K-Means Clustering

k-means_clustering.ipynb

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      "source": [
        "# K-Means Clustering\n",
        "## A Short Case Study\n",
        "\n",
        "On this notebook, we're gonna dive not so deep on the basics of how to dow a K-Means Clustering on a small example dataset. At the end of this study, I hope we could achieve the following understandings regarding our problem:\n",
        "\n",
        "1.   What's a good way to segment our dataset on a small set of clusters?\n",
        "2.   How can we achieve quick results using the **Pandas**, **Numpy**, **Matplotlib**, **Pyplot** and **SKLearn** modules?\n",
        "3.   How the select the best hyperparameters for K-Means Clustering?\n",
        "4.   How to display and visualize data in the most honest and friendly way to our stakeholders?\n",
        "\n",
        " \n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ]
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      "source": [
        "# Why to Cluster Data\n",
        "\n",
        "Segmentation is a natural approach to solve a variety of human issues through history. Linnaeus followed a systematic approach to separate and classify the life on Earth on his famous work *Systema Naturae*, followed by a more scientific and general study made by Willi Hennig on the *Phylogenetic Systematics*. Either way, the classification of the species have meaningful purposes behind; for example, it could be reasonable to prescribe antiobiotics against genetically similar bacteria.\n",
        "\n",
        "\n",
        "| ![Cladistic Clustering Tree Example](https://upload.wikimedia.org/wikipedia/commons/thumb/d/df/Clade-grade_II.png/1920px-Clade-grade_II.png) |\n",
        "| :--: |\n",
        "| *Example of a phylogenetic tree generated by cladistics analysis* |\n",
        "\n",
        "\n",
        "In financial business, clustering data from clients is very useful to a lot of purposes, including directing marketing actions to specific segments, or to avaliate credit lines to be offered or not to ceratin groups. The example taken on this notebook have a similar approach regarding its dataset."
      ]
    },
    {
      "cell_type": "markdown",
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      "source": [
        "# Initialization"
      ]
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    {
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      "source": [
        "# Initialization\n",
        "\n",
        "# Module Imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.express as px\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Style Definitions\n",
        "plt.style.use('Solarize_Light2')"
      ],
      "execution_count": null,
      "outputs": []
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      "cell_type": "markdown",
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      "source": [
        "# Data Understanding\n",
        "\n",
        "Before moving on to the action, let's have some grasp on the dataset acquired. The dataset used was downloaded from [Mall Customer Segmentation Data](https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python]), a [Kaggle](https://www.kaggle.com/) fictional public available dataset. The dataset contains **200** clients information, comprising useful data as each client **Gender**, **Age**, **Annual Income** (in thousands of dollars) and a **Spending Score**, attributted from the consuming historics and potential.\n",
        "\n",
        "\n",
        "> To use this dataset on a notebook copy, download the dataset and upload to the notebook server as `customer.csv`.\n",
        "\n",
        "Here, I'm gonna show some of the dataset metrics, general visualization and some simple graphics regarding its data, to improve our comprehension about a future model to be implemented.\n",
        "\n"
      ]
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        "outputId": "3ea3945c-eea1-4bdd-be9d-28683e466757"
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      "source": [
        "# Get dataframe from CSV file\n",
        "df = pd.read_csv('customers.csv')\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Male</td>\n",
              "      <td>19</td>\n",
              "      <td>15</td>\n",
              "      <td>39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Male</td>\n",
              "      <td>21</td>\n",
              "      <td>15</td>\n",
              "      <td>81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Female</td>\n",
              "      <td>20</td>\n",
              "      <td>16</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Female</td>\n",
              "      <td>23</td>\n",
              "      <td>16</td>\n",
              "      <td>77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Female</td>\n",
              "      <td>31</td>\n",
              "      <td>17</td>\n",
              "      <td>40</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   CustomerID  Gender  Age  Annual Income (k$)  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",
              "4           5  Female   31                  17                      40"
            ]
          },
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        "outputId": "f19a6eda-5906-4e4d-89fa-f2a33c48948f"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 5)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
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        "outputId": "02f0237f-34e9-465d-c883-4d4ce277f4aa"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
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        {
          "output_type": "execute_result",
          "data": {
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</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>count</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "      <td>200.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>38.850000</td>\n",
              "      <td>60.560000</td>\n",
              "      <td>50.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>57.879185</td>\n",
              "      <td>13.969007</td>\n",
              "      <td>26.264721</td>\n",
              "      <td>25.823522</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>18.000000</td>\n",
              "      <td>15.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>50.750000</td>\n",
              "      <td>28.750000</td>\n",
              "      <td>41.500000</td>\n",
              "      <td>34.750000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>100.500000</td>\n",
              "      <td>36.000000</td>\n",
              "      <td>61.500000</td>\n",
              "      <td>50.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>150.250000</td>\n",
              "      <td>49.000000</td>\n",
              "      <td>78.000000</td>\n",
              "      <td>73.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>200.000000</td>\n",
              "      <td>70.000000</td>\n",
              "      <td>137.000000</td>\n",
              "      <td>99.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       CustomerID         Age  Annual Income (k$)  Spending Score (1-100)\n",
              "count  200.000000  200.000000          200.000000              200.000000\n",
              "mean   100.500000   38.850000           60.560000               50.200000\n",
              "std     57.879185   13.969007           26.264721               25.823522\n",
              "min      1.000000   18.000000           15.000000                1.000000\n",
              "25%     50.750000   28.750000           41.500000               34.750000\n",
              "50%    100.500000   36.000000           61.500000               50.000000\n",
              "75%    150.250000   49.000000           78.000000               73.000000\n",
              "max    200.000000   70.000000          137.000000               99.000000"
            ]
          },
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          "execution_count": 19
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          "height": 300
        },
        "outputId": "08b466fb-a2e2-459c-8ab8-2a86ee9ddd56"
      },
      "source": [
        "plt.figure(1, figsize=(16,4))\n",
        "n = 0 \n",
        "for i in ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']:\n",
        "    n += 1\n",
        "    plt.subplot(1 , 3 , n)\n",
        "    plt.subplots_adjust(hspace =0.5 , wspace = 0.5)\n",
        "    sns.distplot(df[i] , bins = 32)\n",
        "    plt.title(f'Histogram of {i}')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KX29S2knkyJM",
        "colab_type": "text"
      },
      "source": [
        "# First Clustering\n",
        "\n",
        "## By Age and Spending Score\n",
        "\n",
        "At first, let's assume that these two columns could form clusters together, so let's just apply the fitting function at the class `sklearn.Kmeans`. The purpose here is to obtain as many values of cluster inertia as possible, selecting the closest value to the elbow of the `inertia` X `cluster_number` graph.\n",
        "\n",
        "Obtained the best cluster size, the model is updated and the labels are associated with each row on the dataset. An example is given at the second plot below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wB7fSpP8-ya9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Assignment Stage\n",
        "\n",
        "X1 = df.loc[:, ['Age', 'Spending Score (1-100)']].values\n",
        "inertia = []\n",
        "for n in range(1 , 11):\n",
        "    model = KMeans(n_clusters = n,\n",
        "               init='k-means++',\n",
        "               max_iter=500,\n",
        "               random_state=42)\n",
        "    model.fit(X1)\n",
        "    inertia.append(model.inertia_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fI4_JF0hCHZ3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "09321078-0d4e-4aa3-9235-5b53c5532f93"
      },
      "source": [
        "plt.figure(1 , figsize = (15 ,6))\n",
        "plt.plot(np.arange(1 , 11) , inertia , 'o')\n",
        "plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)\n",
        "plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ynUMxih5Ypli",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "49c23c5f-c908-4edf-a165-5c901420f6a1"
      },
      "source": [
        "model = KMeans(n_clusters = 4,\n",
        "            init='k-means++',\n",
        "            max_iter=500,\n",
        "            random_state=42)\n",
        "model.fit(X1)\n",
        "labels = model.labels_\n",
        "centroids = model.cluster_centers_\n",
        "y_kmeans = model.fit_predict(X1) \n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(X1[y_kmeans == 0, 0], X1[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
        "plt.scatter(X1[y_kmeans == 1, 0], X1[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(X1[y_kmeans == 2, 0], X1[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
        "plt.scatter(X1[y_kmeans == 3, 0], X1[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
        "plt.scatter(X1[y_kmeans == 4, 0], X1[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
        "plt.title('Clusters of Customers - Age X Spending Score')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Spending Score')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3eAalA96ltF9",
        "colab_type": "text"
      },
      "source": [
        "# Second Clustering\n",
        "\n",
        "## By Annual Income and Spending Score"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JmXZqwR5HZRQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "ddbf1bcb-0b9e-478e-ebef-1c064f63d4f3"
      },
      "source": [
        "# Assignment Stage\n",
        "\n",
        "X2 = df.loc[:, ['Annual Income (k$)', 'Spending Score (1-100)']].values\n",
        "inertia = []\n",
        "for n in range(1 , 11):\n",
        "    model = KMeans(n_clusters = n,\n",
        "               init='k-means++',\n",
        "               max_iter=500,\n",
        "               random_state=42)\n",
        "    model.fit(X2)\n",
        "    inertia.append(model.inertia_)\n",
        "\n",
        "plt.figure(1 , figsize = (20, 10))\n",
        "plt.plot(np.arange(1 , 11) , inertia , 'o')\n",
        "plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)\n",
        "plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j07NlzwEAAABOAoGKLa1WOfqpfklycHo2U6trJS8CAAAATjSBii1vT1dnLhroSyPJ3Ucm02i61Q8AAABaiUDFtvCRkcH01KoZX13LY7PzZc8BAAAATiCBim2hvVLJDWNHb/V7cHImc+vrJS8CAAAAThSBim3jjJ7unNvbk41mMweOTKXpVj8AAABoCQIV28q1o0PprFby6vJKnppfLHsOAAAAcAIIVGwrXbVqrh09eqvf/RPTWdqol7wIAAAA+KAEKradc3u7c3p3V9Yajdw7MVX2HAAAAOADEqjYdoqiyPVjw2mrFHl+YSkvLCyVPQkAAAD4AAQqtqW+tlquGh5MktwzMZXVeqPkRQAAAMD7JVCxbV000JfdnR1Z2qjngcnpsucAAAAA75NAxbZVKYrcuGs41aLIE3MLeW15pexJAAAAwPsgULGtDbW357Kh/iTJ3Ucms9Fwqx8AAABsNwIV296+oYEMtbdlbn0jD03Nlj0HAAAAeI8EKra9alFk/66RFEm+PTOXidW1sicBAAAA74FARUvY1dmRiwf60sjRW/3qzWbZkwAAAIDjJFDRMq4aGUxfrZbJ1bV8e2au7DkAAADAcRKoaBltlUpu3DWcJHloajaza+slLwIAAACOh0BFSzm1uyvn9/Wk3mzmwPhUmm71AwAAgC1PoKLlXDM6lK5qNa8tr+SJuYWy5wAAAADvQqCi5XRWq7ludChJ8sDkTBY3NkpeBAAAALwTgYqWdHZvd87o6cpao5F73eoHAAAAW5pARUsqiiI3jA2nvVLJC4vLeW5xqexJAAAAwDEIVLSsnlotV48MJknuG5/Oar1e8iIAAADg7QhUtLQL+3uzp6sjy/V67p+YLnsOAAAA8DYEKlpaURTZPzaSalHkqfnFvLK0XPYkAAAA4HsIVLS8gfa2XDE8kCQ5cGQq641GyYsAAACANxOo2BEuHezPSEd75jc28s2p2bLnAAAAAG8iULEjVIsi+8eGUyR5dGYu4yurZU8CAAAAXidQsWOMdnbkw4P9aSa568hk6s1m2ZMAAACACFTsMFcMD6S/rZbptfUcnHarHwAAAGwFAhU7Sq1SyY1jI0mSR6bnMr22VvIiAAAAQKBix9nb3ZkL+3tTbzZz4MhUmm71AwAAgFIJVOxIV48MpbtWzeGV1Tw+u1D2HAAAANjRBCp2pI5qJdePDidJvjE1nYX1jZIXAQAAwM4lULFjndXbnbN6u7PeaOaecbf6AQAAQFkEKna060eH016p5KWl5TyzsFT2HAAAANiRBCp2tO5aNdeODiVJ7p+YyvJGveRFAAAAsPMIVOx45/f1ZG9XZ1bqjdw/MV32HAAAANhxBCp2vKIocuOu4dSKIs8sLObFRbf6AQAAwGYSqCBJf1tbrhweTJLcOz6dtUaj5EUAAACwcwhU8LpLBvsy2tGehY2NPDg5U/YcAAAA2DEEKnhdpShy066RVJI8PjufQ8srZU8CAACAHUGggjcZ7mjPvqGBNJMcODKVjUaz7EkAAADQ8gQq+B6XDQ1ksK0tM+vrOTg9W/YcAAAAaHkCFXyPWuXop/olySPTs5laXSt5EQAAALQ2gQrexp6uzlw00JdGkruPTKbRdKsfAAAAnCwCFRzDR0YG01OrZnx1LY/Nzpc9BwAAAFqWQAXH0F6p5Iaxo7f6PTg5k7n19ZIXAQAAQGsSqOAdnNHTnXN7e7LRbObAkak03eoHAAAAJ5xABe/i2tGhdFYreXV5JU/PL5Y9BwAAAFqOQAXvoqtWzbWjR2/1u39iOksb9ZIXAQAAQGsRqOA4nNvbndO6u7LaaOS+iamy5wAAAEBLEajgOBRFkRvGhtNWKfLcwlJeWFgqexIAAAC0DIEKjlNfWy1XDQ8mSe6ZmMpqvVHyIgAAAGgNtc14kam5xfzOl+/K3OJyihTZf8UF+cQ1F2dxeTW/9aU7MjmzkJHB3vzC5z+Wnq6ONJvN/P6ffT3ffubltLfV8jOf2Z8zThlJktx78Ol89e5HkiQ/tP+yXL/vvCTJC69N5N995e6sb9Rz6bmn5Sd/8JoURXHM14D346KBvjy7sJQjK6t5YHI6+3eNlD0JAAAAtr1NeQdVtSjyE5+4Ov/r3/l8/qef/eHc+eBf5dXxmdx6z7fyobNOya/+8o/nQ2edkj+991tJkm8/80qOTM3ln/7Sj+Vv/dD1+Y+33pskWVxezR/f9XB+5ec+k1/5uc/kj+96OIvLq0mS//gn9+W//uEb8k9/6cdyZGoujz7zSpIc8zXg/agURfaPDadaFHlibiGvLa+UPQkAAAC2vU0JVAN93W+8A6qzoy17RgYyM7+Ug0++mOs/fPQdUNd/+Lw88sSLSZKDT76Y6/adm6Iocs6pu7K8spbZ+aU89uwruejsvenp6khPV0cuOntvHnv2lczOL2VlbS3nnLorRVHkun3n5pEnv/uz3u414P0a6mjPvqH+JMndRyaz0XCrHwAAAHwQm34G1cTMfF46PJWzTx3N3OJyBvq6kyT9vV2ZW1xOkszML2Wov+eN7xns78nM/FKmv/fxvp5Mzy8d/fq+tz4+M3/0EOtjvQZ8EJcNDWSovS1z6xt5aHq27DkAAACwrW3KGVTfsbK2nt/84h35yU9ek66O9rc8VxRFiqI4qa//Tq9x1zefyF0PPZkk+dnPXJ+9u0dP6ha2v0+3tedLz72Up5bXcllbe8a6Ok/K6wz095+UnwscP9chlMs1COVzHUK5WuUaXFhYOOZzmxao6vVGfvOLt+eaS8/JFR86M0nS39OV2fmlDPR1Z3Z+KX3dR/8Df7CvO9Nzi29878zcYgb7ujPU150nXzj03cfnF3PBmXuOfv384lseH/zOu6aO8Rrf66YrL8xNV16YJJmdm8vs3NyJ/QOg5XQlOaezPY/Ozuerz76QHz1tTyonOLIO9Pf7dxFK5jqEcrkGoXyuQyhXK12D1cqxb+TblFv8ms1mvvDHB7JnZCA/cO0lbzy+74LTc++3nk6S3Putp7PvgjOOPn7+6bnv4DNpNpt59pUj6exoz0Bfdy4+59Q89uyrWVxezeLyah579tVcfM6pGejrTmd7e5595UiazWbuO/jMd3/WMV4DToSrRgbTV6tlcnUt35ppjb8wAAAAYLMV9aWXmif7RZ5+6XB+/Qt/klN3DeU77y/57C1X5ey9o/mtL92ZqdmFjAz05hd+7GPp6epIs9nMf/rT+/PoM6+kva2an/nM/py59+gtdwcefiq33nMwSfLpG/flhsvOT5K88OpE/t0f3Z219XouOffU/NRfuzZFUWRhaeVtX+OdtEqZZHO8srScW189kmpR5POnn5KB9rYT9rNbqZTDduU6hHK5BqF8rkMoVytdg9VKJb29vW/73KYEqu2mVf6PZ/PceXgiT88v5pSuznx6764Tdp5aK/1FBNuV6xDK5RqE8rkOoVytdA2+U6Da9E/xg1Z07ehQuqrVvLa8kifmjn3oGwAAAPD9BCo4ATqr1Vw3OpQkeWByJosbGyUvAgAAgO1DoIIT5Oze7pzR05W1RiP3jk+l2XT3LAAAABwPgQpOkKIocsPYcNorlbywuJznFpfKngQAAADbgkAFJ1BPrZarRwaTJPeNT2e1Xi95EQAAAGx9AhWcYBf292ZPV0eW6/XcPzFd9hwAAADY8gQqOMGKosj+sZFUiyJPzS/mlaXlsicBAADAliZQwUkw0N6WK4YHkiQHjkxlvdEoeREAAABsXQIVnCSXDvZnpKM98xsb+ebUbNlzAAAAYMsSqOAkqRZF9o8Np0jy6MxcxldWy54EAAAAW5JABSfRaGdHPjzYn2aSu49Mpt5slj0JAAAAthyBCk6yK4YH0t9Wy9Taer41PVf2HAAAANhyBCo4yWqVSm4cG0mSPDw9m+m1tZIXAQAAwNYiUMEm2NvdmQv7e1NvNnPgyFSabvUDAACANwhUsEmuHhlKd62awyureXx2oew5AAAAsGUIVLBJOqqVXD86nCT5xtR0FtY3Sl4EAAAAW4NABZvorN7unNXbnfVGM/eMu9UPAAAAEoEKNt31o8Npr1Ty0tJynl1YKnsOAAAAlE6ggk3WXavm2tGhJMl9E1NZqddLXgQAAADlEqigBOf39WRvV2dW6o3cPzFd9hwAAAAolUAFJSiKIjfuGk6tKPL0/GJeWlwuexIAAACURqCCkvS3teXK4cEkyT3jU1lrNEpeBAAAAOUQqKBElwz2ZbSjPQsbG3lwcqbsOQAAAFAKgQpKVCmK7N81kkqSx2fnc3h5texJAAAAsOkEKijZSEd7PjzUn2aSu8cns9Folj0JAAAANpVABVvA5UODGWxry8zaeg5Oz5Y9BwAAADaVQAVbQK1y9FP9kuSR6dlMra6VvAgAAAA2j0AFW8Sers5cNNCXRpK7j0ym0XSrHwAAADuDQAVbyEdGBtNTq2Z8dS2Pzc6XPQcAAAA2Ra3sAcB3tVcquWFsOP/6sSP5/x6fzvxsR3Z31/KLlxf51Dl6MgAAAK3Jf/HCFvPY4c4ceKErC+uNpGMqhxab+bX7mrn12UbZ0wAAAOCkEKhgi/mNh5tZWx5Ks1lJUV1JaotZqR99HAAAAFqRQAVbzOHFJKmmuTqUJCnap5PK2uuPAwAAQOsRqGCL2d3z+i/qPWmu96Yomik6JrKrxy1+AAAAtCaBCraYX7y8SGf16K+ba0Np1ttTq27ko+dOpdl0mx8AAACtR6CCLeZT51TyK9cV2dOTFCkyVh3Nx06vZrBnJY/Ozpc9DwAAAE64WtkDgO/3qXMq+dQ5yUB/f2bn5vLCwmj+4tB4HpiYzlhHR3Z3dZQ9EQAAAE4Y76CCbeDM3u58eLA/jSS3Hx7P8ka97EkAAABwwghUsE1cNTKY3Z0dWdyo584jE2k4jwoAAIAWIVDBNlEtityyZzSd1UpeWVrJI9OzZU8CAACAE0Kggm2kp1bLx3aPpkjy0NRsXllaLnsSAAAAfGACFWwzp3Z35fLhgTST3Hl4MksbG2VPAgAAgA9EoIJt6PKhgezt6sxyvZ7bDzuPCgAAgO1NoIJtqFIU+dju0XTXqjm0vJoHJ2fKngQAAADvm0AF21RXrZpbdo+mkuTgzFxeXFwqexIAAAC8LwIVbGN7ujpz1chgkqPnUc2tr5e8CAAAAN47gQq2uQ8P9ueMnq6sNRq5/dBENhrOowIAAGB7EahgmyuKIh/dNZK+Wi0Tq2v5+uR02ZMAAADgPRGooAV0VKv5+J7RVIsij8/O59n5xbInAQAAwHETqKBFjHZ25NrRoSTJ3eOTmVlzHhUAAADbg0AFLeRD/b05t7cn641mbjs0no1Go+xJAAAA8K4EKmghRVHkxl3DGWxry/Taeu4Zn0qz6dB0AAAAtjaBClpMW6WSj+8ZTa0o8tT8Yp50HhUAAABbnEAFLWiooz03jA0nSe4dn8rk6lrJiwAAAODYBCpoUef39+bC/t7Um0fPo1qtO48KAACArUmgghZ23ehQRjraM7e+kbvHJ51HBQAAwJYkUEELq1Uq+fju0bRXKnl+YSmPzs6XPQkAAAC+j0AFLa6/vS037RpJkjwwMZ0jK6slLwIAAIC3EqhgBzirtzuXDPSlkeT2QxNZqdfLngQAAABvEKhgh7h6dCi7OjuysLGRrx12HhUAAABbh0AFO0S1KHLL7tF0Vit5aWk5B2fmyp4EAAAASQQq2FF622r56K7RJMmDkzN5dWml5EUAAAAgUMGOc3pPVy4fGkgzyR2HJ7K0sVH2JAAAAHY4gQp2oCuGB7K3qzPL9XruODyZhvOoAAAAKJFABTtQpShy8+7RdFWreW15Jd+cmil7EgAAADuYQAU7VHetmlv2jKZI8sj0XF5cXCp7EgAAADuUQAU72CldnblqZDBJ8rXDk5lfdx4VAAAAm0+ggh1u32B/Tu/uymqjkdsPjafuPCoAAAA2mUAFO1xRFLl590h6a7WMr67l6xPTZU8CAABghxGogHRUq/n4ntFUiyKPzc7n2YXFsicBAACwgwhUQJJkrLMjV79+HtWBI1OZXVsveREAAAA7hUAFvOHigb6c3dudtUYjtx0az0ajUfYkAAAAdgCBCnhDURTZv2skA221TK2t517nUQEAALAJBCrgLSLFAL8AACAASURBVNorlXx8z1iqRZEn5xby1NxC2ZMAAABocQIV8H2GO9pzw9hwkuSe8alMra6VvAgAAIBWJlABb+uC/t5c0N+bjWYztx0az5rzqAAAADhJBCrgmK4fHcpwe1tm1zdy4Mhkms1m2ZMAAABoQQIVcEy118+jaqsUeXZhKY/POo8KAACAE0+gAt7RQHtb9u8aSZJ8fXI64yurJS8CAACg1QhUwLs6p7cnFw/0pd5s5vZDE1mt18ueBAAAQAsRqIDjcvXIUMY62jO/sZE7DzuPCgAAgBNHoAKOS61S5JY9Y+moVPLS0nIOzsyVPQkAAIAWIVABx62vrZaP7j56HtU3J2dyaHml5EUAAAC0AoEKeE/O6OnOvqH+NJLcfngiSxvOowIAAOCDEaiA9+yq4cGc0tWZpY167jw8kYbzqAAAAPgABCrgPasURT62eyRd1WpeXV7Jw1OzZU8CAABgGxOogPelu1bLx3aPpkjy8PRsXl5cLnsSAAAA25RABbxve7s7c+XwYJpJ7jwykYX1jbInAQAAsA29r0DVbDbTeNP/gJ3rsqH+nNbdlZV6I3ccnkjd3wkAAAC8R7Xj/cKZ+aX8pz+9L0+9eDhLK2tvee5f/6OfOeHDgO2hKIrcvHskf/DSazm8sppvTM7k2tGhsmcBAACwjRx3oPrdr96T9rZa/ru/+YP55//h1vzD/+bT+crXHs6l5532rt/7ha/cnW89/XL6ejrzT/7255IkX/naQ7n7oafS192RJPnsLVflw6//rFsPHMyBR55KpSjykz94bS4599QkyaPPvJzf/7Ovp9Fs5sbLz8+nbtiXJJmYmc9vf+nOLC6v5ow9I/m5z96UWrWa9Y16fufLd+XFQ5Pp6erIz3/+5owO9r23PyHgXXVWq7ll91i++sqhfHtmLrs7O3JWb3fZswAAANgmjvsWv2dfGc9Pf+bGnL5nJEWS03YP56c/c2P+4v5vv+v3Xn/Zefn7P/XJ73v8E9denH/8C5/NP/6Fz74Rp14dn8kDjz2Xf/K3P5e//199Mr93631pNBppNBr5vVvvz9/7qU/mf/k7n8sDjz6XV8dnkiT/5bYH84lrLs6v/vKPp7uzPQcefipJcuDhp9Ld2Z5f/eUfzyeuuThfuu3B4/3tAu/R7q6OXP36O6fuOjKZubX1khcBAACwXRx3oKoURSqVo1/e1dme+cWVtLfVMjO/9K7fe/4Ze9Ld1X5cr3PwyRdz9cVnp61WzehgX3YN9+X5Vyfy/KsT2TXcl7GhvtSq1Vx98dk5+OSLaTabeeL513LlRWclSa7fd14eefLFoz/rqRdz/b7zkiRXXnRW/ur519J0Pg6cNJcM9OWs3u6sNRq57fBENhqNsicBAACwDRz3LX5n7R3Nt59+OVd86Mxccs6p+e0v3ZG2Wi1nnjL6vl/8jm88nvu/9UzO3DOSH/+Bq9PT1ZHp+aWcc+rYG18z2NeT6dcj2FBfz3cf7+/Jc6+MZ3F5Nd2d7am+Hs8G+3veiGYz80sZ6j/6PdVKJV0d7VlcXk1vd+f73gwcW1EU2T82kqnVtUyuruW+iens3zVS9iwAAAC2uOMOVD/32ZvynTcf/fVPXpM/v+/RrK6v5xNXX/y+XvjmKz+UH95/WVIU+fIdD+WLf/FAfvpH9r+vn3Ui3PXNJ3LXQ08mSX72M9dn7+73H97gRBro7y97wnv2ua7OfPHZl/LC6nrOayQXDm6/3wO82Xa8DqGVuAahfK5DKFerXIMLCwvHfO64A1V3Z8cbv25vq+WHb7rsA43q7+1649f7rzg//+r3/zJJMtTXnem5xTeem5lfzFDf0cOWp+ff9Pjc0cd7ujqytLKWeqORaqWSmbnFDL7+9YOv/6yh/p7UG40sr66lp+u7v483u+nKC3PTlRcmSWbn5jI7N/eBfn9wIgz092/Lfxfbklze150D41P50+deTMdpezLUcXy3+cJWs12vQ2gVrkEon+sQytVK1+B37n57O+8YqL569yP5of1HQ9SX73zomF/3ozdf8Z5Hzc4vZeD1kPTwEy9m79hgkmTfBafn//2Dr+UT116S2YWlHJmay1l7R9NMcmRqLhMz8xns684Djz2X//ZzH01RFLnwzD355uPP5+pLzsm9B5/OvvPPOPqzzj899x58OuectivffPz5XHjWKSmK4j1vBd67C/t7c3hlNU/PL+a2wxP50dP2pO0d/jICAABg53rHQPXmA9Df/K6m9+q3v3RnnnzhUBaWV/Ir//L38yMfvTxPvnAoLx2eSlEUGRnozd/69PVJkr1jQ7nqorPyv/0/f5BqpchP/bXr3jic/W/8tevyL3/vz9NoNHPDZedl79jRTwz7/Mc/kt/+0p358p0P5fTdw7nx8vOTJDdefn7+7R/elf/5X30x3Z0d+fnP3/y+fw/Ae1MURW4YG87k6lqm19Zz4MhUbt49IhIDAADwfYr60ks+1u57tMpb59j+WuGtnNNra/nKy4ey3mjmxrHhfGigr+xJ8J60wnUI25lrEMrnOoRytdI1WK1U0tvb+7bPHff9Nv/DP/+Pb/v4//gv/tP7WwXsCEPt7blx7Ogn+d03MZ2JldWSFwEAALDVHHegqjca3/9YvZFG0xuwgHd2bl9PLhroS73ZzG2HJ7Jar5c9CQAAgC3kXT/F79e/8NUkRdY36vn1L/zJW56bmV/MOaeOnaxtQAu5ZmQo4yurmVhdy11HJvOJPWPOowIAACDJcQSqGy+/IM1mMy+8NvHG4eNJUiTp6+nKh8465WTuA1pErVLklj2j+cOXDuWFxeV8e2Y+Hx7qL3sWAAAAW8C7Bqrr952XRqORbz/9cj5y8dlpq1U3YxfQgvrb2vLRXSP5i0Pj+cbkdMY627Onq7PsWQAAAJTsuM6gqlQqeeKFQ6lWj/vIKoC3dWZvdz482J9GkjsOT2R5w3lUAAAAO91xF6frPnxuvvbgEydzC7BDXDUymN2dHVncqOfOIxM+bAEAAGCHe9db/L7j+Vcncvs3Hs+f3fetDPf35OgpVEf9w5/+9MnYBrSoavHd86heWVrJw9OzuXJ4sOxZAAAAlOS4A9X+Ky7I/isuOJlbgB2kp1bLzbtH8qevHsnDU7PZ3dmRU7u7yp4FAABACY47UF2/77yTuQPYgU7t7srlwwN5aGo2dx6ezGdP35Oe2nH/tQQAAECLOO4zqJrNZu566Mn8i/9wa371t/4wSfLUi4fyjceeO2njgNZ3+dBATu3uzHK9ntsPTaTuPCoAAIAd57gD1VfufCj3PPxk9l9xQaZmF5Ikg309+bN7v33SxgGtr1IUuXnXaHpq1RxeWc2DkzNlTwIAAGCTHXeguvfg0/m7f+MHcvUl56Qojh6QPjrYm4mZ+ZM2DtgZumrVfGz3aCpJvjUzlxcWlsqeBAAAwCY67kDVaDbT0f7Ws2FW1zbS0ea8GOCD29PVmY+MDCVJvnZkMnPr6yUvAgAAYLMcd6C69NzT8p///IGsb9STHD2T6st3fjMfPv/0kzYO2FkuHezLmT1dWWs0cvuhiWw0nEcFAACwExx3oPqJT16d2YXl/Pe//rtZXl3PP/i/fjeTs4v5sY9/5GTuA3aQoihy067R9LXVMrG6lq9PTpc9CQAAgE1w3PfndXW055f++sczt7icqdnFDPV3Z6C3+2RuA3agjmolH989mj965XAen53P7s6OnNvXU/YsAAAATqLjfgfVm/V0dWRtvZ7x6fmMTzskHTixRjs7ct3o0fOoDoxPZnptreRFAAAAnEzH/Q6qR595OV/4o3sy972frlUU+df/6GdO9C5gh7uwvzeHllfzzMJibjs0kR89bU/aKu+rqQMAALDFHXeg+r1b788P79+X6/adl3af3AecZEVR5MZdw5lcW8vM2nruGZ/KR3eNpCiKsqcBAABwgh332xGWVlZz05UXilPApmmrVPLxPaOpFUWenl/ME3MLZU8CAADgJDjuQHXj5efnnkeePplbAL7PUHt7btw1kiS5b2I6k6vOowIAAGg1x/12qOdeGc9tX388f3rvt9Lf0/WW5/7hT3/6hA8D+I7z+npyaHklT8wt5LZD4/nR005JR9V5VAAAAK3iuAPVjZdfkBsvv+BkbgE4putGhzOxupbJ1bXcfWQyH98z6jwqAACAFvGugeqvnn8tSTLU33PSxwAcS61S5OO7R/OHLx/K84tLeXR2PpcO9pc9CwAAgBPgXQPVv/+jA+/4fFEk//vf/YkTNgjgWPrb23LTrpH85aHxPDAxnbGOjuzu6ih7FgAAAB/Quwaqf/b3xCdg6zirtzuXDvbn2zNzuf3weD53+inprFbLngUAAMAH4JRhYNv5yMhgdnd2ZHGjnjsPT6bZbJY9CQAAgA9AoAK2nWpR5GO7R9NZreTlpeU8Mj1X9iQAAAA+AIEK2JZ622q5eddoiiTfnJrJq0srZU8CAADgfRKogG3rtJ6uXDY0kGaSOw5PZGljo+xJAAAAvA8CFbCtXTE8kL1dnVmu13P74Yk0nEcFAACw7QhUwLZWKYrcvHs03bVqDi2v5sGpmbInAQAA8B4JVMC2112r5pbdo6kkOTg9lxcXl8qeBAAAwHsgUAEtYU9XZ64cGUySfO3wZObXnUcFAACwXQhUQMvYN9if07u7stpo5PZD49loOI8KAABgOxCogJZRFEVu3j2Svlot46treWByuuxJAAAAHAeBCmgpHdVqbtkzmmpR5LHZ+Tw7v1j2JAAAAN6FQAW0nLHOjlwzMpQkuXt8MrNr6yUvAgAA4J0IVEBLumigN2f3dme90cxth8az0WiUPQkAAIBjEKiAllQURfbvGslAWy1Ta+u5d8J5VAAAAFuVQAW0rPZKJR/fM5ZaUeTJuYU8ObdQ9iQAAADehkAFtLThjvbcMDacJLlnfCpTq2slLwIAAOB7CVRAyzu/vzcX9Pem3jx6HtWa86gAAAC2FIEK2BGuHx3KcHtbZtc3cveRyTSbzbInAQAA8DqBCtgRaq+fR9VeqeS5haU8Njtf9iQAAABeJ1ABO8ZAe1tu3HX0PKoHJmdyZGW15EUAAAAkAhWww5zT25OLB/pSbzZz+6GJrNbrZU8CAADY8QQqYMe5ZnQoYx3tWdjYyJ2HnUcFAABQNoEK2HGqRZFb9oylo1LJS0vLOTgzV/YkAACAHU2gAnakvrZaPrp7JEny4ORMXlteKXkRAADAziVQATvWGT3duWyoP80ktx+ayNKG86gAAADKIFABO9qVw4M5paszy/V67jw8kYbzqAAAADadQAXsaJWiyMd2j6SrWs2ryyt5aGq27EkAAAA7jkAF7HjdtVpu2TOaIsnD07N5aXG57EkAAAA7ikAFkOSUrs5cNTKYJLnz8EQW1jdKXgQAALBzCFQAr9s32J/Tu7uy2mjk9sMTqTuPCgAAYFMIVACvK4oiH909kt5aLUdWVvPAxHTZkwAAAHYEgQrgTTqr1dyyZzTVosijs/N5bmGx7EkAAAAtT6AC+B67OjvykdfPo7r7yFTm1tZLXgQAANDaBCqAt3HJQF/O6u3OWqOR2w5PZKPRKHsSAABAyxKoAN5GURTZPzaS/rZaJlfXcp/zqAAAAE4agQrgGDqqlXx8z1iqRZEn5hby1NxC2ZMAAABakkAF8A5GOtpz/dhwkuSe8alMr66VvAgAAKD1CFQA7+KCvp6c19eTjWYztx2ayJrzqAAAAE4ogQrgXRRFkRvGhjPU3paZ9fUcODKZZrNZ9iwAAICWIVABHIe2ytHzqNoqRZ5dWMpfOY8KAADghBGoAI7TYHtb9o+NJEnun5jOxMpqyYsAAABag0AF8B6c09eTiwb6Un/9PKrVer3sSQAAANueQAXwHl0zMpTRjvbMb2zka86jAgAA+MAEKoD3qFYpcsue0bRXKnlxcTnfmpkrexIAAMC2JlABvA/9bW25effR86genJzJoeWVkhcBAABsXwIVwPt0Rk939g32p5Hk9sMTWd5wHhUAAMD7IVABfABXjgxmd2dHljbquePwRBrOowIAAHjPBCqAD6BaHD2PqqtazavLK3l4erbsSQAAANuOQAXwAfXUarl590iKJA9PzeaVpeWyJwEAAGwrAhXACXBqd1euGB5IM8kdhyeyuLFR9iQAAIBtQ6ACOEEuHxrIqd2dWak3cvuhidSdRwUAAHBcBCqAE6Qoity8azQ9tWoOvLaSH/nyVK7/9/V87r/Uc+uzjbLnAQAAbFkCFcAJ1FWrplgdyYGXk5n6XJrVpRxaTH7tvqZIBQAAcAwCFcAJ9nuPtmdtZTBJUnRMJbW5rNSb+Y2H3fIHAADwdgQqgBPs8GKSjd4013tSFI1UOmZSdB3K4ZWlNJ1LBQAA8H0EKoATbHdPkhRpro2ksTKWZqMtRWU9vX0TufXVI5laXSt7IgAAwJYiUAGcYL94eZHO6uv/UO9Kc3lP2jaGcv0p1by6vJI/eOm1HDgymeWNeqk7AQAAtopa2QMAWs2nzqkkaeQ3Hm7m8GKyu6f4/9u78+i4rsPO87/73qsq1F4ASIIENxFcRHEFqX2XZVmmLcm25C2dccd2HKfleNKZzkx60p6e7jPpnrbSJ5lpp5M+6tjttpOJovam2LKjxYolWQspyeG+SNwkkQQJbkDte9WbP6oAAiBAUhTAh+X7OYcHxVuvqm6RemLhh3t/Tw93x/SBxVFt7UvpzVRGb6azOpzNa0NbXNfEo7KN8XraAAAAAOAZAioAmACbuixt6jp//ObZbVoZj+iNM0kdzRf02pl+7UtldOOsVi0MBWUIqgAAAADMQGzxA4ArrNXv172dc3TvvDlK+HxKV6r6+YnT9FMBAAAAmLEIqADAIwvDQX1i0TzdNKtVAcsa7Kd69XQf/VQAAAAAZhS2+AGAh2xjtDoR09JoWNua/VT7UhkdyuTopwIAAAAwY1yRgOqvnnxZuw4eUzTcon/z25+QJOUKJX3ziRd0NplVeyKiLz94l8LBgFzX1feefV27Dx2T3+fo8/ffpkXz2iVJm3ce1N+/vEOS9NHb1uvmdcskSe+eOKPvPvmyKtWa1ixdoM/ce4OMMWO+BgBMNi22PdhP9fqZpI41+6neTGV0A/1UAAAAAKa5K7LF7+b1y/S7v/ahYWNPv7pLK6+ap3/3O5/Uyqvm6ZnNuyRJuw/16FRfWn/0lYf0P330Zj329GZJjUDrZy9t1x9+8X794Rfv189e2q5coSRJeuypLfrcfbfoj77ykE71pbXnUM8FXwMAJqtWv18fHtJPlWr2Uz1z4pT66acCAAAAME1dkYBq+aK5CgX9w8Z27j+im9c2VkDdvHaZdrx1ZHD8pnVLZYxR1/w5KhTLSmXy2nu4R9cs6VQ4GFA4GNA1Szq193CPUpm8iuWyuubPkTFGN61bqh37j1zwNQBgshvop7pxVqv8lqWefFFPNPupijX6qQAAAABML551UKVzBcWjIUlSLBJUOleQJCUzebXGwoPHJWJhJTN59Y8cj4bVn8k3jo8OH09m8hd8DQCYCmxjtCYR0zL6qQAAAABMc5OiJN0YM+HdKhd7jZe2vqWXtu2XJH3h/pvV2TFrQucDXKp4LOb1FOCxuKRNra26vljSqydP62g2rx2ZvN4pVXTr3NlaHAnTTzXBOA8Bb3EOAt7jPAS8NV3OwWw2O+Z9ngVUsXBQqUxe8WhIqUxe0VCLJCkRDak/nRs8LpnOKRENqTUa0v53e8+NZ3JasXhu4/hMbth4YmDV1BivMZrbN16t2zdeLUlKpdNKpdPj+n6ByxGPxfhvEYMcSbcnojrqd/T6mX6dzJT0o0xW80MturG9Va0B/0WfA+8d5yHgLc5BwHuch4C3ptM5aFtjN01dkQ6q0axbsVCbdx2UJG3edVDrVixqjC9fqC07D8l1XR3uOaWWgF/xaEiruuZr7+HjyhVKyhVK2nv4uFZ1zVc8GlKL36/DPafkuq627Dx07rnGeA0AmKqMMVoUDunBRZ3D+qn+7ugJbaafCgAAAMAUZWr5o+5Ev8i3nnhR+9/tVbZQVCwc1AN3dGv9ikX65hMvqi+VVXs8oi8/dJfCwYBc19Xjz7ymPYd65PfZ+vz9t2lxZ2O73SvbD+jpV3dKkj5y6zrdsn65JOnd42f03Z++rHKlptVL5+vXPnyjjDHK5oujvsbFTJdkElPfdErKMTGKtZq2NvupXEl+y6KfapxxHgLe4hwEvMd5CHhrOp2DtmUpEomMet8VCaimmunyF4+pbzr9jwgTq79U1mtn+9WTL0qSEj6fbpjVqoXhoMczm/o4DwFvcQ4C3uM8BLw1nc7BCwVUnm3xAwCMn9aAXx+eN0cfmjdbcZ+jZKWiZ0+c0jPHT6m/XPZ6egAAAABwQZPiKn4AgPdvoJ9qfiiofamMtvWldCxf0PEjBa2MR7WhLa4W2/Z6mgAAAABwHgIqAJhmbGO0JhHT0khY2/ob/VR7UxkdyuS0oS2ulfRTAQAAAJhk2OIHANNU0LF1y+w2PbhwnjqDLSrV69pypl9/d+SEjuYKXk8PAAAAAAYRUAHANNca8GtT5/n9VM/STwUAAABgkmCLHwDMAKP1Ux3NF9TT7Kfa2BZXgH4qAAAAAB4hoAKAGWRoP9XWvqTeSmfppwIAAADgObb4AcAMFHRs3TqnXZ8Y2U919ISO0U8FAAAA4AojoAKAGayt2U91z9zZivkcJcsVPdPsp0qWK15PDwAAAMAMwRY/AJjhjDFaHDnXT7W9n34qAAAAAFcWARUAQJLkWEZrW2NaFj2/n2pjW0Ir4xFZ9FMBAAAAmABs8QMADDNaP9XmM316gn4qAAAAABOEgAoAMCr6qQAAAABcKWzxAwCMaWg/1d5UWjv604P9VNfEo9pAPxUAAACAccAKKgDARTmW0brWuD65qFNXxyJyJe1JZfSDI8e1L5VR3XW9niIAAACAKYyACgBwyUKOrdvmtOvjC+dpXrBFxVpdr55u9FP15OmnAgAAAHB5CKgAAO9Ze8Cvj3TO0Qfnzla02U/19HH6qQAAAABcHjqoAACXxRijqyIhLRiln2pVIqbu1hj9VAAAAAAuCSuoAADvy2j9VLuTafqpAAAAAFwyAioAwLgYq5/q7+inAgAAAHARBFQAgHE1sp+qv9lP9fMTp5SinwoAAADAKOigAgCMu6H9VHtSae3oT+lIrqCefFHXxKPqbo0rYPMzEgAAAAANfHcAAJgwjmW0vjWuTy2ar6tjEdVdt9lP1aM36acCAAAA0ERABQCYcAP9VB9bOE9zgwEVa3W9Qj8VAAAAgCYCKgDAFTMr4NdHOzt099xZijr0UwEAAABooIMKAHBFGWO0JBLWwlDovH6qVfGo1tNPBQAAAMw4fAcAAPDEuX6qTq1o9lPtSqb1wyPH6acCAAAAZhgCKgCAp0KOo9uH9FMVajW9crpPPz56QsfzRa+nBwAAAOAKIKACAEwKI/up+soVPXX8pJ47cUpp+qkAAACAaY0OKgDApDFaP9W7uYKO0U8FAAAATGt8ygcATDr0UwEAAAAzCwEVAGDSGuynWjBXHS30UwEAAADTFQEVAGDSm9US0H3zO/SBEf1U/3DiNP1UAAAAwDRABxUAYEowxqgrEtaiUFB7Uhnt6E/pnVxeR/MFrY5Htb4tLr/Fz10AAACAqYhP8gCAKcWxrMF+quXRsGquq53JtH7wLv1UAAAAwFRFQAUAmJJCjqM7Ombp4yP6qX5yrFcnCvRTAQAAAFMJARUAYEob7KfqmKWI4+hsqay/72n2U1XopwIAAACmAjqoAABTnjFGXdGwFoWD2p2knwoAAACYavi0DgCYNhzLUndbXJ9efH4/1VvpLP1UAAAAwCRFQAUAmHYG+qk+NqSf6uVTZ+mnAgAAACYpAioAwLQ1e6x+ql76qQAAAIDJhA4qAMC0NrSfalcyrZ39ab2TzetorqDViajWt9JPBQAAAHiNT+QAgBnBsSxtaEvoU4s7tWygn6qffioAAABgMiCgAgDMKGHH0Z1j9FP10k8FAAAAeIItfgCAGWmgn+pwNq83zvbrbKmsn/Wc1FWRkK5vT+jVo7Ye3e7qZK5fHWHp4W6jTV38XAcAAACYCARUAIAZyxijpdGwFo/op/rFsbxeOhxRuRSTZKk3Jz2yxZVUJ6QCAAAAJgCfsgEAM97IfqrXT9RVddIywROSk5XkqliTHt1OTxUAAAAwEQioAABoGuinyqY75NYCMlZNVqCvGVTldDJHQAUAAABMBAIqAABG6AgG5BbnqF5sl1v3yVhVWYGzisV7dTiTk8sV/wAAAIBxRUAFAMAID3cbtdhGqoXlFuaqXmqXI0cb51X1/MkzeuLoCb2TzRNUAQAAAOOEknQAAEZoFKHXm1fxM+oIhPXb68PqmpXXjv60+ssV/UPvabUH/NrYFtfCUFDGGK+nDQAAAExZBFQAAIxiU5elTV1SPBZTKp1ujka1LBrR/nRWO5IpnS2V9fMTpzWnJaCNbXF1BlsIqgAAAIDLQEAFAMB74FhGqxJRrYiF9WY6q539aZ0qlvT08VPqaAno2vaE5gVbvJ4mAAAAMKUQUAEAcBkcy9KaRExXxyLam8poV39aJ4sl/X3PSXUGW7SxLaGOYMDraQIAAABTAgEVAADvg8+ytL41rmviUe1JprU7mdHxQlHHe3q1IBTUtW1xzWohqAIAAAAuhIAKAIBx4LcsbWhLaFU8qt3JjPak0jqWL+hYvqBF4aA2tiXUHvB7PU0AAABgUiKgAgBgHAVsW9e2J7Q6EdWu/rT2pjI6kivoSK6gJZGQNrTF1eonqAIAAACGIqACAGACtNi2rp/VqtWJmHb2p/RmOqu3s3m9k81raTSs7ta44n6f19MEAAAAJgUCKgAAJlDIsXXT7DatbY1pR39a+9NZHczkdDiT07JYROtbY4r5CKoAAAAwsxFQAQBwBYQdR7fMbtPaREw7+lM6kM42yPxaRAAAIABJREFUwqp0VstjEXW3xhXx8c8yAAAAZiY+CQMAcAVFfY5um9OudYmYtvWndCiT01vNVVVXN1dUhRz+eQYAAMDMwidgAAA8EPP7dGfHLK1vjWtbX1KHs3ntTWX0VjqrVfGo1iZiCjq219MEAAAArggCKgAAPJTw+/SBubO1vlTWtr6U3snltSuZ1pvpzGBQFbAJqgAAADC9EVABADAJtAX8+uC82TpTKmvr2aSO5gva0Z/WvlRWqxNRrY7HFLAtr6cJAAAATAgCKgAAJpFZAb/u7ZyjU8WStvYl1ZMvaltfSnuTGa1tjWlVPCqfRVAFAACA6YWACgCASWhOS0CbOjvUWyhqa19KJwpF/epsUruTaa1LxHVNPCKHoAoAAADTBAEVAACT2Nxgiz7SGdDxQmMl1cliSa+f7deuZFrrW2O6OhaVYxmvpwkAAAC8LwRUAABMcsYYzQ8F1Rls0bF8UVv7kjpTKmvLmUZQ1d0a1/JYRLYhqAIAAMDUREAFAMAUYYzRwnBQC0ItOpIraGtfUn3lil453aed/Wl1t8W1LBqWRVAFAACAKYaACgCAKcYYo8WRkBaFg3o7l9e2vpSS5YpeOnVWO/pT2tCWUFckRFAFAACAKYOACgCAKcoYo65IWFeFQzqczWtbX1LpSlUvnjyjHf0+bWiLa0k4JENQBQAAgEmOgAoAgCnOMkbLomF1RUI6mMlpe3NF1fO9Z7TD79PGtoQWhYMEVQAAAJi0CKgAAJgmLGO0IhbR0mhYB9JZbe9Pqa9c0XO9pzUr4NfGtoQWhFoIqgAAADDpEFABADDN2MZoZTyqZdGI3kpntKM/rTOlsp49cUodLQFtaIurM0hQBQAAgMmDgAoAgGnKsYxWJ2K6OhbRvlRWO5MpnSyW9PTxU5oXbNHGtrjmBlu8niYAAABAQAUAwHTnWJbWtsZ0dTyifamMdvWndaJQ1M96ipofatHGtoTmtAS8niYAAABmMAIqAABmCL9laX1rXCtjUe1JpbUnmVFPvqiefK8WhoLa2J7QrIDf62kCAABgBiKgAgBghgnYlja2JbQ6HtWuZFp7UxkdzRd0NF/QVeGQNrTF1UZQBQAAgCuIgAoAgBkqYNu6rr1Vq+OxwaDqnVxe7+byWhIJaUNbQgm/z+tpAgAAYAYgoAIAYIYLOrZumNWqNYmodvSn9VY6q8PZvN7O5rU0GtaG1rhiBFUAAACYQARUAABAkhRyHN08u01rEzFt70/pQDqrg5mcDmdyWh6LaH1rXFEfHx0AAAAw/viUCQAAhon4HN02p13rWmPa3pfSwUxObzXDqhWxiNa3xhR2+AgBAACA8cOnSwAAMKqYz6c7OmZpfWtc2/pSOpzNaV8qo/3prFbGIlrXGlfIsb2eJgAAAKYBAioAAHBBcb9Pd82dpfWlmLb2p/RONq89qYzeSme1Kh7V2taYWmyCKgAAAFw+AioAAHBJWgN+fXDubJ0tlbW1L6kjuYJ2JtPal85odTymNYmoAgRVAAAAuAwEVAAA4D1pD/j1oXlzdLpY0ta+lI7lC9ren9LeVEZrElGtTsTktyyvpwkAAIAphIAKAABcltktAX24c45OFkra2pfU8UJRW/tS2pPMaG1rTKviUfkIqgAAAHAJCKgAAMD70hEM6CPzO3SiUNQ/nk3qZLGkX51Nak8yo3WtMa2MReQQVAEAAOACCKgAAMC4mBds0X3zO3S8GVSdLpX12pl+7UqmtT4R14pYRI5lvJ4mAAAAJiECKgAAMG6MMZofCqoz2KKj+YK29qV0tlTW5jN9jaCqNablsYhsQ1AFAACAczwPqL72599Xi98nyxhZlqWvfekB5QolffOJF3Q2mVV7IqIvP3iXwsGAXNfV9559XbsPHZPf5+jz99+mRfPaJUmbdx7U37+8Q5L00dvW6+Z1yyRJ7544o+8++bIq1ZrWLF2gz9x7gwwfigEAmFDGGC0Kh7QwFNS7uYK29iXVX67oldN92plMa0NrXEujYVn8mwwAAABNgoBKkn7/c5sUCbUM/v7pV3dp5VXztOmWdXr61Z16ZvMuPXT3ddp9qEen+tL6o688pLePn9ZjT2/WH37xfuUKJf3spe36V7/5gCTp699+UuuWL1Q4GNBjT23R5+67RUs6Z+vPH39Oew71aM2yBV69VQAAZhRjjK6KhLQoHNTb2by29SWVqlT1y1NntaM/pQ1tCXVFQvzwCAAAYIablI2lO/cf0c1rGyugbl67TDveOjI4ftO6pTLGqGv+HBWKZaUyee093KNrlnQqHAwoHAzomiWd2nu4R6lMXsVyWV3z58gYo5vWLdWO/Ue8fGsAAMxIljFaGg3roUWdumNOu6I+R6lKVS+cPKMnjp7Q29mcXNf1epoAAADwiOcrqIyMvvHYszLG6PYNK3T7xquVzhUUj4YkSbFIUOlcQZKUzOTVGgsPPjYRCyuZyat/5Hg0rP5MvnF8dPh4MpO/Qu8MAACMZBmj5bGIuqJhHUhntaM/rf5yRb/oPaP2gF8b2+JaGAqyogoAAGCG8Tyg+t9+4yNqjYWVzhX0jcee1dxZ8WH3G2OuyIfUl7a+pZe27ZckfeH+m9XZMWvCXxO4FPFYzOspADMe5+HEuDEe17Xz52lfMq2tp/uUq1b1Ul9acwpl3TCnXQvDbP1DA+cg4D3OQ8Bb0+UczGazY97neUA1sPIpFg6q++pFevv4GcXCQaUyecWjIaUyeUWb/VSJaEj96dzgY5PpnBLRkFqjIe1/t/fceCanFYvnNo7P5IaNJ5ors0a6fePVun3j1ZKkVDqtVDo97u8VeK/isRj/LQIe4zyceIscS50dbXozndXO/rSOJFM6kkypoyWgjW0JdQ7pqcTMwzkIeI/zEPDWdDoHbWvspilPO6hK5YqKpcrg7X2Hj2v+7ITWrViozbsOSpI27zqodSsWSZLWLV+oLTsPyXVdHe45pZaAX/FoSKu65mvv4ePKFUrKFUrae/i4VnXNVzwaUovfr8M9p+S6rrbsPDT4XAAAYPJwLEtrEjF9enGnrmtPKGBZOlks6anjJ/VUz0n1FopeTxEAAAATyNMVVOlcUY/+4BeSpHrd1fWrl2j10gVaPG+WvvnEi3pl+wG1xyP68kN3SZLWLFug3Yd69H/+lx/J77P1+ftvkySFgwF99Lb1euS//1SSdN/t6xUOBiRJv77pJn33py+rXKlp9dL5WrN0/pV/owAA4JL4LEvrW+O6Jh7VnmRau5MZHS8UdbynqAWhoDa2xTW7JeD1NAEAADDOTC1/lEvmjDBdls5h6ptOSzmBqYrz0FulWk27kxntSaVVqTc+siwKB7WxLaH2gN/j2eFK4BwEvMd5CHhrOp2DtmUpEomMep/nHVQAAABjCdi2rm1PaHUiql39ae1NZXQkV9CRXEFXRULa2BpXK0EVAADAlEdABQAAJr0W29b1s1q1OhHTzv6U3kxn9U42r3ezeXVFwtrQFlfc7/N6mgAAALhMBFQAAGDKCDm2bprdprWtMe3oT2t/OqtD2ZwOZ3NaFg2ruy2umI+gCgAAYKohoAIAAFNO2HF0y+w2rU3EtKM/pQPprA5kcjqUyWl5LKLu1rhePmrp0e2uTuakjrD0cLfRpi5PL2AMAACAMRBQAQCAKSvqc3TbnHatS8S0rT+lQ5mc3kpn9dS7Wb30TljlUkySrd6c9MgWV1KdkAoAAGASIqACAABTXszv050ds7S+Na5tfUk9vi+rqp2RCWalul9ybZXqjv7zTlurOwKK+GxFHUeORVgFAAAwGRBQAQCAaSPh9+kDc2fra6mI5E/LOHnJLg3e31+Xnj1xLpQK2raiPkcRx1bE5yjqOIo4jiLNMQIsAACAK4OACgAATDsdIb96c7PklmuSqTZ+WVW1BmvqDNaVrVaVq9ZUqDV+nRrjeQYCrKjjKOKzFXGcZqDlKOw4cixzRd8XAADAdEVABQAApp2Hu40e2eKqWLMl15YUUIuk31trtGl+Y1VU3XVVqNWUqVSVqVSVrVaVrdaUrVSVqVaVq1SHBFilUV8n5NhDVl3Zivp8ijTHQgRYAAAAl4yACgAATDuNIvT6Ba/iZxmjcHMl1Nzg+c9Rd13lqzVlq40AK1OtKls5F2LlqlXlqzXlqzWdHCXAMmoEWENXXQ1sHYz6Gq9rGwIsAAAAiYAKAABMU5u6LG3quvzHW8Y0AiXf2AFWrhlgZUcEWJlmgJWr1pSr1nSyOHqAFR66dXAwwGqMEWABAICZhIAKAADgMljGNPqpfI40SoBVc13lqlVlKyNCrOZYbuB2tSqNsgLL0kCANaLEfbADy5ZFgAUAAKYJAioAAIAJYBujmM+nmM836v0111VuMLSqNVdfnevDyldrylQb949maIA1eCXCIdsJQwRYAABgCiGgAgAA8IBtjGJ+n2L+0QOsat1VvnouwMpUKoOrsTIjAqwThfMfb0kKD1t1NaTE3ecoaBNgAQCAyYOACgAAYBJyrIsHWAPbBAevQlhphlbNKxAOXKFQowRYtjEKj1HiHmsGWIYACwAAXCEEVAAAAFOQYxnF/T7Fxwyw6sO3Do4ocS/UakpXqkpfIMAa6L4aFmI5jqI+mwALAACMKwIqAACAacixLCX8lhIXCLAyo5W4N0OsQq2mVKWqVGX0DizbmGHbB0eWuAdtiwALAABcMgIqAACAGcixLLX6/Wr1j35/pV4/t23wvFVYVRVrdSUrFSUrlVEfPzTAio4MsRxHLRcJsJ4+XNej212dzPWrIyw93G20qcsaj7cOAAAmIQIqAAAAnMd3kQCrXK+fuwrhQHn7kFVYpfqFAyzHmBGrrhoBVsxxtPmYpT953ahUawRYvTnpkS2upDohFQAA0xQBFQAAAN4zv2XJH/CrNTB6glWu18/1XzW3DWZHBljlipLl8wOsx/fVVQ5YMq4t1R3JdVR0bf35DkfXdvovaQUWAACYWgioAAAAMO78lqW2gF9tYwRYpVp9lO6rRpCVK5dlTF0ydclqBFhGUp8r/eRYYwWVY8ywKw9GKHEHAGBKI6ACAADAFRewLQVsv9pHCbC+80ZNvbm6ZFUlU5VMTcaqKtFSU3ugfklbCG1jFHbswSsQDr0dcRyFHFsWARYAAJMGARUAAAAmlYe7jR7ZYqlY80tqBFgBW/q9tUabFjZWUA3dQjjYgVWtKjfkKoTpSlXpSlUqnP8alqTw0BVYQ65AGPHZCjuObAIsAACuGAIqAAAATCqNIvSBq/hp1Kv4XWwL4cBVCHNDwqtspapctaZMtap882umWh318UZS6AIrsMKOI8ciwAIAYLwQUAEAAGDS2dRlaVOXFI/FlEqn3/PjL3YVwmrdVa56rvfqXAdWY0VWrtoIs3LVmk4WS6M+R9C2m4HV+SuwIo4jn8UVBwEAuFQEVAAAAJhxHMso7vcp7veNen/NdQdDqqFF7gMrsHKVqgq1xlbCU2O8RottDQ+uHHvYCqyATYAFAMAAAioAAABgBNsYxXw+xXw+KXj+/XXXVb5aG2MFVmOsWKurWCvrTKk86mv4LWtwBVZ4yPbBgV6sgGVxJUIAwIxBQAUAAAC8R5YxjSDJN/rHadd1VajVBsOrkSuwspWqyvW6zpbKOjv6DkL5LNNceTWkzN3nKNpcgRW0CbAAANMHARUAAAAwzowxCjmOQo6jOS2B8+53XVfFWl25wQL32nkrsMr1uvrLFfWXK6O+hm2Mos3wKjxk++BAD1bItgmwAABTBgEVAAAAcIUZYxR0bAUdW7M0eoBVrtcvuAKrVK8rWakoWRk7wAoPK3AfelXCRqhlEWABACYJAioAAABgkjHGKGDbCti22gOjX4qwXK8rVxl7BVahVlO6UlW6UpUK5z/ekhQeugJrRKF72OfIJsACAFwhBFQAAADAFOS3LPkDfrWOEWBVh6zAyja3EuaaQVamWlW+uRorU62O+ngjKTSwAqu5bXBkJ5ZjjX0lwqcP1/Xodlcnc1JHWHq422hTF1cuBACMjoAKAAAAmIYcy1LCbynh9416f7XuKj/QgXXelQhrylUbWwpz1ZpOavQm96BtN7cMDl+B9XqP0Z/9ylap1gikenPSI1tcSXVCKgDAqAioAAAAgBnIsYxifp9iYwRYNddVvlob3DaYaQZWg7crVRVqja2EIz3+Vl2VFsm4tlS3JVkqubb+bJelea0+BW1bLbbV/GoraFsXXI0FAJj+CKgAAAAAnMc2RlFfo1RdwfPvr7uuCrWaMpXzV2DlShW5pipjapJ9LsBK1qRfnR09iPJZphlWDQ2vrFHGGrcpeAeA6YWACgAAAMB7ZhnTvBrg+d9SPGrV1Jtz5Zq6ZGrNX3W1B2tal5AKtZqKtfqwr5W6q0q9qkxl9E6skUYPr4aHWAO3/ZaRIdACgEmNgAoAAADAuHq42+iRLVKxZkuuLUlqsaXfXWd0/azzV1C5rquK66pYralQq6vY3Dp4LsQaHmgVB7/WlVTlovOxjblgoBVguyEAeI6ACgAAAMC4ahShX/pV/Iwx8hsjv99S7BKev+66Kg0GWRcPtMr1+mDh+6VguyEAXHkEVAAAAADG3aYuS5u6Jua5LWMUdGwFHVutl3B8te42Q6uxAq3hwdZ72W5oJAXYbggA7xsBFQAAAIBpzbGMIpajiO/i3/64rqtyM9AauqVwrECr1NxqOF7bDQe2GbY0bzsWYRaAmYGACgAAAACajDEK2EYB21JcvoseX3fdMUKs0QOtSt19T9sN/ZZ10W2GA4FWgO2GAKYwAioAAAAAuEyWMQo5tkKOfUnHV+v1MbcZnjdWralcb3RopS9xu2HLJfRmBW1LLY4tn7nwdsOnDw/0iPVftEcMAN4vAioAAAAAuEIcy1LUshS95O2GYwdagyu1qo3bpXrja6FWU/8lbjcca5vh1l6jb+8wKtVsyVjqzRl9fUsjnCKkAjARCKgAAAAAYBJqbDe0FbBt6RK2G9ZcV6VhRfDngq3SGNsNs9WqsqMsznr8YF0VvzQ0iqpI+n/2WEpZjvyWkc+y5LcsBezG14FfPsvIb1sKDBtrfKVTC8BYCKgAAAAAYBqwjVHIcRS6xO/yKvX6mCXw2UJFMjXJ1CXVJVOXMXVlK3VlqxffbnihOfqtZnhlN8OsgaBrYMw0vvqtkcdaF92WCGDqIqACAAAAgBnI11zZNNp2ww6npt7c8DFXrjrCrj6z2Ax2Y5Vr9XO363WV6+7Y99Xqqrnu4DbES9iFeB6jRnG8f9iqrXMhl3/Eaq5zY+eOoUgemJwIqAAAAAAAwzzcbfTIFlfFIRcbbLGNvtJtKeq7vA4q13VVa/Zqlep1lWuN25VmgFUaEmRVhgRdQ8errqtSc+xyDV21daEga/TQy8hmFRcwIQioAAAAAADDNIrQB67ip3G5ip8xRo4xcixLoct8jprrNgKt2rnwqjJi1dZAoFUZuqKrNnBs4/hKvaacahd/wVEMbFMcCLN8zRBreOeWGaOfq/E4Ai7gfARUAAAAAIDzbOqytKlLisdiSqXTXk9HUiMcsptXGrwcruuq4rqjb008b2zkVsVG2HVum+LlvQejxvbK0Xq4RnZuDevhGhKG2ZcQcD19eHwDRmCiEVABAAAAAGYEY4z8zRVQl6s6dMVWc1VW6bzOrcZKr9KI0Ksy4rG6zL55x5jBrYcBa3iY5bctbes1+pvdUqlmSbal3oLR1183ylUtfWiJLcc0rqjoGEMnFyYNAioAAAAAAC6RYxk5lq2QLm8VV725TbE0ZOXWqD1cQ1Z6Ddu22OziqlZryo+xTfH7h+uq+CTLd26sIunRt6TkiHDONo1erYHAamAbpjNyvDnmGCO72cU1cnzgWNsyjRBsyBjbGnExBFQAAAAAAFwhljEK2LYC72ObYtUd2a01fGXWt3JVydQbv+QOfs3VXUUd0wi43Lqq9UZxfaO8flzf5nnsESHWpYRgjdvWkNu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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LjqQ_6FOHixj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "a7a92235-7b92-4e3b-fc2a-86063ede046b"
      },
      "source": [
        "model = KMeans(n_clusters = 5,\n",
        "            init='k-means++',\n",
        "            max_iter=500,\n",
        "            random_state=42)\n",
        "model.fit(X2)\n",
        "labels = model.labels_\n",
        "centroids = model.cluster_centers_\n",
        "y_kmeans = model.fit_predict(X2) \n",
        "\n",
        "plt.figure(figsize=(20,10))\n",
        "plt.scatter(X2[y_kmeans == 0, 0], X2[y_kmeans == 0, 1], s = 100, c = 'red', label = 'Cluster 1')\n",
        "plt.scatter(X2[y_kmeans == 1, 0], X2[y_kmeans == 1, 1], s = 100, c = 'blue', label = 'Cluster 2')\n",
        "plt.scatter(X2[y_kmeans == 2, 0], X2[y_kmeans == 2, 1], s = 100, c = 'green', label = 'Cluster 3')\n",
        "plt.scatter(X2[y_kmeans == 3, 0], X2[y_kmeans == 3, 1], s = 100, c = 'cyan', label = 'Cluster 4')\n",
        "plt.scatter(X2[y_kmeans == 4, 0], X2[y_kmeans == 4, 1], s = 100, c = 'magenta', label = 'Cluster 5')\n",
        "plt.title('Clusters of Customers - Annual Income (k$) X Spending Score')\n",
        "plt.xlabel('Annual Income (k$)')\n",
        "plt.ylabel('Spending Score')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "VYwEOpIVlCGa"
      },
      "source": [
        "# Final Clustering\n",
        "\n",
        "## By Age, Annual Income and Spending Score\n",
        "\n",
        "Here we have an example of a multi-dimensional K-Means Clustering Analysis. The curve of inertia here is harder than the previous ones to interpret, but a visual inspection at the plot is of help in these situations.\n",
        "\n",
        "After calculating the respective clusters, it's a good idea to append this information at the dataset. The final result can be seen at the second graph below."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-v0n2ap1JRSG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        },
        "outputId": "838e6ad4-a358-4f49-ff64-25b64a99d18c"
      },
      "source": [
        "# Assignment Stage\n",
        "\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "X3 = df.loc[:, ['Age', 'Annual Income (k$)', 'Spending Score (1-100)']].values\n",
        "inertia = []\n",
        "for n in range(1 , 11):\n",
        "    model = KMeans(n_clusters = n,\n",
        "               init='k-means++',\n",
        "               max_iter=500,\n",
        "               random_state=42)\n",
        "    model.fit(X3)\n",
        "    inertia.append(model.inertia_)\n",
        "\n",
        "plt.figure(1 , figsize = (20, 10))\n",
        "plt.plot(np.arange(1 , 11) , inertia , 'o')\n",
        "plt.plot(np.arange(1 , 11) , inertia , '-' , alpha = 0.5)\n",
        "plt.xlabel('Number of Clusters') , plt.ylabel('Inertia')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Ms6DSvNecbOCWtmYA9vQOMlWrRZxIkiRJkiTNJAsqLQg3tORpTyUZqVR4fqAQdRxJkiRJkjSDLKi0IMSDgO1d7cSAQ8Mj9I5PRB1JkiRJkiTNEAsqLRgdqSQ3tOYJgV29A1RqYdSRJEmSJEnSDLCg0oJyS2szzQ0JCpNTHCgMRx1HkiRJkiTNAAsqLSiJWIztXe0AHBgqMjQxGXEiSZIkSZL0YVlQacHpTjdyXb6Jahiyu2+QWuhWP0mSJEmSFjILKi1ImzpaySbi9I5PcGh4JOo4kiRJkiTpQ7Cg0oKUjMXY2tkGwPMDBYpTUxEnkiRJkiRJH5QFlRasFdkMq5oyVMKQvX2DhG71kyRJkiRpQbKg0oJ2R0cbqViM0+Vxjo2Uoo4jSZIkSZI+AAsqLWjpRJzbO1oBeLZ/iHKlGnEiSZIkSZL0fllQacFbnctyVSbNRK3GM/2DUceRJEmSJEnvkwWVFrwgCNja2UZDLOD10TJvjJajjiRJkiRJkt4HCyrVhVxDglvbWgDY2z/IRLUWcSJJkiRJkjRdFlSqG9c351jSmKJcqbJvYCjqOJIkSZIkaZosqFQ3YkHAtq424kHA4eIoZ8rjUUeSJEmSJEnTYEGlutKaTHJTax6APX0DVGpu9ZMkSZIkab6zoFLd2dDaTFuygeJUhRcHh6OOI0mSJEmS3oMFlepOPAjY3tVOABwsFOkfn4g6kiRJkiRJehcWVKpLnY0pPtKSJwR29Q5QDcOoI0mSJEmSpCuwoFLd2tjWTK4hweDkFC8XilHHkSRJkiRJV2BBpbrVEIuxrbMNgBcHhylMTkWcSJIkSZIkXU5iLk4yWCzxN9/bRbE0RkDA9lvWcs/m9Xz/Jy+y+8Wj5DIpAB68+1ZuXH0VAI/uOcCel44SCwK+sON2PnLtMgBeee0U3975HLUwZNvNa/j41g0A9BdG+MZDT1Eam2BFdztfevBOEvE4U5Uqf/O9XZw8N0A2neLLn72LjpbcXHzZmgeWZdKszTdxpDjK7t4BPrVsCUEQRB1LkiRJkiS9zZwUVPEg4Nfu2cSKnnbGJ6b4D//l+1x/zVIA7rl9PTvuuOEdjz/TV2Dfodf509/9DMOjZf6vv9vJn/3BZwH41qPP8q9/cwet+Qz/+395mA1rVrC0s4V/euJ57tm8nk0fWcXfPbKXPfuPctet17Fn/1EyjUm+9tXPs++V4zz0xPN85XO/MhdftuaJze0tvFka4/z4BK8Oj7LeglKSJEmSpHllTrb4NecyrOhpB6Ax1UB3ezOFkfIVH3/gyEk2rb+GhkScjpYcXW05Tpzp58SZfrracnS25kjE42xafw0HjpwkDEMOnzjLxutXArBlw2peOnLywmsdPcmWDasB2Hj9Sn524iyhN8xeVFLxOFsvbvX76eAQo1OViBNJkiRJkqS3m/N7UPUXRnjz/CDXLOsA4Mc/fZWv/dV3+eb3d1MamwBgaKRMaz576TktuSxDI+UL67m3recvrJfGJsg0JonHYpfW3yrACm97rXgsRjqVvHQeLR4rmzKszGaYqoXs7Ru0pJQkSZIkaR6Zky1+bxmfnOIvv/NjvnDfZtKpJHdtvI5Pbb8JgoDv/fhFvvPDfXzx09vnMtIlu144zK4XjwDwOw9sYemSjkhyaPbcn87wrddO0FetcT4MWNecjzrStDTnF0ZOqZ45h1K0nEEpes6hFK16mcHR0dErHpuzgqparfGX33mSzTes4pbrrgYg35S+dHz7LWv4T9/+EQCtuQxDxdKlY4WREq25DABDI29bL15Yz6ZTlMcnqdZqxGMxCsUSLRcf33LxtVrzWaq1GmMTk2TTqV/Kd+fGddy5cR0Aw8Uiw8XiDH8HNB/c1JRhV+8AP3rjTZprS0kn4lFHelfN+bx/F6WIOYdStJxBKXrOoRSteprBt3a+Xc6cbPELw5Bv/vMeutubuff2j1xaH37bfaj2Hz7J0s4WADasXc6+Q68zVanSXxihd7DIyqUdXL20g97BIv2FESrVKvsOvc6GtcsJgoB1V3fzwqsnAHj6wDE2rFlx4bXWLOfpA8cAeOHVE6xb2eOnuC1ia3JZlqYbGa/WeLZ/KOo4kiRJkiQJCKrlN2f9ZjzH3jzP17/5A5Z1tfJWNfTg3bfy01eO8+b5QYIgoL25id/6xBaaL1759Mjul9j70jHisYBfv28zN6y+CoCDx07xj48/R60WsvWm1Xxy+00A9A2N8I2HnqI8PsHyJW186cGP0pCIM1Wp8Nff3cWb5wfJNKb48mfvorP13T/FrV6aSV1ecWqKh06epRKG3NfTyYpsJupIV1RPTbm0UDmHUrScQSl6zqEUrXqawXgsRlNT02WPzUlBtdDUyw9eV/Zyociz/UNkE3E+t2IpyXe5zDBK9fRGJC1UzqEULWdQip5zKEWrnmbw3Qqq+flbuTTL1jfn6EwlKVWq/HSgEHUcSZIkSZIWNQsqLUqxIGB7Vzsx4NXhEc6NjUcdSZIkSZKkRcuCSotWWyrJTa3NAOzuHaBSc7erJEmSJElRsKDSorahtZmWZAPDUxX2D7nVT5IkSZKkKFhQaVFLxAK2d7YTAAeHigxMTEYdSZIkSZKkRceCSoveknSK65tz1IBdvQPUQrf6SZIkSZI0lyyoJOC29haaEgkGJiZ5uVAfH98pSZIkSdJCYUElAQ2xGNs62wB4YXCY4cmpiBNJkiRJkrR4WFBJF12VTbM6l6UahuzpGyR0q58kSZIkSXPCgkp6m9s7WknH45wdG+dwcTTqOJIkSZIkLQoWVNLbNMbj3NHRCsC+gQKlSiXiRJIkSZIk1T8LKukXXNOUYUU2zWStxtNu9ZMkSZIkadZZUEm/IAgCtna2kYzFeKM0xuulctSRJEmSJEmqaxZU0mVkEwk2tbcA8EzfEOPVasSJJEmSJEmqXxZU0hWsyzfRnU4xVq3yXP9Q1HEkSZIkSapbFlTSFQRBwPbOduJBwNGREqfLY1FHkiRJkiSpLllQSe+iOdnALW3NAOzpHWSqVos4kSRJkiRJ9ceCSnoPN7bkaU8lGalUeH6gEHUcSZIkSZLqjgWV9B5iQcD2rnZiwKHhEXrHJ6KOJEmSJElSXbGgkqahI5XkhtY8IbCrd4BKLYw6kiRJkiRJdcOCSpqmW1qbaW5IUJic4kBhOOo4kiRJkiTVDQsqaZoSsRjbu9oBODBUZGhiMuJEkiRJkiTVBwsq6X3oTjdyXb6Jahiyu2+QWuhWP0mSJEmSPiwLKul92tTRSjYRp3d8gkPDI1HHkSRJkiRpwbOgkt6nZCzG1s42AJ4fKFCcmoo4kSRJkiRJC5sFlfQBrMhmWNWUoRKG7OkdJHSrnyRJkiRJH5gFlfQB3dHRRioW48zYOMdGSlHHkSRJkiRpwbKgkj6gdCLOHRe3+j3bP0S5Uo04kSRJkiRJC5MFlfQhXNuUYXkmzUStxtP9g1HHkSRJkiRpQbKgkj6EIAjY0tlGQyzgxGiZN0bLUUeSJEmSJGnBsaCSPqRcQ4Lb2loB2Ns/yES1FnEiSZIkSZIWFgsqaQZc39zEksYU5UqVfQNDUceRJEmSJGlBsaCSZkAQBGzraiMeBBwujnKmPB51JEmSJEmSFgwLKmmGtCaT3NzaDMCevgEqNbf6SZIkSZI0HRZU0gy6sTVPW7KB4lSFFwaHo44jSZIkSdKCYEElzaB4ELC9q50AeLlQpH98IupIkiRJkiTNexZU0gzrbExxQ0ueENjVO0A1DKOOJEmSJEnSvGZBJc2CjW3N5BoSDE5OcXCoGHUcSZIkSZLmNQsqaRYkYjG2d7YDsH9omMLkVMSJJEmSJEmavyyopFmyNNPIunwT1TBkd+8AoVv9JEmSJEm6LAsqaRZtam8hHY9zfnyCV4dHo44jSZIkSdK8ZEElzaJUPM7WzjYAfjo4xOhUJeJEkiRJkiTNPxZU0ixb2ZRhZVOGqVrI3r5Bt/pJkiRJkvQLLKikObClo41kLMab5TFeGy1HHUeSJEmSpHnFgkqaA5lEnNs7WgF4tn+QsUo14kSSJEmSJM0fFlTSHFmTy7I03ch4tcaz/UNRx5EkSZIkad6woJLmSBAEbOtqIxEEvDZa4mTJrX6SJEmSJIEFlTSn8g0N3NreAsDevkEma7WIE0mSJEmSFD0LKmmOrW/O0ZlKUqpU+elAIeo4kiRJkiRFzoJKmmOxIGB7Vzsx4NXhEc6NjUcdSZIkSZKkSFlQSRFoSyW5qbUZgN29A1RqYcSJJEmSJEmKjgWVFJENrc20JBsYnqqwf8itfpIkSZKkxcuCSopIIhawvbOdADg4VGRgYjLqSJIkSZIkRcKCSorQknSK65tz1IBdvQPUQrf6SZIkSZIWHwsqKWK3tbeQSyQYmJjk5UIx6jiSJEmSJM05CyopYg2xGFs72wB4YXCY4cmpiBNJkiRJkjS3LKikeeCqbJrVuSzVMGRP3yChW/0kSZIkSYuIBZU0T9ze0Uo6Hufs2DiHi6NRx5EkSZIkac5YUEnzRGM8zh0drQDsGyhQqlQiTiRJkiRJ0txIRB1A0s9d05ThtdE0T5wq8/cH+ykUYUk24PdvDvj4KvtkSZIkSVJ98jdeaR4JgoDyaAt73gwYro4Rxsc4V4I/fybk0eO1qONJkiRJkjQrLKikeeavD8aZnGgBIEgNAVXGq/AX+71xuiRJkiSpPllQSfPM+RJQyRJWGwmCKkFjPxBeWJckSZIkqQ5ZUEnzzJIsQEA40UZYSxDEJwhSA3RlvYJKkiRJklSfLKikeeb3bw5ojANhgnC8gzCMkWgoc9+aAmFoSSVJkiRJqj8WVNI88/FVMf74joDuLARhko5YBx+9KkY6XeJgoRh1PEmSJEmSZlwi6gCSftnHV8X4+CpozucZLhY5Pgo/PtfPvoEC6XicNfmmqCNKkiRJkjRjvIJKWgBWNWXZ3NEKwO7eAU6XxyJOJEmSJEnSzLGgkhaIG1ry3NiSpwb86Fwf/ROTUUeSJEmSJGlGWFBJC8im9haubcoyVQvZeaaX4tRU1JEkSZIkSfrQLKikBSQIAu5c0s7SdCNj1So7z/QyXq1GHUuSJEmSpA/FgkpaYOJBwD09nbSnkgxPVXj8bB+VWi3qWJIkSZIkfWAWVNIClIzF2NHTSS6RoHd8gifP91MLw6hjSZIkSZL0gVhQSQtUJpFgx9IuUrEYJ0tj7O0bJLSkkiRJkiQtQBZU0gLWkmzgvp4u4kHA4eIo+4eGo44kSZIkSdL7ZkElLXBL0inuXtJBALwwOMzh4mjUkSRJkiRJel8sqKQ6cHVThq2dbQDs7R3gZKkccSJJkiRJkqbPgkqqE9c157i5tZka8OS5fvrGJ6KOJEmSJEnStFhQSXVkY1sza3JZKmHIzrO9DE9ORR1JkiRJkqT3ZEEl1ZEgCNjW1c5VmTTj1RqPne2lXKlGHUuSJEmSpHdlQSXVmXgQ8LHuDjpSSUamKjx+tpepWi3qWJIkSZIkXZEFlVSHGmIx7uvpIt+QoH9ikifO9VMNw6hjSZIkSZJ0WYm5OMlgscTffG8XxdIYAQHbb1nLPZvXUxqb4K8e+jEDhVHaW5r4ymd/hWw6RRiGfHvnc7z82imSDQl++4HtrOhpB+DpA8d4ZPdLAHxy+01s2bAagDfO9vO339/NVKXKDddexRd2bCYIgiueQ6p3mUSc+3u6ePj0eU6Vx9jTO8CdXe0EQRB1NEmSJEmS3mFOrqCKBwG/ds8m/pff+yz/4+98iqee/xln+go8uvcg163s4Wtf/TzXrezhsacPAvDya6fpHSzyZ3/wOX7rk1v4+0efBqA0NsE/79rPH3/pAf74Sw/wz7v2Uxq78Ellf/+DZ/iXn9rKn/3B5+gdLPLKa6cBrngOaTHIJxu4r6eTRBBwdKTEC4PDUUeSJEmSJOmXzElB1ZzLXLoCqjHVQHd7M4WRMgeOnGTLjReugNpy42peOnwSgANHTnLHhmsJgoBVy7oYG59keKTMoeOnuf6apWTTKbLpFNdfs5RDx08zPFJmfHKSVcu6CIKAOzZcy0tHfv5alzuHtFh0Nqb4WHcnMWD/0DCvDo9EHUmSJEmSpHeY83tQ9RdGePP8INcs66BYGqM5lwEg35SmWBoDoDBSpkx0a6sAACAASURBVDWfvfSclnyWwkiZoV9cz2UZGilfeHzuneuFkTLAFc8hLSbLs2m2dV3cJts3yInRcsSJJEmSJEn6uTm5B9Vbxien+Mvv/Jgv3LeZdCr5jmNBEMz6vXHe7Ry7XjjMrhePAPA7D2xh6ZKOWc0iTVdzPj8jr7Mpn4dUin29AzxTGKGrpZmeTHpGXluqdzM1h5I+GGdQip5zKEWrXmZwdHT0isfmrKCqVmv85XeeZPMNq7jluqsByGfTDI+Uac5lGB4pk8s0AtCSyzBULF16bqFYoiWXoTWX4cgb536+PlJi7dXdFx4/UnrHestbV01d4Ry/6M6N67hz4zoAhotFhovFmf0GSB9Acz4/o38X1yQT9KYaOFwc5Z+OHOdTVy2hNZl87ydKi9hMz6Gk98cZlKLnHErRqqcZjMeuvJFvTrb4hWHIN/95D93tzdx7+0curW9Yu5ynDx4D4OmDx9iwdsWF9TXLeebAa4RhyPHTvTSmkjTnMqxftYxDx89QGpugNDbBoeNnWL9qGc25DI3JJMdP9xKGIc8ceO3nr3WFc0iLURAEbO1sY0U2zUStxs4zfZQrlahjSZIkSZIWuaBafjOc7ZMce/M8X//mD1jW1cpbG+wevPtWrlnawV899BSDw6O0Nzfxlc/9Ctl0ijAM+YfHnuWV106TbIjz2w9s5+qlF7bc7dl/lEf3HgDgE9s2sPWmNQC8caafv314N5NTVT5y7TJ+4/7bCYKA0fL4Zc/xbuqlmdTCN1tNeaVW4wdneukdn6At2cAnl3WTis/5LemkBaGe/o+VtBA5g1L0nEMpWvU0g/FYjKampssem5OCaqGplx+8Fr7ZfCMar1Z5+NQ5hqcqLE03smNpF/FZvg+ctBDV038QSAuRMyhFzzmUolVPM/huBZWXTEiLVGM8zv1Ll5COxzkzNs6u8wOEoX21JEmSJGnuWVBJi1iuIcGOpV00xAJeGy2xb6AQdSRJkiRJ0iJkQSUtch2pJPd0dxIDDhaKvFyoj0tHJUmSJEkLhwWVJJZl0ty55MIHETzXP8Tx0VLEiSRJkiRJi4kFlSQAVueybGpvIQR+cn6As2PjUUeSJEmSJC0SFlSSLrmxJc/65hzVMOSHZ/sYnJiMOpIkSZIkaRGwoJJ0SRAE3N7RysqmDJO1GjvP9jI6VYk6liRJkiSpzllQSXqHWBBwV1cH3ekUpUqVx872MlGtRh1LkiRJklTHLKgk/ZJELODe7k5akg0UJqf44bk+KrUw6liSJEmSpDplQSXpslLxOPf3dJFNxDk3NsFTvf3UQksqSZIkSdLMs6CSdEVNDQl29HSRjMU4MVrm2f4hQksqSZIkSdIMs6CS9K7aUknu7ekkHgQcGh7hYKEYdSRJkiRJUp2xoJL0nnrSjXx0STsBsG+gwNHiaNSRJEmSJEl1xIJK0rSsasqyuaMVgN29A5wuj0WcSJIkSZJULyyoJE3bDS15bmzJUwN+dK6P/onJqCNJkiRJkuqABZWk92VTewvXNmWZqoXsPNNLcWoq6kiSJEmSpAXOgkrS+xIEAXcuaWdpupGxapWdZ3oZr1ajjiVJkiRJWsAsqCS9b/Eg4J6eTtpTSYanKjx+to9KrRZ1LEmSJEnSAmVBJekDScZi7OjpJJdI0Ds+wZPn+6mFYdSxJEmSJEkLkAWVpA8sk0iwY2kXqViMk6Ux9vYNElpSSZIkSZLeJwsqSR9KS7KBe3s6iQcBh4uj7B8ajjqSJEmSJGmBsaCS9KF1pxu5e0kHAfDC4DCHi6NRR5IkSZIkLSAWVJJmxNVNGbZ2tgGwt3eAk6VyxIkkSZIkSQuFBZWkGXNdc46bW5upAU+e66dvfCLqSJIkSZKkBcCCStKM2tjWzJpclkoYsvNsL8XJqagjSZIkSZLmOQsqSTMqCAK2dbVzVSbNeLXGo2d7KVeqUceSJEmSJM1jFlSSZlw8CPhYdwcdqSQjUxUeP9vLVK0WdSxJkiRJ0jxlQSVpVjTEYtzX00WuIUH/xCRPnOunGoZRx5IkSZIkzUMfqKAKw5Da2/6RpMvJJOLc39NFYzzGqfIYe3oHCH3PkCRJkiT9gsR0H1gYKfMPjz3D0ZPnKY9PvuPYf/53vz3jwSTVh+ZkAzt6unjk9HmOjpTIJhLc2t4SdSxJkiRJ0jwy7Suo/u6RvcRjMf6739xBKpngf/pvP82GNcv5zU9smc18kupAZ2OKu7s7iAH7h4Z5dXgk6kiSJEmSpHlk2gXV8dN9fPGBbSzvbicArlrSxhcf2MYPn315FuNJqhcrshm2dbUD8HTfIG+MliNOJEmSJEmaL6ZdUMWCgFjswsPTjUlGSuMkGxIURvwlU9L0rM03sbGtmRB48nw/58bGo44kSZIkSZoHpl1QrVzawcvHTgHwkVXL+MZDP+b//q9PcnVPx6yFk1R/bm5tZl2+iWoY8sOzfQxNTr73kyRJkiRJdW3aN0n/0oN38taHb/36fZt5/JlXmJia4p5N62crm6Q6FAQBWzvbGKtWOVkaY+eZPj591RIyiWm/HUmSJEmS6sy0fyPMNKYu/TnZkOBTd940K4Ek1b9YEHD3kg5+cKaX3vEJHjvTyyeXdZOKT/uiTkmSJElSHXnXguqR3S/xye0XiqjvPfXiFR/3q3fdMrOpJNW9RCzGfT2dPHzqHIOTUzxxro8dS7uIB0HU0SRJkiRJc+xdC6q33wB9qFia9TCSFpfGeJwdS7t4+NR5zoyNs+v8AHctaSewpJIkSZKkReVdC6rf/MSWS3/+7U9vn/UwkhaffEMDO5Z28cjpc7w2WiKTiLO5ozXqWJIkSZKkOTTtG7789//n3192/X/4j/8wY2EkLU4dqST3dHcSAw4WirxcKEYdSZIkSZI0h6ZdUFVrtV9eq9aovfXRfpL0ISzLpLlzSQcAz/UPcXzUbcWSJEmStFi856f4ff2bjwABU5UqX//mD95xrDBSYtWyztnKJmmRWZ3LUq5U2DdQ4CfnB0jH4/SkG6OOJUmSJEmaZe9ZUG27eS1hGPLG2X623bzm0noA5LJprlvZM5v5JC0yN7bkKVWqHBoe4Ydn+/jUsiW0pZJRx5IkSZIkzaL3LKi2bFhNrVbj5WOnuG39NTQk4nORS9IiFQQBt3e0Uq5WOTFaZufZXh5Y1k1Tw3u+XUmSJEmSFqhp3YMqFotx+I1zxOPTvmWVJH1gsSDgrq4OutMpSpUqj53tZaJajTqWJEmSJGmWTLtxuuPGa/nJ84dnM4skXZKIBdzb3UlLsoHC5BQ/PNdHpeaHMkiSJElSPZr2npkTZ/p58qevsvOZg7Tls1y4C9UF//aLn5iNbJIWuVQ8zv09XTx8+hznxiZ4qrefu5d0EAuC936yJEmSJGnBmHZBtf2WtWy/Ze1sZpGkX9LUkGBHTxf/fPo8J0bLPBsf4o6OVgJLKkmSJEmqG9MuqLZsWD2bOSTpitpSSe7t6eSxM70cGh4hm4izobU56liSJEmSpBky7XtQhWHIrheP8B//v0f52l99F4CjJ8/x00Ovz1o4SXpLT7qRjy5pB2DfQIFjI6WIE0mSJEmSZsq0C6rvP/Uie/cfYfstaxkcHgWgJZdl59Mvz1o4SXq7VU1Zbu9oBWDX+X5Ol8ciTiRJkiRJmgnTLqiePnCMf/Xf3Mumj6y6dO+XjpYm+gsjsxZOkn7RDS15bmzJUwN+dK6P/onJqCNJkiRJkj6kaRdUtTAklXznLasmJiukGqZ9GytJmhGb2lu4tinLVC1k55leilNTUUeSJEmSJH0I0y6obrj2Kv7x8X1MVarAhXtSfe+pF7hxzfJZCydJlxMEAdu72lmabmSsWmXnmV7Gq9WoY0mSJEmSPqBpF1S/dt8mhkfH+Ddf/zvGJqb41//H3zEwXOJzH7ttNvNJ0mUlYgH39HTSnkoyPFXh8bN9VGq1qGNJkiRJkj6Aae/PS6eS/MGvf4xiaYzB4RKt+QzNTZnZzCZJ7yoZi7Gjp5PvnzpP7/gET57v557uTmIX75MnSZIkSVoYpn0F1dtl0ykmp6r0DY3QN+RN0iVFJ5NIsGNpJ6lYjJOlMfb2DRKGYdSxJEmSJEnvw7SvoHrltVN88+G9FEfL7zwQBPznf/fbM51LkqatNZnk3p5OHj3Ty+HiKNlEnFvaWqKOJUmSJEmapmkXVN969Fk+tX0Dd2xYTdJP7pM0z3SnG7l7SQc/OtfHC4PDZBMJ1uaboo4lSZIkSZqGaW/xK49PcOfGdZZTkuatq5sybOlsA2BP7wAnS+X3eIYkSZIkaT6YdkG17eY17H3p2GxmkaQP7frmHDe3NlMDnjzXT9/4RNSRJEmSJEnvYdqXQ71+uo8nnnuVx54+SD6bfsexf/vFT8x4MEn6oDa2NVOqVDg6UmLn2V4+vaybfLIh6liSJEmSpCuYdkG17ea1bLt57WxmkaQZEQQB27raGavWOFUe49GzvTywrJtMIh51NEmSJEnSZbxnQfWzE2cBaM1nZz2MJM2UeBDwse4OHjl9nv6JSR4/28snly2hITbtnc2SJEmSpDnyngXV//vwnnc9HgTwv/6rX5uxQJI0UxpiMe7r6eLh0+fon5jkiXP93NvTSTwIoo4mSZIkSXqb9yyo/rc/tHyStHBlEnHuv1hSnSqPsad3gDu72gksqSRJkiRp3nCvi6S615xsYEdPF4kg4OhIiRcGh6OOJEmSJEl6GwsqSYtCZ2OKu7s7iAH7h4Z5dXgk6kiSJEmSpIssqCQtGiuyGbZ2tQPwdN8gb4yWI04kSZIkSQILKkmLzLp8ExvbmgmBJ8/3c25sPOpIkiRJkrToWVBJWnRubm1mXb6Jahjyw7N9DE1ORh1JkiRJkhY1CypJi04QBGztbGNFNs1ErcbOM32UK5WoY0mSJEnSomVBJWlRigUBdy/poKsxxWilwmNnepmo1qKOJUmSJEmLkgWVpEUrEYtxX08nzQ0JBieneOJcH9UwjDqWJEmSJC06FlSSFrXGeJwdS7tIx+OcGRtn1/kBQksqSZIkSZpTFlSSFr18QwM7lnbREAt4bbTEvoFC1JEkSZIkaVGxoJIkoCOV5J7uTmLAwUKRlwvFqCNJkiRJ0qJhQSVJFy3LpNne1Q7Ac/1DHB8tRZxIkiRJkhYHCypJeps1+SZua28hBH5yfoCzY+NRR5IkSZKkumdBJUm/YENLnvXNOaphyA/P9jE4MRl1JEmSJEmqaxZUkvQLgiDg9o5WVjZlmKzV2Hm2l9GpStSxJEmSJKluWVBJ0mXEgoC7ujroTqcoVao8draXiWo16liSJEmSVJcSc3GSb35/NwePnSKXbeRPf/czAHz/Jy+y+8Wj5DIpAB68+1ZuXH0VAI/uOcCel44SCwK+sON2PnLtMgBeee0U3975HLUwZNvNa/j41g0A9BdG+MZDT1Eam2BFdztfevBOEvE4U5Uqf/O9XZw8N0A2neLLn72LjpbcXHzJkupAIhZwb3cnD58+T2Fyih+e6+P+niUkYkHU0SRJkiSprszJFVRbblrNH/3Gfb+0fs/t6/mTrzzIn3zlwUvl1Jm+AvsOvc6f/u5n+KN/cR/fevQZarUatVqNbz36LH/4G/fx73/vM+x75XXO9BUA+Kcnnueezev52lc/T6YxyZ79RwHYs/8omcYkX/vq57ln83oeeuL5ufhyJdWRVDzO/T1dZBNxzo1N8FRvP7UwjDqWJEmSJNWVOSmo1qzoJpNOTuuxB46cZNP6a2hIxOloydHVluPEmX5OnOmnqy1HZ2uORDzOpvXXcODIScIw5PCJs2y8fiUAWzas5qUjJy+81tGTbNmwGoCN16/kZyfOEvqLpaT3qakhwY6eLpKxGCdGyzzbP+R7iSRJkiTNoEjvQfXjn77K1/7qu3zz+7spjU0AMDRSpjWfvfSYllyWoZHyhfXc29bzF9ZLYxNkGpPEY7FL64WRMgCFt71WPBYjnUpeOo8kvR9tqST39nQSDwIODY9wsFCMOpIkSZIk1Y05uQfV5dy18To+tf0mCAK+9+MX+c4P9/HFT2+PKg67XjjMrhePAPA7D2xh6ZKOyLJIb9ecz0cdQRc15/MkGtPsPHWWg6NjdDU3s67Fn89i4BxK0XIGpeg5h1K06mUGR0dHr3gssoIq35S+9Oftt6zhP337RwC05jIMFUuXjhVGSrTmMgAMjbxtvXhhPZtOUR6fpFqrEY/FKBRLtFx8fMvF12rNZ6nWaoxNTJJNpy6b586N67hz4zoAhotFhoteHaHoNefz/l2cZzoDuCmX4dn+IX5w/A2qS7tYlkm/9xO1YDmHUrScQSl6zqEUrXqawbd2v11OZFv8hi9uwwPYf/gkSztbANiwdjn7Dr3OVKVKf2GE3sEiK5d2cPXSDnoHi/QXRqhUq+w79Dob1i4nCALWXd3NC6+eAODpA8fYsGbFhddas5ynDxwD4IVXT7BuZQ9B4KdvSfpwbmjJc2NLnhrwo3N99E9MRh1JkiRJkha0oFp+c9bv9PuNh57iyBvnGB0bJ59N8+mP3syRN87x5vlBgiCgvbmJ3/rEFpovXvn0yO6X2PvSMeKxgF+/bzM3XPyEv4PHTvGPjz9HrRay9abVfHL7TQD0DY3wjYeeojw+wfIlbXzpwY/SkIgzVanw19/dxZvnB8k0pvjyZ++iszX3nnnrpZnUwldPTXm9CcOQp84P8NpoiXQ8zgNXLSHf0BB1LM0C51CKljMoRc85lKJVTzMYj8Voamq67LE5KagWmnr5wWvhq6c3onpUqYU8fraXM2PjNDckeOCqbhrj8f+fvTuPkus87/z+e++9tW+9r0CTbBAACZANkKK4iZQoiZIhkZRE0JacxLHkceShrThzjieT43ES58QzieSccRxlPD6KpaNI9oS2ZZOUSNGiKFkiRYqrRIJYuGAj2Y3et1q69qp780c1Gg2gwQXoxu3l+zmnTnfdqq56CsBFV//6eZ/X77KwzDgPAX9xDgL+4zwE/LWezsG3C6h83cUPANYyxzL6SFe7WoIBZao1/Wh0UjXX9bssAAAAAFhzCKgA4AKEbEu/0tOhuONoolTWT8en5Ho0pgIAAADAe0FABQAXKOo4+nhPu0KWpcF8UU9PzsgjpAIAAACAd42ACgCWQXMwqNu722Ubo9ezc9o3m/G7JAAAAABYMwioAGCZdEXC+nBnm4yk7xxL687vZnTT39T1mQfqevQ4s6kAAAAA4FwIqABgGV0Sj0qVJj11wtN0fVaeXdRYXvrKsx4hFQAAAACcAwEVACyzh16Lq1ZOyhhPJjQlBbIq1T19bR9zqQAAAABgKY7fBQDAejOelzylJOPJBHIywbQ8J6/xYoukqN/lAQAAAMCqQwcVACyzzpgkGXmVZrnFDnluQMaqKp4c188nplWus9QPAAAAABYjoAKAZXbvbqOwPX/FDcsrdsqpJfX+LqPXsnO6f3BEx+fy8jyW/AEAAACAxBI/AFh2e/otSa6+ts/TeF7qjFm6d3ezbuhN6OeTMxovlfXTsSkdjUZ0U3uLEgH+KwYAAACwsfFTEQCsgD39lvb0n3k0qDt6O/V6dk4vTKc1VChqdHBE17Y0aWdTQpYxfpQKAAAAAL5jiR8AXETGGF2RSuievm5dFo+q5nl6fnpWD50Y01Sp7Hd5AAAAAOALAioA8EHUcfSRrnZ9vLtDCcfRdLmih06M6dnJGVVchqgDAAAA2FgIqADAR5tjEd3d162rm5KSpEOZnB4cHNVgvuBzZQAAAABw8RBQAYDPApal69ua9anN3WoLBTVXq+lHo5P659FJ5Ws1v8sDAAAAgBVHQAUAq0RbKKi7NnXphrZmBSyjN/MFPTA4qlfSObme53d5AAAAALBiCKgAYBWxjNFVTUnt3dyjvlhEFdfVM1MzemR4XDPlit/lAQAAAMCKIKACgFUoHnB0e1e7PtrVrqhja6JU1veGRvXC9KxqDFEHAAAAsM4QUAHAKmWM0aXxqPZu7tGOVEKepP2zWT0wNKrhQtHv8gAAAABg2RBQAcAqF7It3dTeojs3daklGFCuWtOjIxN6fGxKxVrd7/IAAAAA4IIRUAHAGtERDulTm7t1XWuTHGN0bC6v+wdH9Hp2Th5D1AEAAACsYQRUALCG2MZoV3NKd/d1qzcaVtl19dTEtP5pZFzpStXv8gAAAADgvBBQAcAalAwE9CvdHbqts00R29ZYsazvDo3qxZm0ai7dVAAAAADWFgIqAFijjDHakojpnr5ubU/GVfc8vTST0feGRjVaLPldHgAAAAC8awRUALDGhWxbt3S06o7eTjUFAkpXq/qn4XE9OTGtcp0h6gAAAABWPwIqAFgnuiJhfXpzt65pSck2Roezc7p/cFRHc3mGqAMAAABY1QioAGAdcSyja1ua9JnN3eqOhFWs1/XE+JR+ODqhLEPUAQAAAKxSBFQAsA41BQP6RE+Hbu1oVciyNFwo6YGhUb08m1GdbioAAAAAqwwBFQCsU8YYbUvGdU9fjy5PxFT3PP1iOq2HhkY1USr7XR4AAAAALCCgAoB1LuLY+lBnm/b0dCgRcDRTqer7J8b09OSMynXX7/IAAAAAgIAKADaK3mhEezd3a6A5KSPp1UxODwyN6I05hqgDAAAA8BcBFQBsII5l6f2tzfr05m51hEMq1Or6ydiUfjw2qblqze/yAAAAAGxQBFQAsAG1hIK6s7dTN7e3KGhZGswX9cDQiA6ms3LppgIAAABwkRFQAcAGZYzRlamE7unr1qXxqKqup+emZvXwiTFNlSt+lwcAAABgAyGgAoANLuo4+mhXuz7W3a6442iqXNFDQ6N6bmpWVZch6gAAAABWHgEVAECS1BeLam9ft3amEpKkg+msHhgc1WC+4HNlAAAAANY7AioAwIKAZenG9hZ9alOXWkNBzdVq+tHopP55bFKFGkPUAQAAAKwMAioAwFnawiF9alOXbmhrVsAyenOuoPsHR/VqJiePIeoAAAAAlhkBFQBgSZYxuqopqbs392hzNKKK6+rpyRk9MjyuWYaoAwAAAFhGBFQAgLeVCDj6WHe7PtLVpqhja7xU1neHRvWL6VnVGKIOAAAAYBkQUAEA3pExRpfFY9q7uUdXphLyJL08m9WDQ6MaLhT9Lg8AAADAGkdABQB410K2pZvbW3RHb5eagwFlqzU9OjKhJ8anVKzV/S4PAAAAwBpFQAUAeM86IyF9enO3rmttkm2Mjubyun9wRIezcwxRBwAAAPCeEVABAM6LbYx2Nae0d3O3eiJhlV1XT05M6wcjE8pUqn6XBwAAAGANIaACAFyQZDCgPT0d+lBnm8K2pdFiSQ8OjeqlmbRqLt1UAAAAAN4ZARUA4IIZY3R5IqZ7+nq0LRlX3fP04kxG3xsa1Vix5Hd5AAAAAFY5AioAwLIJ27Zu7WjVJ3s7lQo4SleremR4XE9NTKtcZ4g6AAAAgKURUAEAll13JKzPbO7RNc0p2cbo9eyc7h8c1bFcniHqAAAAAM5CQAUAWBGOZXRta5M+vblLXZGQivW6Hh+f0mOjk8pWGaIOAAAA4BQCKgDAimoOBvXJnk7d0tGqoGXpRKGoBwdHtX82ozrdVAAAAABEQAUAuAiMMdqejOuevh5ticdU8zy9MJ3WQ0OjmiyV/S4PAAAAgM8IqAAAF03UsXVbV5t+pbtDiYCjmUpVD58Y0zOTM6q4rt/lAQAAAPAJARUA4KLbFIto7+ZuDTQlZSS9ksnpgcERvTlX8Ls0AAAAAD4goAIA+MKxLL2/rVmf2tyt9lBQ+Vpd/zw2qR+PTmiuWvO7PAAAAAAXEQEVAMBXraGg7tzUpZvaWhS0LL2VL+qBoREdSmflMkQdAAAA2BAIqAAAvrOM0Y6mhPb2devSWFRV19OzU7P6/okxTZcrfpcHAAAAYIURUAEAVo2Y4+ij3e26vatdMcfWZLmih4ZG9fzUrKoMUQcAAADWLQIqAMCqc0k8qr19PdqRSsiTdCCd1YODoxrKF/0uDQAAAMAKIKACAKxKQcvSTe0tumtTl1pDQeVqNT02OqGfjk2qUKv7XR4AAACAZURABQBY1drDIX1qU5eub22WY4yOzxV0/+CIXsvk5DFEHQAAAFgXCKgAAKueZYyubk5qb1+PNkcjqriufj45o0eGxzVbYYg6AAAAsNYRUAEA1oxEwNHHutv14c42RWxb46Wyvjc0pl9Op1Vz6aYCAAAA1ioCKgDAmmKMUX8ipnv6unVFMq6652nfbEbfHRrRSKHkd3kAAAAAzgMBFQBgTQrZtj7Q0ao7ejvVFAwoU63pByPjemJ8SqU6Q9QBAACAtYSACgCwpnVFwvrM5m69r6VJtjE6msvr/sERHcnOMUQdAAAAWCMIqAAAa55tjHa3pHT35m71RMIq1V39bGJaj45MKFOp+l0eAAAAgHdAQAUAWDdSwYD29HTogx2tCtuWRoolPTg0qn0zGdXppgIAAABWLQIqAMC6YozR1mRc9/T1aGsiprrn6ZczaX13aFRjRYaoAwAAAKsRARUAYF0K27Y+2NmmT/R0KhlwlK5U9cjwuJ6amFa57vpdHgAAAIBFCKgAAOtaTzSsuzf3aHdzSrYxej07p/sHR3Q8l2eIOgAAALBKEFABANY9xzJ6X2uTPr2pS53hkIr1un46PqUfjU4qV635XR4AAACw4RFQAQA2jOZQUHf0duoD7S0KWpaGCkU9MDiiA7NZuXRTAQAAAL4hoAIAbCjGGF2RSuievh71x6OqeZ6en57VQyfGNFkq+10eAAAAsCERUAEANqSoY+vDXe36eHeHEo6j6XJFD58Y0zOTM6q4DFEHAAAALibH7wIAAPDT5lhEXZFuvTST0aF0Vq9kcnorX1Ct1KR/fCWi8fysOmPSvbuN9vTzex0AAABgJRBQAQA2vIBl6fq2Zm1JxPTUxLSeHSvrqROTqtWi8kyTxvKOvvKsJ8klpAIAAABWAO+ytDFo1wAAIABJREFUAQCY1xoK6q5NXdo/3KSaa8k4BZnIqExwRiW3qq/tY5A6AAAAsBIIqAAAWMQyRjO5hLxil7xaVJJkAnMykTFN1KY0xSB1AAAAYNmxxA8AgDN0xqSxvCOv3CaZqhTISU5esUhB3ztRUk8krIHmpHoiYRlj/C4XAAAAWPPooAIA4Az37jYK2/NXvIC8SouC5R7dfWlKActopFjSoyMTeujEmN6Yy8v1WPoHAAAAXAg6qAAAOENjELqrr+3zNJ7X/C5+jvb0t6hcT+m17JwOpXOaKlf0k7EppQKOrmpK6vJEXI5FRxUAAADwXpl6YWjFf+371w8/pQNHTygRC+uPf+czkqR8sayvP/i4ptNzam2K64t336ZYJCTP8/Sdx57XwWMnFAw4+vydt6ivu1WS9Mz+o/qnp16WJH3yll26aeBySdJbo1P69sNPqVqr66otm/TZj18vY8w5n+OdZLLZFfqTAN6bVDLJv0fAZ+c6D2uuqyO5vA6ks8pVa5KkqGNrZyqhK1IJBS2alIHlwPdCwH+ch4C/1tM5aFuW4vH4krddlHfPN+26XL//6x877dijTx/QFZd269/93j264tJu/fCZA5Kkg8eGNTGT1Z/87l79V5+8Sfc9+oykRqD1yJP79Ie/daf+8Lfu1CNP7lO+2BhUe98PntVv3HGz/uR392piJqtDx4bf9jkAALhQjmXpylRCv9rXo9s629QSDKhQq+uF6bT+/s1h/WJ6VoVa3e8yAQAAgDXhogRUW/u6FI0ETzu2//Cgbrq60QF109WX6+XXBxeO3ziwRcYY9fd2qFiqKJMr6JXjw7rysh7FIiHFIiFdeVmPXjk+rEyuoFKlov7eDhljdOPAFr18ePBtnwMAgOViGaMtiZg+s7lbH+/uUFckpIrr6uXZrL7z1rCenpxRtlr1u0wAAABgVfNtBlU2X1Qq0di+OxmPKJsvSpLSuYKak7GF+zUlY0rnCpo983giptlcoXH/xOnH07nC2z4HAADLzRijzbGINsciGi+WtT+d0WC+qFczOb2Wyak/HtPVzUm1hoLv/GAAAADABrMqhqQbY1Z8m+53eo4nX3xdT750WJL0hTtvUk9n24rWA7xbqWTS7xKADe+9noeppLSts10z5bJemprV4UxOw9Wahidm1BeP6dq2ZnVHIyv+vQ9YL/heCPiP8xDw13o5B+fm5s55m28BVTIWUSZXUCoRVSZXUCIaliQ1JaKazeYX7pfO5tWUiKo5EdXht8ZOHc/lte2Srsb9c/nTjjed7Jo6x3Ms5dZrt+vWa7dLagxJXy8DyLC2radheMBadSHnoS3pukRUV4SDOpjO6vXsnA5Pl3V4ekYd4ZAGmpLqixFUAW+H74WA/zgPAX+tp3PQfpuNhHzbYmhg22Y9c+CoJOmZA0c1sK2vcXzrZj27/5g8z9Px4QmFQ0GlElHt6O/VK8dHlC+WlS+W9crxEe3o71UqEVU4GNTx4Ql5nqdn9x879VjneA4AAC6meMDRje0t+tylvbqmJaWQZWmiVNaPxyb1wNCojmTnVPdWfFNdAAAAYNUy9cLQir8j/saDT+jwW2OaK5aUjEV01wd3a9e2Pn39wSc0k5lTayquL+69TbFISJ7n6e9++JwOHRtWMGDr83feokt6Gsvtfr7viB59er8k6RMfGNDNu7ZKkt4amdK3v/+UKtW6dm7p1a//yg0yxmiuUFryOd7Jekkmsfatp6QcWKtW4jysuq5ez87pUDqnuVpNkhR3HO1sSmh7Mq7A2/xmCdho+F4I+I/zEPDXejoHbctSPB5f8raLElCtNevlLx5r33r6jwhYq1byPKx7no7n8tqfzipdaez0F7Is7WhKaEcqobBtr8jzAmsJ3wsB/3EeAv5aT+fg2wVUq2JIOgAAG5FtjLYm47o8EdNQoaiXZ7OaKJX10kxGB2az2p6M66qmpOIBvl0DAABgfeMdLwAAPjPGqC8W1eZoROOlsvbPZjVUKOpQJqdXMzn1J2IaaEqqORT0u1QAAABgRRBQAQCwShhj1BUJqysS1nS5ogOzWR2fy+tornHpi0U00JRS57uYpwgAAACsJQRUAACsQq2hoG7ratO11ZQOpnM6nJ3TYL6owXxRXZGQBppS2hQNyxjjd6kAAADABSOgAgBgFUsGArq5vUW7m1N6JZPVq5k5jRXLGitOqCUY0EBzSpfFo7IIqgAAALCGEVABALAGRB1b17U2a6A5pdcyOR3K5DRTqerx8Sn9csbR1U1JbU3E5FiW36UCAAAA7xkBFQAAa0jQsjTQnNKOVFLH5vI6MJtRplrT05Mzemkmo51NCV2RTChkE1QBAABg7SCgAgBgDXIso+3JuLYmYnorX9D+2aymyhX9Yjqt/bNZbU/GdVVTQlGHb/UAAABY/XjXCgDAGmYZo8viMV0ai2qkWNL+2axGiiUdSGf1SianyxMxXd2UVCoY8LtUAAAA4JwIqAAAWAeMMeqNRtQbjWiqVNbL6azemivo9eycDmfndEk8ql1NSbWFQ36XCgAAAJyFgAoAgHWmLRzSR7valalUdSCd1dFcXm/OFfTmXEE9kbAGmpPqiYRl2PkPAAAAqwQBFQAA61QqGNAtHa26tiWlQ+mcXs3mNFIsaaRYUlsoqIHmpC6JRWURVAEAAMBnBFQAAKxzUcfR+9uaNdCc1GvZOR1K5zRVrugnY1NKBRxd3ZzSlnhMjkVQBQAAAH8QUAEAsEGEbFu7mlPamUroSC6vA7NZZao1PTUxrRdn0tqZSuiKVEJBy/K7VAAAAGwwBFQAAGwwjmXpylRC25NxvTFX0P7ZjGYqVb0wndbLs1ldmYprRyqpqGP7XSoAAAA2CAIqAAA2KMsYbUnE1B+P6kShpP3pjMaKZb08m9XBdE7bknFd1ZRQMhDwu1QAAACscwRUAABscMYYbY5FtDkW0XixrP3pjAbzRb2ayem1TE798Ziubk6qNRT0u1QAAACsUwRUAABgQWckpI9FOjRbqejAbFbHcnkdm2tcNkcjGmhOqjMckmHnPwAAACwjAioAAHCW5mBQH+xs07UtTTqYzur17JyGCkUNFYrqDIc00JzU5miEoAoAAADLgoAKAACcUzzg6Mb2Fu1uSemVTE6vpHMaL5X1o9FJNQUDGmhKqj8Rk01QBQAAgAtAQAUAAN5R2LZ1bUuTrm5K6vXsnA6ms0pXqvrZxLRenMloZ1NjV8CAZfldKgAAANYgAioAAPCuBSxLVzUldWUqoeO5vPbPB1XPTc1q30xGO5oS2pFKKGzbfpcKAACANYSACgAAvGe2MdqajOvyREyD+aL2p7OaKJX10kxGB2az2p6M66qmpOIB3moAAADgnfGuEQAAnDdjjC6JR9UXi2i8VNb+2ayGCkUdyuT0aianLYmYrm5OqjkY9LtUAAAArGIEVAAA4IIZY9QVCasrEtZ0uaIDs1kdn8vrSK5x6YtFNNCUUmck5HepAAAAWIUIqAAAwLJqDQV1W1ebrq2mdDCd0+HsnAbzRQ3mi+qKhDTQlNKmaFiGnf8AAAAwj4AKAACsiGQgoJvbW7S7OaVXMlm9mpnTWLGsseKEWoIBDTSndFk8KougCgAAYMMjoAIAACsq6ti6rrVZA80pvZbJ6VAmp5lKVY+PT+mXM46ubkpqayImx7L8LhUAAAA+IaACAAAXRdCyNNCc0o5UUsfm8jowm1GmWtPTkzN6aSajnU0JXZFMKGQTVAEAAGw0BFQAAOCiciyj7cm4tiZieitf0P7ZrKbKFf1iOq39s1ldkYxrZ1NCUYe3KQAAABsF7/wAAIAvLGN0WTymS2NRjRRL2j+bbXxMZ3Uok9PliZiubkoqFQz4XSoAAABWGAEVAADwlTFGvdGIeqMRTZXKejmd1VtzBb2endPh7JwuiUe1qymptnDI71IBAACwQgioAADAqtEWDumjXe3KVKo6kM7qaC6vN+cKenOuoJ5IWAPNSfVEwjLs/AcAALCuEFABAIBVJxUM6JaOVl3TktKhdE6vZXMaKZY0UiypLRTUQHNSl8SisgiqAAAA1gUCKgAAsGrFHEfXtzVrV3NSr2XndCid01S5op+MTSkVcHR1c0pb4jE5FkEVAADAWkZABQAAVr2QbWtXc0o7UwkdyeV1YDarTLWmpyam9eJMWlelktqeiitoWX6XCgAAgPNAQAUAANYMx7J0ZSqh7cm43pgraP9sRjOVqp6fntW+2YyuTMW1M5VUxLH16HFXX9vnaTwvdcake3cb7eknwAIAAFiNCKgAAMCaYxmjLYmY+uNRnSiUtD+d0VixrJdnszqYzilXiOofDyZUrjXe6ozlpa8860lyCakAAABWId6hAQCANcsYo82xiO7o7dKdvV3qi0VU9zw99GZOleCoTGhasouSXJXq0tf2eX6XDAAAgCXQQQUAANaFzkhIH4t0aLZc0Td+mZacgoyTl3Hy8jwjuSGNV8KaLsfUEgzIsAMgAADAqkFABQAA1pXmUFCdgVaNFVKSk5fskmRVZOySYuGSvjuUVcS21RsNqycSVm80oqhj+102AADAhkZABQAA1p17dxt95VlHpWpKqqYk1RUMlnTn5WXFnLLytbqO5vI6mstLklpDQfVGwuqJhtUZDsux6K4CAAC4mAioAADAutMYhL54Fz9b9+6Oa09/Up7nKV2tarhQ0nChpLFiSdPliqbLFe1PZ+UYo+75sKo3GlZTgOWAAAAAK42ACgAArEt7+i3t6T/7uDFGzcGgmoNBXdWUVM31NF4qaaRQ0vB8WDVUKGqoUJQkxRxbvdHIwpLAsM1yQAAAgOVGQAUAADY0xzLzAVRE75dUqNU0XChppNjosMrX6jqcndPh7JyMpLZQcCGwag+HZNNdBQAAcMEIqAAAABaJOo62JuPamozL8zxNV6oaLhQ1UihpvFTWZLmiyXJF+2YzClqWuiOhRmAVCSsZDPhdPgAAwJpEQAUAAHAOxhi1hYJqCwW1qzmlqutqrFjWifnAKl2t6q18UW/lG8sBkwFnIazqjoYVtCyfXwEAAMDaQEAFAADwLgUsS5tjEW2ORSRJc9WahoulhQ6rbLWmbCanVzM5WZLawyFtml8O2BoKymI5IAAAwJIIqAAAAM5TPOBoeyCu7cm4XM/TVLmi4UJRw4WSJktljc9ffjkjhSxrfmfARodVPMDbMAAAgJN4ZwQAALAMLGPUEQ6pIxzSNS1Sue5qdL67arhYUq5a0xtzBb0xV5AkNQUD6o00AquuSEgBlgMCAIANjIAKAABgBYRsS5fGo7o0HpXnecpVazpRKGm4WNRosaR0pap0papDmZxsY9QZDql3vsOqJRiQYTkgAADYQAioAAAAVpgxRslgQDuCAe1oSqjueZoslTVcKOlEoajpckUjxZJGiiW9MJ1WxLYXwqqeSFhRx/b7JQAAAKwoAioAAICLzDZGXZGwuiJhva+1SaV6XSOF0sLA9XytrqO5vI7m8pKk1lBwYTlgRzgkx6K7CgAArC8EVAAAAD4L27b6EzH1J2LyPE/pSnU+rCpptFjSdLmi6XJF+9NZOcaoOxKeH7geVlOA5YAAAGDtI6ACAABYRYwxag4F1RwK6qqmpGqup/FSI6waLhQ1U6lqqFDUUKEoSYo7jnqj84FVJKyQzXJAAACw9hBQAQAArGKOZdQbjag3GpHUrEKtpuFCY17VcKGkuVpNr2fn9Hp2TkZSWyioTdGIeqJhtYdDsumuAgAAawABFQAAwBoSdRxtTca1NRmX53marlQ1XChqpFDSeKmsyXJFk+WKXprNKGhZ6o6E5gOusJKBgN/lAwAALImACgAAYI0yxqgtFFRbKKhdzSlVXVej851VI4WS0tWq3soX9Va+sRwwGXAaYVUkrO5oWEHL8vkVAAAANBBQAQAArBMBy1JfLKq+WFSSlKvWNFwoarjYCKyy1ZqymZxezeRkSeqIhNQbaXRXtYaCslgOCAAAfEJABQAAsE4lAo6uSCV0RSoh1/M0Va40AqtCSZOlssaKjcsvZ6SQZak3Gl5YDhhzeJsIAAAuHt55AAAAbACWMeoIh9QRDumaFqlcP7kcsBFY5Wo1HZ8r6PhcQZLUFAyoNxLWpmhEXZGQHJYDAgCAFURABQAAsAGFbEuXxqO6NB6V53nKVhu7Aw4XixotlpSuVJWuVHUok5NtjDrDoYUOq5ZgQIblgAAAYBkRUAEAAGxwxhilggGlggHtaEqo7nmaLJV1Yr67arpc0UixpJFiSS9MpxWx7VPLASNhRRzb75cAAADWOAIqAAAAnMY2Rl2RsLoiYV3XKhVrdY0WS43AqlhSoVbX0VxeR3N5SVJrKNgIrCIRdYRDciy6qwAAwHtDQAUAAIC3FXFs9Sdi6k/E5Hme0pWqhoslDRdKGi02OqymyxXtn83KMUbdkfBCh1Uq4LAcEAAAvCMCKgAAALxrxhg1h4JqDgV1VVNSNdfVeKncmF9VKGqmUtVQoaihQlHSrOKOo95oWD3RsHojYYVslgMCAICzEVABAADgvDmW1ZhFFY1IalahNj9sfX7g+lytptezc3o9OycjqT0cUm+kEVh1hEOy6K4CAAAioAIAAMAyijqOtibj2pqMy/M8TVeqGp4ftj5RKi9cXprNKGhZ6plfDtgTDSsZCPhdPgAA8AkBFQAAAFaEMUZtoaDaQkHtak6p6roanZ9dNVwoKlOt6c18QW/mC5KkZMBRbzSiTdGw9o8G9Y39RuP5WXXGpHt3G+3pt3x+RQAAYKUQUAEAAOCiCFiW+mJR9cWikqRctdboriqWNFIoKVutKZvJ6eE3s/r5CalaD0qBsMbKjr78vK1iPaBPXR6QzbJAAADWHQIqAAAA+CIRcHRFKqErUgm5nqfJUkXDxaL+4ZWCqm5Fxi5LdllGUlXSX74mzRpLEcdW3HEUW/Qx5jiKz38M2xY7BwIAsMYQUAEAAMB3ljHqjITUGQkpk0nIkyvPLslYFcmqS6amvFuX5KpQq6tQq5/zsWxjFsKqeGDpIMuxWC4IAMBqQkAFAACAVaUzJo3lLakelVePnnb8C1ssFWp1zdVqyi/+WK2pUG98LLuuMtWaMtWaVFz6OcK2dVrXVdxxFAs0gqy4Yyts2+wwCADARURABQAAgFXl3t1GX3nWU2lRk1TYbhy3jFE80OiMOpeK6ypfqylfXSLIqtVUqNVVqrsq1SuaLi/9GJak2KLQ6lQX1nyoFXAUpAsLAIBlQ0AFAACAVaWxW5+rr+3zNJ7Xe97FL2hZCgaDag4ufbvneSrWGyHW4g6sxUFWsV5XrlZTrlaTtHSKFbSs05YPNpYTnurKijo2A90BAHiXCKgAAACw6uzpt7SnX0olk8pks8v62MYYRR1bUcdWu0JL3qfmusrX6vOX2umdWPOdWRXXVaXiarZSXfp5JEXnA6zomYPdA40gK2Qx0B0AAImACgAAADiLY1lKBS2lgoElb/c8T2XX1Vytrvx899WZQVZhIeA690B3x5jT5l+dFWQx0B0AsEEQUAEAAADvkTFGYbsxTL0ttPRawrrnzYdUtUaQddpywsaxiusqXa0qXV26C0uSIrZ91lLCk0FW3LEVsW26sAAAax4BFQAAALACbGOUCDhKvM1A93LdVWFRgHXmUPd8taZivTETa6pcOefzLNV5dfIjA90BAGsBARUAAADgk5BtKWQH1bz0KCy5nqdSva65M+ZfLe7KKtVd5ao15aq1cz5P0LIWhrc3hrmfHmRFHVsWXVgAAB8RUAEAAACrlGWMoo6jqOOo4x0Gup/ZfXXmUsKZiquZdzHQ/VR45SgesBd2Jgwy0B0AsIIIqAAAAIA17N0MdC/VXeXrjSWDZwVZtZqK72Kge8AyitmnBrqfFmQ5tqKOI8c6FWA9etzV1/Z5Gs9LnTHp3t1Ge/pZaggAWJrvAdUf/cU/KBwMyDJGlmXpj377LuWLZX39wcc1nZ5Ta1NcX7z7NsUiIXmep+889rwOHjuhYMDR5++8RX3drZKkZ/Yf1T899bIk6ZO37NJNA5dLkt4andK3H35K1VpdV23ZpM9+/Hp+8wMAAIANwxijiGMr4rz9QPfFQ9wL9ZMdWKeCrIrrKu2+80D3uGPryIyl7x22Vak5km1rrGjry8/Z8jxHn9hir9RLBQCsYb4HVJL0B7+xR/FoeOH6o08f0BWXdmvPzQN69On9+uEzB7T3I9fp4LFhTcxk9Se/u1dvjEzqvkef0R/+1p3KF8t65Ml9+rf/4i5J0pe/+bAGtm5WLBLSfT94Vr9xx826rKddf/F3P9ahY8O66vJNfr1UAAAAYNWxjVEyEFAyEJAiS9+nXHfPGuReWDQbK187NdD90UFXNVuyFmVRVUl//opRxm50XUUdWzG7sYQw5tiN6/PHbX6hDAAbzqoIqM60//Cg/uA39kiSbrr6cv2f//lR7f3Iddp/eFA3DmyRMUb9vR0qlirK5Ao6PDimKy/rUSzSWJd/5WU9euX4sLb1dalUqai/t0OSdOPAFr18eJCACgAAAHiPTg50bzlHF5breSrW65qr1vWN58vyrLqMqUumLpmaZBq3vdNSQqnRiRVzTl6chfAqNh9qRR1bDjsTAsC64ntAZWT01fsekzFGt16zTbdeu13ZfFGpRFSSlIxHlM0XJUnpXEHNydjC1zYlY0rnCpo983giptlcoXH/xOnH07nCRXplAAAAwMZhGTMfIjnqDDsay0veGffpiHn67CVSfr77Kj+/E2G+Xp+/3jh+shNrqnzu5wvbViO8sk8FWYs7sWKOrQAhFgCsGb4HVP/9b35CzcmYsvmivnrfY+pqS512uzHmosyMevLF1/XkS4clSV+48yb1dLat+HMC70YqmfS7BGDD4zwE/MU5uPb8wc1l/fFPCyrVTh0LO9K/vjmmTa1L70Z4kut5KtRqp3YhrNZODXevnjruep7ykvJ1V5N1VyqfPRsraFuNQe4BR/FAYNHnJ3cpdNid8F3iPAT8tV7Owbm5uXPe5ntAdbLzKRmLaPf2Pr0xMqVkLKJMrqBUIqpMrqDE/HyqpkRUs9n8wtems3k1JaJqTkR1+K2xU8dzeW27pKtx/1z+tONN851ZZ7r12u269drtkqRMNqtMNrvsrxV4r1LJJP8WAZ9xHgL+4hxcmz7YJf3hDeasXfw+2FVWJvs2bVGLROYvbQFbCtiSTgVbnuepWHdVmJ+Hla/Vla+f0ZVVq6vkeXqnfz0ndyeMOufuxApt8BCL8xDw13o6B+236Wz1NaAqV6ryPCkcCqhcqerV4yO649ZdGti2Wc8cOKo9Nw/omQNHNbCtT5I0sHWzHv/Fa7pux2V6Y2RS4VBQqURUO/p79d2fvqh8sfHN7pXjI/rMh9+nWCSkcDCo48MTuqynXc/uP6bb3n+lny8ZAAAA2BD29Fva078yj22MUXQ+RDrXugfP81R23YXA6szwqjAfalVd7x13J7SNOTu8sp2FOVlRx1HE3tghFgBcKF8Dqmy+pK/9408kSa7r6f07L9POLZt0SXebvv7gE/r5viNqTcX1xb23SZKuunyTDh4b1v/8lw8oGLD1+TtvkSTFIiF98pZd+sr/+31J0h237loYmP5f7rlR3/7+U6pU69q5pVdXbem9+C8UAAAAwEVljFHYthW2bbWeY7C753mquN6pTqz60mFWxXWVrdaUrdaWfBxJsqRFA93tsz6PObYiti2LEAsAlmTqhaEzZxdueOuldQ5r33pq5QTWKs5DwF+cg1gNqm/XiTUfapXq7js+jpEWlg5G53ckPHNJYdSxZa+yEIvzEPDXejoHbctSPB5f8jbfZ1ABAAAAwGoWsCw1BS01BQPnvE/NdeeXDdbPCLNOzckqLdxWf9vniyzsTLi4E2t+SaHdCLMcdigEsM4QUAEAAADABXIsS8mgpaTOHWLVPW8htDqtE6teP+14sd64TL3NPPmwbTXCK/vcw90DhFgA1hACKgAAAAC4CGxjlAg4SgTO/WOY63kq1pcY6L7o80K9rlLdVale0fTbPF/QspYOr+xTw92DljnncPdHj7vzOzHOLuzEuKef0AvAyiCgAgAAAIBVwjJmPlByJIWWvI/neSrW3VPD3ed3JFxquHul4mq2cu4dCgOWUcw+e6D7L0ct/dVLlso1W5KlsbzRV571JLmEVABWBAEVAAAAAKwhxhhF5zui2s5xH8/zVH674e7zoVbV9ZR2q0pXTw+x/u6Iq2pIskInH89S2bP05weNakFHQctSyLYUsCwFLaOgZZ1+sS0FLKPQ/HXbnLtTCwAkAioAAAAAWHeMMQrbtsK2rdZQ8Jz3q7jukgFWvliVZ9UlU5MxroxxJeMqV5PGS28/5H0plqSgbS0dZBmzcFvIOhl6NW4LLrrNIuAC1jUCKgAAAADYoIKWpWAwqOYzMqwOp66xfONzT54kTzKuOmKu7ug1qriuynVXVddrLCU88zJ/W3n+et3z5udmuedda8AyC+HVqSDLnAqzFgVfgfnjC/ebD8Lo4gJWLwIqAAAAAMBp7t3dmDnVaJYykozClqXf22XUFXnvM6hqrqfqUkGW682HWe5CmFU9eXw+6KosHPdUdesq6L13cJ18FYs7txYHWWeGWUstWwxYlhyLgAtYKQRUAAAAAIDTNAahn9zFTxe8i59jGTmWrYjs8/p6z/NU9eZDrnojzFqye6t+KsyqnBF6nezoKruuVDuvMmSbk8HV0p1bp3Vv2aeCrVO308UFnAsBFQAAAADgLHv6Le3pl1LJpDLZrK+1GGMa86gsS7Hz/Cm27i3u4vIWhVmLg6yzO7cWh191z1OxXlfx/Jq4JOmsIGulBs4/enz5AkbgYiCgAgAAAACse7YxsucHx58Pz/NU9051ZlUXhVxnLluszs/oOmvJ4qLL+Xo3A+dfHDX6m4NG5bqRLEtjRUtffs6o4tr6ZL/DUkWsSgRUAAAAAAC8A2OMHGPkWJZICrLZAAAQqUlEQVSi5/kYruedtjTxZEfXcg+c//vjrqoByQqcOlaV9B9flSbV6MJaPHR+cUfXyU6ugGWddv3k54vvZ7NcEcuIgAoAAAAAgIvAMkYh2yhkn/9Su3czcP4bhZokVzKN3RdPfp6vuLJNY7ljve5d0K6KkuQYc1agdTLsOlf4dXK5YmDRdYugCyKgAgAAAABgzXg3A+c7A3WN5Zc4HpO+sMVeCLmqZyxLXDyH68zri4fNn7xe8zzVLnAml6QzurdOBVyhJbq2lurmClqWnHc5mwurFwEVAAAAAADryL27jb7yrKfSouAobDeOSxe+q6LUmMlVWxg8f3pX1+kh1+nh18mh9NXT7uep6tYlnX/SZaTTAq7z6eYKWuZdD6HH8iOgAgAAAABgHWns1reyu/gZYxSYX+J3vjO5pEbQVfVOLU9cCLS8U/O3FgIu7/TdF6uut3C/mneqs+tCnDmf6/Sw6+zrJ5c3ntndtRzzuU7txDi7IXZiJKACAAAAAGCd2dNvaU+/31W8M2OMgqYR7FwI9226uc7V3XVy+Pzibq7lms9lm5NBljlr6eLSg+lPv/74W57+w/PzOzFKGstLX3nWk+Su25CKgAoAAAAAAKxpjQH0tkLnv2pRkk6fz3Uy9KovCru803dXbHR2zX++qLur7nkqXsB8rr971VU1LBnPkldql9yQSnXpa/u8NRE8ng8CKgAAAAAAAC3ffK665zV2Vjxj4PzSM7jOntWVr9TkyZMxrjydWi44vsTw+/WCgAoAAAAAAGCZGGPkGCPHkqLnGXT97Yt1jeU9efKkRQFVZ2yZilyF1ufCRQAAAAAAgDXq3t1GYduoEds0AqrFOzGuR3RQAQAAAAAArCIXYyfG1YaACgAAAAAAYJU5uRNjKplUJpv1u5wVt36jNwAAAAAAAKwJBFQAAAAAAADwFQEVAAAAAAAAfEVABQAAAAAAAF8RUAEAAAAAAMBXBFQAAAAAAADwFQEVAAAAAAAAfEVABQAAAAAAAF8RUAEAAAAAAMBXBFQAAAAAAADwFQEVAAAAAAAAfEVABQAAAAAAAF8RUAEAAAAAAMBXBFQAAAAAAADwFQEVAAAAAAAAfEVABQAAAAAAAF8RUAEAAAAAAMBXBFQAAAAAAADwFQEVAAAAAAAAfEVABQAAAAAAAF+ZemHI87uI1SabzcqyyO7gv1yhpEQ07HcZwIbGeQj4i3MQ8B/nIeCv9XQOuq6rZDK55G3ORa5lTTjXHxZwsf3ff/9T/dFv3+V3GcCGxnkI+ItzEPAf5yHgr41yDtImBAAAAAAAAF8RUAEAAAAAAMBXBFTAKnbrNdv8LgHY8DgPAX9xDgL+4zwE/LVRzkGGpAMAAAAAAMBXdFABAAAAAADAV+ziB6xCM9m8vvXQk8rmizIyuuWabfro9Tv8LgvYcFzX1Ze/+X01JaL60udu97scYMMplMr6m0ee1sjkrIyMfvPOD6h/U4ffZQEbxo+fO6Sf7zsiY6Se9mZ9/q4PKODwIySwkv764ad04OgJJWJh/fHvfEaSlC+W9fUHH9d0ek6tTXF98e7bFIuEfK50+fG/C7AK2cboVz/6fvV1t6pUrup//+bDuvKyHvW0N/ldGrCh/OSFV9XVllKpXPW7FGBD+s5jz2tnf6/+5T0fVq1eV6Va87skYMOYzeb10xde1f/yLz+jYMDRXz3wuF449IZu3rXV79KAde2mXZfrtuuu1LcefnLh2KNPH9AVl3Zrz80DevTp/frhMwe09yPX+VjlymCJH7AKpRJR9XW3SpLCoYC6WlNK5wo+VwVsLLPZvA4cPaEP7N4YQymB1aZYqujI4Lg+sLvxw7Bj24qG199vi4HVzHVdVWt11V1X1WpNTYmo3yUB697Wvi5FI8HTju0/PKibrr5cknTT1Zfr5dcH/ShtxdFBBaxyU+mchsZndFlvm9+lABvKd370vPZ+5H0qVeieAvwwlc4pHg3r299/SsPjs+rratVnP369QsG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            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uFo2hoN9OQti",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        },
        "outputId": "483d361c-0f12-409f-99aa-d9b6809125ab"
      },
      "source": [
        "model = KMeans(n_clusters = 6,\n",
        "            init='k-means++',\n",
        "            max_iter=500,\n",
        "            random_state=42)\n",
        "model.fit(X3)\n",
        "labels = model.labels_\n",
        "#centroids = model.cluster_centers_\n",
        "\n",
        "df['cluster'] =  labels\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>Gender</th>\n",
              "      <th>Age</th>\n",
              "      <th>Annual Income (k$)</th>\n",
              "      <th>Spending Score (1-100)</th>\n",
              "      <th>cluster</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",
              "      <td>0</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",
              "      <td>1</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",
              "      <td>0</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",
              "      <td>1</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",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>196</td>\n",
              "      <td>Female</td>\n",
              "      <td>35</td>\n",
              "      <td>120</td>\n",
              "      <td>79</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>197</td>\n",
              "      <td>Female</td>\n",
              "      <td>45</td>\n",
              "      <td>126</td>\n",
              "      <td>28</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>198</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>126</td>\n",
              "      <td>74</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>199</td>\n",
              "      <td>Male</td>\n",
              "      <td>32</td>\n",
              "      <td>137</td>\n",
              "      <td>18</td>\n",
              "      <td>2</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",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>200 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "     CustomerID  Gender  ...  Spending Score (1-100)  cluster\n",
              "0             1    Male  ...                      39        0\n",
              "1             2    Male  ...                      81        1\n",
              "2             3  Female  ...                       6        0\n",
              "3             4  Female  ...                      77        1\n",
              "4             5  Female  ...                      40        0\n",
              "..          ...     ...  ...                     ...      ...\n",
              "195         196  Female  ...                      79        4\n",
              "196         197  Female  ...                      28        2\n",
              "197         198    Male  ...                      74        4\n",
              "198         199    Male  ...                      18        2\n",
              "199         200    Male  ...                      83        4\n",
              "\n",
              "[200 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tE0JfgZdNNRM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "8ff27ebc-83e2-4437-bb55-51359ff7a2c9"
      },
      "source": [
        "\n",
        "fig = px.scatter_3d(df,\n",
        "                    x=\"Age\",\n",
        "                    y=\"Annual Income (k$)\",\n",
        "                    z=\"Spending Score (1-100)\",\n",
        "                    color='cluster',\n",
        "                    hover_data=[\"Age\",\n",
        "                                \"Annual Income (k$)\",\n",
        "                                \"Spending Score (1-100)\"],\n",
        "                    category_orders = {\"cluster\": range(0, 5)},\n",
        "                    )\n",
        "\n",
        "fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))\n",
        "fig.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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          "metadata": {
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "udCZpuvdPXIC",
        "colab_type": "text"
      },
      "source": [
        "# Clustering Analysis\n",
        "\n",
        "From a visual perspective of the graph above, we can identify some clusters of clients, from a raw behavioural perspective.\n",
        "\n",
        "*    *Cluster 0* (**Dark Blue**): Clients of all ages, with a low annual income and low spending score.\n",
        "*    *Cluster 1* (**Purple**): Young clients (< 40 years old) with low average annual income and high spending score. \n",
        "*   *Cluster 2* (**Magenta**): Clients of all ages, with a an average to high annual income and low spending score.\n",
        "*   *Cluster 3* (**Red**): Clients with age greather than 50 years, with an average annual income and average spending score.\n",
        "*   *Cluster 4* (**Orange**): Young clients (< 40 years old), with high annual income and high spending score.\n",
        "*   *Cluster 5* (**Yellow**): Clients with age greather than 50 years, with an average annual income and average spending score."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kaPBRap-dr3G",
        "colab_type": "text"
      },
      "source": [
        "# Conclusions\n",
        "\n",
        "After all the data processing ad visualization, it's safe to assume that this particular dataset was clustered in some pretty efficient ways. Regarding the age X spending score, we see that it's a bit unclear how the data could be clustered, so the algorithm help us a lot. The other graphs are way easier to understand, despite the fact that the last one give us a multidimensional analysis and let's us split the customers in more personalizaed groups.\n",
        "\n",
        "This have some interesting pratical applications. For example, customers from *cluster 2* might be biased to spent more of their income on a particular business service, while customers from *cluster 0* might be not. In another way, *cluster 4* represents young customers, with high acquisitive power, individuals who the business would like to preserve as much as possible on its customer base."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "q8mGOuUGZTCE",
        "colab_type": "text"
      },
      "source": [
        "# Acknowledgements\n",
        "\n",
        "Besides the work done here, I need to say thanks to the following data scientists, whose notebooks helped me as sources to continue with this study.\n",
        "\n",
        "  * [Customer Segmentation with Machine Learning](https://towardsdatascience.com/customer-segmentation-with-machine-learning-a0ac8c3d4d84), by Ceren Iyim\n",
        "  * [Customer Segmentation (K-Means) | Analysis](https://www.kaggle.com/kushal1996/customer-segmentation-k-means-analysis), by Kushal Mahindrankar\n",
        "  * [KMeans Clustering in Customer Segmentation](https://www.kaggle.com/vjchoudhary7/kmeans-clustering-in-customer-segmentation), by Vijay Choudhary, who also provided the example dataset available [here](https://www.kaggle.com/vjchoudhary7/customer-segmentation-tutorial-in-python)."
      ]
    }
  ]
}