBt99+G8eOHcNrr70GlUqFcePGyd1eo/PWW29Bp9MhKCgIVlZW0Ov1+OijjzBmzBi5W2v0MjMzAQDe3t5G2729vaV9tYmhiixKVFQUzp49i4MHD8rdSqN15coVvP7664iOjoatra3c7RAq/s9G165d8fHHHwMAOnfujLNnz+Lrr79mqJLBhg0b8MMPP2DNmjXo0KED4uPjMW3aNPj5+fHv0cjx9B9ZjKlTp2Lbtm3Yu3cvmjZtKnc7jdaJEyeQnZ2NLl26wNraGtbW1ti3bx+WLFkCa2tr6PV6uVtsdHx9fdG+fXujbe3atUNaWppMHTVus2bNwltvvYXnn38eISEhePHFFzF9+nTMnz9f7tYaPR8fHwBAVlaW0fasrCxpX21iqCLZCSEwdepUbN68GXv27EHz5s3lbqlR69+/P86cOYP4+Hhp6dq1K8aMGYP4+HhYWVnJ3WKj06NHjyq3Gfnjjz8QGBgoU0eNW2FhIZRK469PKysrGAwGmTqiSs2bN4ePjw92794tbdPpdIiLi0N4eHitvz9P/5HsoqKisGbNGmzZsgVOTk7SeW9nZ2fY2dnJ3F3j4+TkVGU+m4ODA9zd3TnPTSbTp09H9+7d8fHHH2PEiBE4evQoli9fjuXLl8vdWqM0ZMgQfPTRRwgICECHDh1w8uRJfPrpp/j73/8ud2uNQn5+Pi5cuCCtp6SkID4+Hm5ubggICMC0adPw4YcfonXr1mjevDneeecd+Pn5SVcI1ipBJDMA1S4rVqyQuzW6rU+fPuL111+Xu41GbevWrSI4OFio1WoRFBQkli9fLndLjZZOpxOvv/66CAgIELa2tqJFixbin//8pygpKZG7tUZh79691X5njBs3TgghhMFgEO+8847w9vYWarVa9O/fXyQlJdVJb7xPFREREZEZcE4VERERkRkwVBERERGZAUMVERERkRkwVBERERGZAUMVERERkRkwVBERERGZAUMVERERkRkwVBFRg3D58mUoFArEx8fL3Yrk/PnzeOyxx2Bra4tOnTrV+PWW+JmI6O4YqojILMaPHw+FQoEFCxYYbf/pp5+gUChk6kpe8+bNg4ODA5KSkoyeRSaXlStXwsXFRe42iBoshioiMhtbW1ssXLgQt27dkrsVsyktLX3g1168eBE9e/ZEYGAg3N3dzdiVvPR6PR8eTFQNhioiMpuIiAj4+Phg/vz5d6159913q5wK++yzz9CsWTNpffz48Rg6dCg+/vhjeHt7w8XFBe+//z7Ky8sxa9YsuLm5oWnTplixYkWV458/fx7du3eHra0tgoODsW/fPqP9Z8+exaBBg+Do6Ahvb2+8+OKLuHHjhrS/b9++mDp1KqZNmwYPDw9ERkZW+zkMBgPef/99NG3aFGq1Gp06dcLOnTul/QqFAidOnMD7778PhUKBd999967HWbRoEVq1agW1Wo2AgAB89NFH1dZWN9L015HAU6dOoV+/fnBycoJGo0FoaCiOHz+OmJgYTJgwAVqtFgqFwqinkpISvPHGG2jSpAkcHBwQFhaGmJiYKu/7888/o3379lCr1UhLS0NMTAy6desGBwcHuLi4oEePHkhNTa22d6LGgKGKiMzGysoKH3/8Mb744gtcvXr1oY61Z88epKenY//+/fj0008xb948PPnkk3B1dUVcXBymTJmCyZMnV3mfWbNmYebMmTh58iTCw8MxZMgQ5OTkAAByc3Px+OOPo3Pnzjh+/Dh27tyJrKwsjBgxwugYq1atgkqlwqFDh/D1119X29/nn3+Of/3rX/jkk09w+vRpREZG4qmnnkJycjIAICMjAx06dMDMmTORkZGBN954o9rjzJkzBwsWLMA777yDc+fOYc2aNfD29n7g39uYMWPQtGlTHDt2DCdOnMBbb70FGxsbdO/eHZ999hk0Gg0yMjKMepo6dSpiY2Oxbt06nD59GsOHD8fAgQOlzwIAhYWFWLhwIb799lskJCTAzc0NQ4cORZ8+fXD69GnExsZi0qRJjfZULxEAoE4e20xEDd64cePE008/LYQQ4rHHHhN///vfhRBCbN68Wdz5n5p58+aJjh07Gr323//+twgMDDQ6VmBgoNDr9dK2tm3bil69eknr5eXlwsHBQaxdu1YIIURKSooAIBYsWCDVlJWViaZNm4qFCxcKIYT44IMPxIABA4ze+8qVKwKA9BT7Pn36iM6dO9/38/r5+YmPPvrIaNujjz4qXn31VWm9Y8eOYt68eXc9hk6nE2q1Wvzf//1ftfsrP9PJkyeFEEKsWLFCODs7G9X89ffr5OQkVq5cWe3xqnt9amqqsLKyEteuXTPa3r9/fzFnzhzpdQBEfHy8tD8nJ0cAEDExMXf9fESNDUeqiMjsFi5ciFWrViExMfGBj9GhQwcolX/+J8rb2xshISHSupWVFdzd3ZGdnW30uvDwcOlna2trdO3aVerj1KlT2Lt3LxwdHaUlKCgIQMX8p0qhoaH37E2n0yE9PR09evQw2t6jR48afebExESUlJSgf//+Jr/mfmbMmIGXX34ZERERWLBggdHnqs6ZM2eg1+vRpk0bo9/Lvn37jF6rUqnwyCOPSOtubm4YP348IiMjMWTIEHz++efIyMgw2+cgqo8YqojI7Hr37o3IyEjMmTOnyj6lUgkhhNG2srKyKnU2NjZG6wqFotptNZkwnZ+fjyFDhiA+Pt5oSU5ORu/evaU6BwcHk4/5MOzs7GpUb8rv7t1330VCQgIGDx6MPXv2oH379ti8efNdj5mfnw8rKyucOHHC6HeSmJiIzz//3KjXv57aW7FiBWJjY9G9e3esX78ebdq0wZEjR2r0mYgaEoYqIqoVCxYswNatWxEbG2u03dPTE5mZmUbhwJz3YbrzS728vBwnTpxAu3btAABdunRBQkICmjVrhlatWhktNQlSGo0Gfn5+OHTokNH2Q4cOoX379iYfp3Xr1rCzszP5dguenp7Iy8tDQUGBtK26312bNm0wffp0/Prrrxg2bJg0oV+lUkGv1xvVdu7cGXq9HtnZ2VV+Jz4+PvftqXPnzpgzZw4OHz6M4OBgrFmzxqTPQtQQMVQRUa0ICQnBmDFjsGTJEqPtffv2xfXr17Fo0SJcvHgRS5cuxY4dO8z2vkuXLsXmzZtx/vx5REVF4datW/j73/8OAIiKisLNmzcxatQoHDt2DBcvXsSuXbswYcKEKmHjfmbNmoWFCxdi/fr1SEpKwltvvYX4+Hi8/vrrJh/D1tYWs2fPxptvvonVq1fj4sWLOHLkCP7zn/9UWx8WFgZ7e3u8/fbbuHjxItasWYOVK1dK+4uKijB16lTExMQgNTUVhw4dwrFjx6RQ2axZM+Tn52P37t24ceMGCgsL0aZNG4wZMwZjx47Fpk2bkJKSgqNHj2L+/Pn45Zdf7tp7SkoK5syZg9jYWKSmpuLXX39FcnKy9F5EjRFDFRHVmvfff7/K6bl27drhq6++wtKlS9GxY0ccPXr0rlfGPYgFCxZgwYIF6NixIw4ePIiff/4ZHh4eACCNLun1egwYMAAhISGYNm0aXFxcjOZvmeK1117DjBkzMHPmTISEhGDnzp34+eef0bp16xod55133sHMmTMxd+5ctGvXDiNHjqwyT6ySm5sbvv/+e2zfvh0hISFYu3at0a0arKyskJOTg7Fjx6JNmzYYMWIEBg0ahPfeew8A0L17d0yZMgUjR46Ep6cnFi1aBKDiNN7YsWMxc+ZMtG3bFkOHDsWxY8cQEBBw177t7e1x/vx5PPvss2jTpg0mTZqEqKgoTJ48uUafn6ghUYi/nqAnIiIiohrjSBURERGRGTBUEREREZkBQxURERGRGTBUEREREZkBQxURERGRGTBUEREREZkBQxURERGRGTBUEREREZkBQxURERGRGTBUEREREZkBQxURERGRGTBUEREREZnB/wdX/aEmcwfdbQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "The graph shown below depicts that *5 clusters* are possible(optimal) for the given dataset."
      ],
      "metadata": {
        "id": "NYjJ8fVHrQAX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Scaling"
      ],
      "metadata": {
        "id": "S5BJUYkHrgFW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)"
      ],
      "metadata": {
        "id": "pDeBVW9NrfBE"
      },
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Clusters"
      ],
      "metadata": {
        "id": "_KEXET4PsD21"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Training the KMeans model with n_clusters=5\n",
        "kmeans_model = KMeans(n_clusters=5, init='k-means++', random_state=42)\n",
        "y_kmeans = kmeans_model.fit_predict(X)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pdD_B-IDsHDK",
        "outputId": "54443cc0-fada-4b0e-ce21-9af0dc78b4ed"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/cluster/_kmeans.py:1416: FutureWarning: The default value of `n_init` will change from 10 to 'auto' in 1.4. Set the value of `n_init` explicitly to suppress the warning\n",
            "  super()._check_params_vs_input(X, default_n_init=10)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualising the clusters\n",
        "plt.scatter(X[y_kmeans == 0, 0], X[y_kmeans == 0, 1], s = 30, c = 'brown', label = 'Cluster 1')\n",
        "plt.scatter(X[y_kmeans == 1, 0], X[y_kmeans == 1, 1], s = 30, c = 'gold', label = 'Cluster 2')\n",
        "plt.scatter(X[y_kmeans == 2, 0], X[y_kmeans == 2, 1], s = 30, c = 'lightgreen', label = 'Cluster 3')\n",
        "plt.scatter(X[y_kmeans == 3, 0], X[y_kmeans == 3, 1], s = 30, c = 'blue', label = 'Cluster 4')\n",
        "plt.scatter(X[y_kmeans == 4, 0], X[y_kmeans == 4, 1], s = 30, c = 'red', label = 'Cluster 5')\n",
        "plt.scatter(x=kmeans_model.cluster_centers_[:, 0], y=kmeans_model.cluster_centers_[:, 1], s=100, c='black', marker='+', label='Cluster Centers')\n",
        "plt.legend()\n",
        "plt.title('Clusters of customers')\n",
        "plt.xlabel('Annual Income')\n",
        "plt.ylabel('Spending Score')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "5X_iHkrtsK2i",
        "outputId": "8cd1a72f-34bc-4155-83ca-30f711ff8070"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "*## Please refer to the report, where I have provided a detailed explanation.*"
      ],
      "metadata": {
        "id": "N81mZdHywZwz"
      }
    }
  ]
}