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      "execution_count": 22,
      "metadata": {
        "id": "_6GYK5xX79-F"
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      "source": [
        "# Importing library\n",
        "# Adding Preliminary Libraries\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Importing Dataset\n",
        "\n",
        "seed = pd.read_csv('Seed_Data.csv')\n",
        "seed\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "tQ3vlaEK8AuC",
        "outputId": "950b40ee-741d-49ee-c590-0845286f47d7"
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      "execution_count": 23,
      "outputs": [
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            "text/plain": [
              "         A      P       C     LK     WK  A_Coef    LKG  target\n",
              "0    15.26  14.84  0.8710  5.763  3.312   2.221  5.220       0\n",
              "1    14.88  14.57  0.8811  5.554  3.333   1.018  4.956       0\n",
              "2    14.29  14.09  0.9050  5.291  3.337   2.699  4.825       0\n",
              "3    13.84  13.94  0.8955  5.324  3.379   2.259  4.805       0\n",
              "4    16.14  14.99  0.9034  5.658  3.562   1.355  5.175       0\n",
              "..     ...    ...     ...    ...    ...     ...    ...     ...\n",
              "205  12.19  13.20  0.8783  5.137  2.981   3.631  4.870       2\n",
              "206  11.23  12.88  0.8511  5.140  2.795   4.325  5.003       2\n",
              "207  13.20  13.66  0.8883  5.236  3.232   8.315  5.056       2\n",
              "208  11.84  13.21  0.8521  5.175  2.836   3.598  5.044       2\n",
              "209  12.30  13.34  0.8684  5.243  2.974   5.637  5.063       2\n",
              "\n",
              "[210 rows x 8 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>A</th>\n",
              "      <th>P</th>\n",
              "      <th>C</th>\n",
              "      <th>LK</th>\n",
              "      <th>WK</th>\n",
              "      <th>A_Coef</th>\n",
              "      <th>LKG</th>\n",
              "      <th>target</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>15.26</td>\n",
              "      <td>14.84</td>\n",
              "      <td>0.8710</td>\n",
              "      <td>5.763</td>\n",
              "      <td>3.312</td>\n",
              "      <td>2.221</td>\n",
              "      <td>5.220</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>14.88</td>\n",
              "      <td>14.57</td>\n",
              "      <td>0.8811</td>\n",
              "      <td>5.554</td>\n",
              "      <td>3.333</td>\n",
              "      <td>1.018</td>\n",
              "      <td>4.956</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>14.29</td>\n",
              "      <td>14.09</td>\n",
              "      <td>0.9050</td>\n",
              "      <td>5.291</td>\n",
              "      <td>3.337</td>\n",
              "      <td>2.699</td>\n",
              "      <td>4.825</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>13.84</td>\n",
              "      <td>13.94</td>\n",
              "      <td>0.8955</td>\n",
              "      <td>5.324</td>\n",
              "      <td>3.379</td>\n",
              "      <td>2.259</td>\n",
              "      <td>4.805</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>16.14</td>\n",
              "      <td>14.99</td>\n",
              "      <td>0.9034</td>\n",
              "      <td>5.658</td>\n",
              "      <td>3.562</td>\n",
              "      <td>1.355</td>\n",
              "      <td>5.175</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>205</th>\n",
              "      <td>12.19</td>\n",
              "      <td>13.20</td>\n",
              "      <td>0.8783</td>\n",
              "      <td>5.137</td>\n",
              "      <td>2.981</td>\n",
              "      <td>3.631</td>\n",
              "      <td>4.870</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>206</th>\n",
              "      <td>11.23</td>\n",
              "      <td>12.88</td>\n",
              "      <td>0.8511</td>\n",
              "      <td>5.140</td>\n",
              "      <td>2.795</td>\n",
              "      <td>4.325</td>\n",
              "      <td>5.003</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>207</th>\n",
              "      <td>13.20</td>\n",
              "      <td>13.66</td>\n",
              "      <td>0.8883</td>\n",
              "      <td>5.236</td>\n",
              "      <td>3.232</td>\n",
              "      <td>8.315</td>\n",
              "      <td>5.056</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>208</th>\n",
              "      <td>11.84</td>\n",
              "      <td>13.21</td>\n",
              "      <td>0.8521</td>\n",
              "      <td>5.175</td>\n",
              "      <td>2.836</td>\n",
              "      <td>3.598</td>\n",
              "      <td>5.044</td>\n",
              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>209</th>\n",
              "      <td>12.30</td>\n",
              "      <td>13.34</td>\n",
              "      <td>0.8684</td>\n",
              "      <td>5.243</td>\n",
              "      <td>2.974</td>\n",
              "      <td>5.637</td>\n",
              "      <td>5.063</td>\n",
              "      <td>2</td>\n",
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              "<p>210 rows × 8 columns</p>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Preparing Data\n",
        "Y = seed['target']          # Split off classifications\n",
        "X = seed.iloc[:, [0, 1, 2, 3, 4, 5, 6]].values # Split off features\n"
      ],
      "metadata": {
        "id": "uNNBmzHD8nbA"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we will separate the target variable from the original dataset and again convert it to an array by using numpy.\n",
        "Y = seed['target']\n",
        "Y = np.array(Y)\n",
        "Y"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ladgurkq8rqR",
        "outputId": "427c46fc-661a-43b4-f4e1-6ea5783ece9f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Seed dataset clustering plot\n",
        "# Visualise Classes\n",
        "# seed dataset has three classes in target\n",
        "\n",
        "\n",
        "plt.scatter(X[Y == 0, 0], X[Y == 0, 6], s =80, c = 'orange', label = 'Target 0')\n",
        "plt.scatter(X[Y == 1, 0], X[Y == 1, 6], s =80,  c = 'yellow', label = 'Target 1')\n",
        "plt.scatter(X[Y == 2, 0], X[Y == 2, 6], s =80,  c = 'green', label = 'Target 2')\n",
        "plt.title('Seed dataset plot')\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "CkWZ5vGp8uPo",
        "outputId": "4f6b3938-d006-4eb9-971d-26a8e78cb4af"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3a81caeaf0>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        },
        {
          "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": [
        "# Kmeans Clustering for Seed Dataset\n",
        "from sklearn.cluster import KMeans\n",
        "\n",
        "# Calculating WCSS (within-cluster sums of squares) \n",
        "\n",
        "\n",
        "wcss=[]\n",
        "for i in range(1, 11):\n",
        "    kmeans = KMeans(n_clusters = i, init = 'k-means++', max_iter = 300, n_init = 10, random_state = 0)\n",
        "    kmeans.fit(X)\n",
        "    wcss.append(kmeans.inertia_)\n",
        "# Running K-Means Model\n",
        "\n",
        "\n",
        "cluster_Kmeans = KMeans(n_clusters=3)\n",
        "model_kmeans = cluster_Kmeans.fit(X)\n",
        "pred_kmeans = model_kmeans.labels_\n",
        "pred_kmeans\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3VwGGfwW8ws7",
        "outputId": "4a72618a-3e56-4445-b731-372b82c46881"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_kmeans.py:870: 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",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2,\n",
              "       2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 1, 2, 0, 2, 2, 2, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 2, 2,\n",
              "       2, 2, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 2, 1, 1, 1, 1, 1, 1, 1,\n",
              "       2, 2, 2, 2, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0], dtype=int32)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Kmeans Clustering plot for Seed dataset\n",
        "# Visualizing Output\n",
        "# In the above output we got value labels: ‘0’, ‘1’  and ‘2’. For a better understanding, we can visualize these clusters.\n",
        "\n",
        "\n",
        "plt.scatter(X[pred_kmeans == 0, 5], X[pred_kmeans == 0, 0], s = 80, c = 'orange', label = 'Target 0')\n",
        "plt.scatter(X[pred_kmeans == 1, 0], X[pred_kmeans == 1, 5], s = 80, c = 'yellow', label = 'Target 1')\n",
        "plt.scatter(X[pred_kmeans == 2, 0], X[pred_kmeans == 2, 5], s = 80, c = 'green', label = 'Target 2')\n",
        "\n",
        "\n",
        "plt.title('Kmeans Plot for Seed dataset')\n",
        "\n",
        "\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "ZjP0Ixdw8zRO",
        "outputId": "8fe9045b-2b01-40e1-9e34-14f75586f881"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3a81c40ca0>"
            ]
          },
          "metadata": {},
          "execution_count": 28
        },
        {
          "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": [
        "# KNN accuracy\n",
        "\n",
        "\n",
        "seed=pd.read_csv('Seed_Data.csv')\n",
        "\n",
        "X=seed.iloc[:,:-1].values\n",
        "y=seed.iloc[:,-1].values\n"
      ],
      "metadata": {
        "id": "B5uWumQe82ey"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Splitting the dataset into the Training set and Test set\n",
        "\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state = 0)\n"
      ],
      "metadata": {
        "id": "7ZbJPhP0882q"
      },
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Calculating Accuracy score, Confusion matrix, Classification report.\n",
        "\n",
        "\n",
        "from sklearn import neighbors, datasets, preprocessing\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "\n",
        "X=seed.iloc[:,:-1].values\n",
        "y=seed.iloc[:,-1].values"
      ],
      "metadata": {
        "id": "t6bzBr-78_bB"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Xtrain, Xtest, y_train, y_test = train_test_split(X, y)\n",
        "scaler = preprocessing.StandardScaler().fit(Xtrain)\n",
        "Xtrain = scaler.transform(Xtrain)\n",
        "Xtest = scaler.transform(Xtest)\n",
        "\n",
        "\n",
        "knn = neighbors.KNeighborsClassifier(n_neighbors=14)\n",
        "knn.fit(Xtrain, y_train)\n",
        "y_pred = knn.predict(Xtest)\n",
        "\n",
        "\n",
        "print('Accuracy score:', accuracy_score(y_test, y_pred))\n",
        "print('Confusion matrix:')\n",
        "print(confusion_matrix(y_test, y_pred))\n",
        "print('Classification report:')\n",
        "print(classification_report(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aHsx39fp9Cr5",
        "outputId": "b0d8a1d7-bbda-405b-8cde-f603e80c999e"
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Accuracy score: 0.9056603773584906\n",
            "Confusion matrix:\n",
            "[[16  1  2]\n",
            " [ 1 18  0]\n",
            " [ 1  0 14]]\n",
            "Classification report:\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.89      0.84      0.86        19\n",
            "           1       0.95      0.95      0.95        19\n",
            "           2       0.88      0.93      0.90        15\n",
            "\n",
            "    accuracy                           0.91        53\n",
            "   macro avg       0.90      0.91      0.91        53\n",
            "weighted avg       0.91      0.91      0.91        53\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import Library for Hierarchical clustering\n",
        "\n",
        "\n",
        "import matplotlib.pyplot as plt  \n",
        "from sklearn.cluster import AgglomerativeClustering\n"
      ],
      "metadata": {
        "id": "ZkhA62249GBo"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting of Dendrogram\n",
        "\n",
        "\n",
        "import scipy.cluster.hierarchy as sch\n",
        "\n"
      ],
      "metadata": {
        "id": "VqbO8-Xv9KGz"
      },
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Decide the number of clusters by using this dendrogram\n",
        "Z = sch.linkage(X, method = 'median')\n",
        "plt.figure(figsize=(20,7))\n",
        "den = sch.dendrogram(Z)\n",
        "plt.title('Dendrogram for the clustering of the dataset seed)')\n",
        "plt.xlabel('Type')\n",
        "plt.ylabel('Euclidean distance in the space with other variables')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "1xSADg1e9MHR",
        "outputId": "8b76acf1-3d19-4b15-ffc7-be3c1937d162"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Euclidean distance in the space with other variables')"
            ]
          },
          "metadata": {},
          "execution_count": 35
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 2000x700 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Building an Agglomerative Clustering Model\n",
        "\n",
        "\n",
        "#Initialise Model\n",
        "\n",
        "\n",
        "cluster_H = AgglomerativeClustering(n_clusters=3)\n"
      ],
      "metadata": {
        "id": "kERwX4kt9O-n"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Modelling the data\n",
        "model_clt = cluster_H.fit(X)\n",
        "model_clt\n",
        "pred1 = model_clt.labels_\n",
        "pred1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfNn7r3u9SJm",
        "outputId": "40b02af7-30bd-461d-acc5-cd28136e1e1d"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2, 2,\n",
              "       2, 0, 2, 2, 0, 0, 2, 2, 0, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 0, 2,\n",
              "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0,\n",
              "       2, 2, 2, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
              "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1,\n",
              "       2, 1, 2, 2, 1, 2, 2, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
              "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Plotting the HCA Cluster\n",
        "\n",
        "\n",
        "plt.scatter(X[pred1 == 0, 0], X[pred1 == 0, 3], s = 80, c = 'orange', label = 'Target 0')\n",
        "plt.scatter(X[pred1 == 1, 1], X[pred1 == 1, 4], s = 80, c = 'yellow', label = 'Target 1')\n",
        "plt.scatter(X[pred1 == 2, 1], X[pred1 == 2, 5], s = 80, c = 'green', label = 'Target 2')\n",
        "plt.title('Hierarchical Plot for Seed dataset')\n",
        "plt.legend()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 469
        },
        "id": "WcD0HHSb9Uhg",
        "outputId": "0fa7be40-bd3a-4296-d262-6cdeebbb6a2f"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f3a8185ef70>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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RwLlkt0uXLu6fm9TrA6jz3qHIwR4L0s0jjzyCN998E7fccgtmzJiBQYMG4fTp07BYLCgtLUVVVZV7KZovt9xyCzZs2IDx48fjV7/6FY4cOYJVq1ahX79+Xv+4efPss89i2LBhGDhwIGbPno0ePXqgqqoKb7/9tirprx955BGUlpaioKAAd955JwYNGoSff/4Zb775JlatWoUBAwao8jrU8sQTT2DLli0YNmwY7rnnHphMJrzwwgtoaGjAk08+6d7uiiuugNFoxNKlS3Hq1CmYzWaMHDnSa44IX2JiYrB06VLccccduO666zBlyhT3ctOMjAw89NBDil/H/fffjzNnzmD8+PG49NJL0djYiJ07d6KkpAQZGRm44447ADh7Ip544gnMnTsXVVVVyMvLQ0JCAo4cOYKNGzdi9uzZmDNnDgwGA9asWYMxY8agf//+uOOOO9C1a1dUV1fjww8/RGJiIt566y0AznwR77zzDoYPH4577rkHNpsNzz33HPr3748vv/wy4DV54okncNddd2HkyJGYNGkSjhw5grVr17aYYyH1fdO2bVv069cPJSUluOSSS9CxY0dcdtlluOyyyzBixAg8+eSTOHfuHLp27Yp33323RZ6Turo6pKWlIT8/HwMGDEB8fDzee+897N69G08//TQAyLo+gwYNAuBchjx58mTExMRg7Nixmqd4pxAJzWIUijTelg42dd111wVcbiqKolhXVyfOnTtXzMzMFGNjY8WLL75YvPbaa8Vly5a5lwW6lk4+9dRTLc7jcDjEJUuWiN27dxfNZrN45ZVXips2bRKnT58udu/e3b2dv2OIoiju27dPHD9+vNi+fXuxTZs2Yp8+fcR58+a5v+9abnrixAmv16Hpsjlvr/Onn34S77vvPrFr165ibGysmJaWJk6fPl388ccfZb0OUZS33NTX6w10vD179oi5ublifHy82K5dO/H6668Xd+7c2WLf1atXiz179nQvo/S39NTfe6akpES88sorRbPZLHbs2FGcOnWq+P3333tsM336dDEuLi7g63HZvHmzeOedd4qXXnqpGB8fL8bGxoqZmZni/fffL1qt1hbbv/766+KwYcPEuLg4MS4uTrz00kvFe++9Vzx48KDHdp9//rl42223iRdddJFoNpvF7t27ixMnThTff/99j+0++ugjcdCgQWJsbKzYs2dPcdWqVe73kRR//etfxR49eohms1kcPHiwuHXrVvG6667zWG4q532zc+dOd3ua/sy///5793s/KSlJLCgoEI8dO+axTUNDg/jII4+IAwYMEBMSEsS4uDhxwIAB4l//+tcW7ZZ6fRYtWiR27dpVNBgMXHoa5QRRVKn/l4iIiFo9zrEgIiIi1TCwICIiItUwsCAiIiLVMLAgIiIi1TCwICIiItUwsCAiIiLV6J4gy+Fw4NixY0hISGCaVyIiogghiiLq6urQpUsXvzV8dA8sjh075q49QERERJHlu+++81t1WPfAIiEhAYCzYb7KThMREVF4qa2tRXp6uvs+7ovugYVr+CMxMZGBBRERUYQJNI2BkzeJiIhINQwsiIiISDUMLIiIiEg1us+xICKi1kkURdhsNtjt9lA3hbwwGo0wmUxBp4JgYEFERJprbGzE8ePHcebMmVA3hfxo164dOnfujNjYWMXHYGBBRESacjgcOHLkCIxGI7p06YLY2FgmSAwzoiiisbERJ06cwJEjR9C7d2+/SbD8YWBBRKQya70VFVUVqGusQ0JsAnIycpASnxLqZoVMY2MjHA4H0tPT0a5du1A3h3xo27YtYmJi8O2336KxsRFt2rRRdBwGFkREKrFYLViybQlKD5TC5rC5nzcZTMjvm4/C4YXISskKYQtDS+knYNKPGj8j/pSJiFRQfqgc2WuyUbrfM6gAAJvDhtIDpchek43yQ+UhaiGRPhhYEBEFyWK1IK8kDw22BthEm9dtbA4bGmwNyCvJg8Vq0bmFRPrhUAhRJPjFCtRUAOfqgJgEIDkHaNt6x+zDzZJtS2Cz2yBC9LudCBE2hw3F24uxbsI6nVoXZfi7EPbYY0EUzk5agO1TgLI0YMdkoHKW89+yNOfzJ/nJN9Ss9VbnnAofPRXN2Rw2rN+/HjWnazRuWZQJwe+CIAh+HwsXLlT9nHLaVlZWFnC7n3/+GVOnTkViYiLat2+P3/zmN6ivr9e0bQwsiMLVsXLgnWzgu1Kg+U1LtDmffyfbuR2FTEVVRYs5FYHYHDZUVFVo06BoFKLfhePHj7sfK1euRGJiosdzc+bMkXW8xsZGVdsnxdSpU/HVV19hy5Yt2LRpE7Zu3YrZs2drek4GFkRy/WIFvi0BDq1x/vuLVf1znLQAW/MAR0PLP6Quos35/a157LkIobrGOkX71TbUqtySKBXC34XU1FT3IykpCYIguL8+ffo0pk6dipSUFMTHx2PIkCF47733PPbPyMjAokWLMG3aNCQmJrpv6KtXr3YvvR0/fjyWL1+O9u3be+z7xhtvYODAgWjTpg169uyJoqIi2Gw293EBYPz48RAEwf11cwcOHMA777yDNWvW4KqrrsKwYcPw3HPP4dVXX8WxY8dUu07NMbAgkkrPrth9S87/EfU/Zg+Izu2+Klbv3CRLQmyCov0SzYkqtyRKhenvQn19PW6++Wa8//77+PzzzzF69GiMHTsWR48e9dhu2bJlGDBgAD7//HPMmzcPO3bswN13340HHngAe/fuxahRo7B48WKPfbZt24Zp06bhgQcewP79+/HCCy/g5Zdfdm+3e/duAMDatWtx/Phx99fNffzxx2jfvj0GDx7sfu7GG2+EwWDAJ598oubl8MDAgkiKYLpi5fZw/GL1fh5fRBtwdD1wlmP2oZCTkQOTQd48eJPBhJyMHG0aFE3C+HdhwIABuOuuu3DZZZehd+/eWLRoEXr16oU333zTY7uRI0fi97//PXr16oVevXrhueeew5gxYzBnzhxccskluOeeezBmzBiPfYqKivDYY49h+vTp6NmzJ0aNGoVFixbhhRdeAAB06tQJANC+fXukpqa6v27uhx9+QHJyssdzJpMJHTt2xA8//KDWpWiBq0Io+qg9a7xpV6yvT02iDRDtzu1GVwLts5z77VvS8g+jYALS84HMu4AGa8t21lRI/0Pa9PzWCqD7RCWvkIKQEp+C/L75zvwVEn5uJoMJBf0KkByXHHDbVi+Mfxfq6+uxcOFCvP322zh+/DhsNht++eWXFj0WTXsLAODgwYMYP368x3PZ2dnYtGmT++svvvgCO3bs8OjJsNvtOHv2LM6cORP22UsZWLR20bR0K9CN/LJC5w1fLiVdsT2mO4MM0ea9h+NoCXD0Vc/nXe1M6iO/jQBwjmP2oVI4vBBlB8tgt9n9LjkVIMBkMGHusLk6ti6CnVM2f0WP34U5c+Zgy5YtWLZsGTIzM9G2bVvk5+e3mKAZFxcn+9j19fUoKirCbbfd1uJ7ctJsp6amoqbGs/fGZrPh559/Rmpqqux2ScXAorXS6iYcKsfK/d/IvysFvi8DRpQBXXKlH1dRV+xrwNENgNgI38GIl+dd7fxOYXGmGI7Zh0pWShbKJpUhryQPNrvNa8+FyWCCyWBC2aSyVp3WW5YYZfNX9Phd2LFjB2bMmOHufaivr0dVVVXA/fr06dNiTkTzrwcOHIiDBw8iMzPT53FiYmIClp+/5pprcPLkSXz22WcYNGgQAOCDDz6Aw+HAVVddFbCtSjGwaI0C3YSPvua8wfV9BOjzgHo9GP56R4LpOZE8VGEDPhrrDJoS+0o7v6KuWPv5dgTq4fDRTiUEE5CSo2xfUkVuZi4qZ1aieHsx1u9f36JWSEG/AswdNpdBhRzJOc73tpzfC51+F3r37o0NGzZg7NixEAQB8+bNg8PhCLjf/fffjxEjRmD58uUYO3YsPvjgA2zevNmj2uv8+fNxyy23oFu3bsjPz4fBYMAXX3yBffv24YknngDgXBny/vvvY+jQoTCbzejQoUOLc/Xt2xejR4/GrFmzsGrVKpw7dw733XcfJk+ejC5duqh3MZphYNHaSLkJwwGIDmB/MXDgSSC9wH8PRqCg4IcK4MvHgR93OY/tIpiA1BudzbC+p7znRPJQBQDxHGApCnx+GIC0cUCHAYGP6VXgPzCBCZD0mgQT0K0AaBPlY/YRMGyXlZKFdRPWYeXolaioqkBtQy0SzYnIycjhnAol2qY4/w5I7TXU8Xdh+fLluPPOO3Httdfi4osvxh/+8AfU1gYeghk6dChWrVqFoqIi/O///i9yc3Px0EMP4c9//rN7m9zcXGzatAl//OMfsXTpUsTExODSSy/FzJkz3ds8/fTTePjhh7F69Wp07drVZ2/JP//5T9x333244YYbYDAYMGHCBDz77LNBv35/BFEUFXysUq62thZJSUk4deoUEhPZdau77VPkde0Dzl9WwdRyGCHQcEq3fODgc8CJj5S11dd5m/rF6lzuqfSTfuBGQFHPg24EwGC+MGE0GkXbsF0rdPbsWRw5cgQ9evSQX4r7pMW54srvhyEgkn8XZs2aha+//hrbtm0LdVP8/qyk3r/ZYxFNAn2ikztfwMXbigcpwynNJyfK5e28zSkZqpDXCA2PrQYBGLgi4v6QSqbV3BmKHO2znD9fX+8DwPNDSAT8LixbtgyjRo1CXFwcNm/ejFdeeQV//etfQ90s1TCPRTSQmrgpqJtwkxUPUjLhqTIU0Oy83iidNR4tBAHY81B0pvVm9lFy6ZLr/HDRrcAZQDTlGv4YXRkxwWVlZSVGjRqFrKwsrFq1Cs8++6zHMEekY49FpJPziS7Ym7Ar+YztF+lzGtTgOu+glS3HTpXOGo8Wot05H8Zfr06kUrLMdygrhkat9lnOn++glc48Fedqnas/UnIibn7Ra6+9FuomaIo9FkrpUS8iELmf6Br/G/w5RRtQ/abGww8+zmutaPm8a9a4XgSjfueSLArTeodxxkUKsTbJzuRXmTOd/0ZYUNEatO4eCyWzzMNpIpncT3Q/fix/6ZZXag1zyNQ06U3Tn13HbOCnTwD4X9Otiouudp6r+QqSUF0TF9EGfPua916dSBTGGReJyL/WGVgoDQ7CaSLZf/c5J0hKvaGJNqD6LaDLLcCxTfr3OKghJtH3z04vPWcAIzZ4dsWe+R74/Pf6t6UFO/D1SuCKJaFuSPDCOOMiEfnX+gILScmhNgLXvdFyaaWSehFNqbEO33VjlRNUNG1f8lDg+DtNkjjJFaJP54IJgOhcduZrZrgeYhIvdMW6fFsSmrZ4c+ApIGNK5M+1COOMi0TkX+uaYyF1NYPYAFTcDBx9/cLTwZTuVavcdtMKm0pv7jHtnT0qBrOyuQkpOdD9bSOYgNRRwMczAvzsdGiHt4x+es/z8Ed0RMdcCyXXlNlHicJC6wos5GRohAPYPtF5Mw9mIpkrGDi63vfwia9y201JCookiEn0XLoll8EMdByk/PxKuHor9FyJ0oLRd0Y/V3ZAqTdCwQR0nwKk5UH9X0FHdExiVHJNW0P2UQJgBVACYM35f0MwcZ78aj2BhaLkUA5g663Akb8rm0h25O/AR+MAx1n4nFgodR2+rKDIh6af6NpnORMrQeYqh+PlQJfRytsgm9HZzh+ap9zWmwikT/D97csKz98EAxUQE5zb9Z8LXP5HwBArYR+ZfK2giTRKrilFMQuAKQDSAEwGMOv8v2nnn2cek3DRegILpcmhHOeAo6XKzvnNajgrXAYSYLmg0oyZTXn7RFdTAfkrKRyAKACCXm8dO/DpveEx2XTn7b57llzZAf0NMQkm5/dd2QHdwZ3KgQUQHZMYlVxTilLlALIBlAJo/rfAdv757PPbqUcQBL+PhQsXqno+uW0rKysLuN3ixYtx7bXXol27dmjfvr3m7QJaU2ChODmUA/j5U2W71v9b+rb+1uEHnbbaxyc6pdfk+DtA+kQdczqEeCknAMDh7Hn6aJzvniW52QGPlTuzZgr+AotWXkI9yjIukhIWAHkAGtAyqHCxnf9+HtTsuTh+/Lj7sXLlSiQmJno8N2fOHFnHa2yU8kFTXY2NjSgoKMBvf/tb3c7ZegKLoDI0OqDLpfLVhR1Mxkx/n+iUXpOfPwV634XIXlRkgKKbttgIfPo73993ZQccXw0MLQGyVzv/HV/tfN71M/CYM6Ny/o1om8Qo9ZpSlFoCZ+AgYeI8bADUm7ycmprqfiQlJUEQBPfXp0+fxtSpU5GSkoL4+HgMGTIE7733nsf+GRkZWLRoEaZNm4bExETMnj0bALB69Wqkp6ejXbt2GD9+PJYvX96iN+GNN97AwIED0aZNG/Ts2RNFRUWw2Wzu4wLA+PHjIQiC+2tvioqK8NBDDyErS7/fk9YTWAQ7c7/jIBkTyYL4JH+utmVWT8U3HoP/T3TJOVD2FnA4e1b6PqywXSEWbE9LTUXgOQyBsgPKWWUEAZKDoGiexMiMi62QFd6HP3yxAVgPQPvJy/X19bj55pvx/vvv4/PPP8fo0aMxduxYHD161GO7ZcuWYcCAAfj8888xb9487NixA3fffTceeOAB7N27F6NGjcLixYs99tm2bRumTZuGBx54APv378cLL7yAl19+2b3d7t27AQBr167F8ePH3V+Hi0j+yCmPa5a5kvwPgPMP2UmLhPwPgnPJn1J7HwMq74JnG5V0hwvAzV8C7fv73qRtijNg+lnBm/JcLRDfU0G7Qk0ARNfPL4iJsF/8L3DTdmX7yp4zI7WdnMRI0aYC0oMKF9v5/bTNwDpgwAAMGDDA/fWiRYuwceNGvPnmm7jvvvvcz48cORK///2FBHqPP/44xowZ4x5GueSSS7Bz505s2rTJvU1RUREee+wxTJ8+HQDQs2dPLFq0CI8++igWLFiATp06AQDat2+P1NRUTV+nEq2nxwI4P8s8Rv5+ggnoMU3iRLLYoJqIxp/QMvBReAPc90TgHBlKlpwCzjH8iCsAZjj/8xER9LyNHz9WvqRT6ZwZIYaTGKmVUToMrP3k5fr6esyZMwd9+/ZF+/btER8fjwMHDrTosRg8eLDH1wcPHkR2drbHc82//uKLL/DHP/4R8fHx7sesWbNw/PhxnDlzRpsXpKLWFViYk5V136eOcna7SplIdvkihC7XQlOitBwZPaYpWOFhcI7hh1NiKCnSbgV63wN1fj4O4KslyorPKZ0zc1khJzFSK6P0w4v2k5fnzJmDjRs3YsmSJdi2bRv27t2LrKysFhM04+LiZB+7vr4eRUVF2Lt3r/thsVjwzTffoE2bNmq9BM1E0F0hCGrWlwhUuvfQGhUarJJAKcaB80NEE+UPEZ21Oo+Xnh+6uh2SCc5kVCM2OAMttRx8Bvj3X+QXn1NaZTaxH5C1MHLKRquRwp5auRw4b1Ny/r6Yzu+nrR07dmDGjBkYP348AGcwUFVVFXC/Pn36tJgT0fzrgQMH4uDBg8jMzPR5nJiYGNjtOhReVCD6Awt/tUGk+mGLs9u76R/v5vUiXMJueEB05uKovBvoeYf3P/CXFZ4PLGQc8/M/ANf/n3Pf78uCqD2iAyEWuLzIeaP7+TN1jy23+NyxcuCLefLP03Slh6/3XrgIpwrAFOFSAORD+gROE4ACANoH2r1798aGDRswduxYCIKAefPmweEI/OHs/vvvx4gRI7B8+XKMHTsWH3zwATZv3gyhybLz+fPn45ZbbkG3bt2Qn58Pg8GAL774Avv27cMTTzwBwLky5P3338fQoUNhNpvRoUMHr+c7evQofv75Zxw9ehR2ux179+4FAGRmZiI+Pj74C+FFdA+FqJUGW04mw7AcHrADP+70XaPEnCxzfqgIHN8MVJVIS2IUUgZnQbn2WecTgmmQE0Nq9lTX+1FS0rQmImmlR9N6NsGksCdyK4QzYJCQgRUmAPpMXl6+fDk6dOiAa6+9FmPHjkVubi4GDhwYcL+hQ4di1apVWL58OQYMGIB33nkHDz30kMcQR25uLjZt2oR3330XQ4YMwdVXX40VK1age/fu7m2efvppbNmyBenp6bjyyit9nm/+/Pm48sorsWDBAtTX1+PKK6/ElVdeiU8/VZifSQJBFEVdP2bW1tYiKSkJp06dQmKixuNg26eo103fa7YzBbOUrlw1z6sVweR8jCgDzp10BhyyjxEDjPnMedM+aQG+XAB8v1HtlgbBAAx7Deh2PhX3oTXO4EorrgBg6Drv31f0vhCcQZuvoaxwctLiDBr8VQAGEFGviVRx9uxZHDlyBD169FA4R6AczuRXNnjvuTCdf5QBiLx5RrNmzcLXX3+Nbdu2hbopfn9WUu/f0dtjoUYa7KYOvyi9Gqm7xkEYa/opu/aAwmPYL6Qhb58FxHZUrXnBMThvXDn/dyGoALQfpvKXPVXp+9EQGzkrPYKpAEzkVy6ASjiHOZr/bXUNf1QiUoKKZcuW4YsvvsChQ4fw3HPP4ZVXXnEvLY0G0RtYBJ0G2wupXbmu4QElS1t1df4P/PEtCvdvUknzpAX4z99UbZ0iggnoPgkYvbvlfAc9hql8DZspfT9e/kRkrPQIpgIwkSRZANYBqIazqunq8/9Wn38+AoLv8yorKzFq1ChkZWVh1apVePbZZzFz5sxQN0s1Yf6xOgjBpMH2R8pKC8B5M7isELAUadMOtYg24MdPnNkolWT4dN1Iq/4FdSdvCtKPJxiB/v8LJPXzv0LClSRN62EqbwXAlL4fY9sH1RTdKAmcXO+dcJ6ISmEoGVonv9Laa6/JmSwfeaK3x0LTbu/zKy0CdeUm9tWwDWqyAxddBcVvhzPfA9VvqtoiWbNJOw0HLl8oLc2z5FLcQfBWAEzp+zFSiokpDZyioQorEXmI3sBC825vu7OOh6+u3F+swOmj3r8XjlJHKa+hceYo1F9tIeN4J7ZL71LXehWLrwJgSt6PkVRMLNoDJyKSLHoDC1e3t6bBhQP4z989nzppcU7wLEsD9j6q4blVltQPuObvgbdrTjAB7bqp2BAFb0k5y4GBCxlUu46Vfy5//C0Llft+jKQlpkD0B05EJFn0BhaAzG5vhV3jR9df+L+/NfyaC+JH6foDnzEZSB0N2ZU023VVfm7PAypIL36e3C51czLQLl3ZubySUABM8vsxAouJRXvgRESSRXdgIavbW+HEw58/vbAqQo1kXEoIRmcl06ElQPZq4OJrpQ9rNP8DP/BJ5/UKvOOFm58aw06uIlp9H1G2v9Qu9aY9Sv9+Vtm5mpNaAEzK+zGSi4lFc+AkkbXeipJ9JVizZw1K9pXAWq+glgxRhJMVWCxcuBCCIHg8Lr30Uq3apg5/hcNU4XB2w0tew6+Btl2d5dG7TwQyZwLZq84vdVXwB95982sj/ebn+rQKhXM0mhbR6vOAdl3qWvQoyS0AJqWQXaQWE4v2wMkPi9WCKaVTkLYiDZNfn4xZb83C5NcnI21FGqaUToHFGiD3DUnG4C38yb7T9u/fH++9996FA5giYMWqq3BY5mzgg5sA8Zy6xz/zfWgzbZ6p9qxl4voD769GStPMm83/wLtufl8VO4d6mtd76FbgDEaa7ueqGeKQuGS110znShRvRbTkLAmV2qXetEcpmOBPMAKDnwcEQXkBsECF7CKZkvdOhCs/VI68kjzY7DbYmr1nbQ4bSg+UouxgGcomlSE3MwIDxjBhsVqwZNsSlB4ohc1x4TqbDCbk981H4fBCZKVEz/sqksmOCkwmE1JTU7Voi/a+eQGa9CicORri9N32lvkAgv0DL/fm1zSYcZxztskrAzD4L8Ald/t+OZILm8noUlejR8l13XqrlBY83IuJKRXNgVMzFqsFeSV5aLA1QPTx3rI5bLA77MgryUPlzEre/BQIVfDWtDCYNwsWLMDChQtVO58cgiBg48aNyMvL87lNVVUVFi1ahA8++AA//PADunTpgttvvx2PP/44YmNjNWub7MDim2++QZcuXdCmTRtcc801KC4uRrduvlcFNDQ0oKGhwf11bW2I1q2rneLbRfVVEQp5m7yoxh94OTc/f8EMDEDarc4qo4E+rQbb49KcKj/76J0XoJloDZyaWLJtCWx2m8+gwkWECJvDhuLtxVg3wUctGfIqlMHb8ePH3f8vKSnB/PnzcfDgQfdzcquDNjY2anpDb+7rr7+Gw+HACy+8gMzMTOzbtw+zZs3C6dOnsWzZMs3OK2uOxVVXXYWXX34Z77zzDp5//nkcOXIEw4cPR12d7+Q4xcXFSEpKcj/S09WciX/eL1ZnTolDa5z//uJlzE2LFN+qr4oIgr/Ji64/8JkzpSWRCoYrmBlffWEy6dAS4LbjwIgN0rvA1ZyLEOzPPkrnBVBwrPVWZ7e8xPeWzWHD+v3rUXOaaczlUBK8qSU1NdX9SEpKgiAI7q9Pnz6NqVOnIiUlBfHx8RgyZIjHNAHAWdp80aJFmDZtGhITEzF79mwAwOrVq5Geno527dph/PjxWL58Odq3b++x7xtvvIGBAweiTZs26NmzJ4qKimCz2dzHBYDx48dDEAT3182NHj0aa9euxU033YSePXti3LhxmDNnDjZs2KDaNfJGVo/FmDFj3P+//PLLcdVVV6F79+547bXX8Jvf/MbrPnPnzsXDDz/s/rq2tla94OKkxdnF3fzTqGByjtNfVnjhRqB6iu8mn2DNyc7/BxW4GJzH9DmE4K8pYZgPQI1Pq2p1qQfzs4/SeQEUvIqqCo+xfilsDhsqqiowsX909+SoRWnwtnL0SiTHaTvsVl9fj5tvvhmLFy+G2WzG3//+d4wdOxYHDx706MVftmwZ5s+fjwULFgAAduzYgbvvvhtLly7FuHHj8N5772HevHkex962bRumTZuGZ599FsOHD8fhw4fdQcmCBQuwe/duJCcnY+3atRg9ejSMRukT50+dOoWOHbUtGBnUzMv27dvjkksuwaFDh3xuYzabYTZLWb4o07Fy313lrmJh35c5P2V2yVU3xbe3bvigalCcD1I6XevMIinnGK0hH0CwQYrSn32fB50BRTRfW1KsrlFZwFrbwDTmUoVz8DZgwAAMGDDA/fWiRYuwceNGvPnmm7jvvvvcz48cORK///3v3V8//vjjGDNmDObMmQMAuOSSS7Bz505s2rTJvU1RUREee+wxd8XTnj17YtGiRXj00UexYMECdOrUCYDzHixnzqOrmqqWwyBAkHks6uvrcfjwYXTu3Fmt9kgjJWdE07LgJy0Kcy0ILfNB+OqGD6oGhehcqfLjLmDkFuDKpyS2leP+kijNCsmggvxIiFUWsCaamcZcqnAO3urr6zFnzhz07dsX7du3R3x8PA4cOICjRz1LOQwePNjj64MHDyI7O9vjueZff/HFF/jjH/+I+Ph492PWrFk4fvw4zpw5o6i91dXVGD16NAoKCjBrlkoT0H2Q9dd2zpw5GDt2LLp3745jx45hwYIFMBqNmDJlilbt807yDP/zZcG/KnZ2qStZxqhkVYSvCYdS2nroRWdbk7LUm7zY2smtatoaeoEoaDkZOTAZTLI+UZsMJuRk5GjXqCgTzsHbnDlzsGXLFixbtgyZmZlo27Yt8vPz0djY6LFdXFyc7GPX19ejqKgIt912W4vvtWnTRvbxjh07huuvvx7XXnstXnzxRdn7yyWrx+L777/HlClT0KdPH0ycOBEXXXQRdu3a5e6W0YXcGf6izblC4WyNssyAciY+BpuMq2lbozmRUigwKySpLCU+Bfl982GS+LtuMphQ0K9A87H/aOIK3uTQK3jbsWMHZsyYgfHjxyMrKwupqamoqqoKuF+fPn2we/duj+eafz1w4EAcPHgQmZmZLR4Gg/O2HRMTA7s98Jy86upq5OTkYNCgQVi7dq17fy3J+om9+uqrWrVDOiUz/F1FqrpPVHcZozdNJxx+tQQ4+IzytraifACaU3sJKxGAwuGFKDtYBrvN7nfVggABJoMJc4cxYJXDFbyV7pc2gVPP4K13797YsGEDxo4dC0EQMG/ePDgcgasy33///RgxYgSWL1+OsWPH4oMPPsDmzZs9cmbMnz8ft9xyC7p164b8/HwYDAZ88cUX2LdvH5544gkAzpUh77//PoYOHQqz2YwOHTq0OJcrqOjevTuWLVuGEydOuL+nZT6qyKsVonSGvyvPg149AW2SgaTLgmtr02PptVw0mrEXiFSWlZKFskllMJvMPnsuTAYTzCYzyiaVMTmWAoXDC2EymiAE6G3UO3hbvnw5OnTogGuvvRZjx45Fbm4uBg4cGHC/oUOHYtWqVVi+fDkGDBiAd955Bw899JDHEEdubi42bdqEd999F0OGDMHVV1+NFStWoHv37u5tnn76aWzZsgXp6em48sorvZ5ry5YtOHToEN5//32kpaWhc+fO7oeWBFEUdS1uUVtbi6SkJJw6dQqJiQrGwb4tAXZMlr/f0JKWKwvO1mjbE6BmW0ldWv/sqVWxWC0o3l6M9fvXt0g3XdCvAHOHzW3VQcXZs2dx5MgR9OjRQ9EcAX+ZNwHndTYZTBGbNn3WrFn4+uuvsW3btlA3xe/PSur9OwIKfTTjmuEvd0mmtzwPWmcGVLOtpK5WkBWS9JOVkoV1E9Zh5eiVqKiqQG1DLRLNicjJyOGcChXkZuaicmZl1ARvy5Ytw6hRoxAXF4fNmzfjlVdewV//+tdQN0s1kRdYRNIM/0hqKxEFLTkumcmvNBJNwVtlZSWefPJJ1NXVoWfPnnj22Wcxc+bMUDdLNZEXWADaFKnSSiS1lYgozEVD8Pbaa6+FugmairzJm8CFGf4Gs+9lneFS3yGS2kpERBSkyAwsgMia4R9JbSUi2az1VpTsK8GaPWtQsq8E1novhRAJOq8VIAXU+BlF5lCISyTleYikthKRJBarBUu2LXEWymo2oTC/bz4KhxdGzIRCLcXExAAAzpw5g7Zt24a4NeSPK2W462emROQtNyUiCgPRvgRSbcePH8fJkyeRnJyMdu3aeSSEotATRRFnzpxBTU0N2rdv7zXXRfQuNyUiCjGL1YK8kjw02Bp8Zty0OWywO+zIK8lD5czKVt9z4cr0WFNTE+KWkD9yK6Z6w8CCiEimJduWwGa3+U3jDQAiRNgcNhRvL8a6Cet0al14EgQBnTt3RnJyMs6dOxfq5pAXMTExMBqNgTcMgEMhREQyWOutSFuRJruqafXD1RGXb4GoKan378hdFUJEFAIVVRWyggrAOSxSUVWhTYOIwgwDCyIiGeoalRVCrG2oDbwRURRgYEFEJENCbIKi/RLNHPql1oGBBRGRDDkZOTAZ5M17NxlMyMnI0aZBRGGGgQURkQwp8SnI75sPk68U/c24qm9y4ia1FgwsiIhkKhxeCJPRBAH+kzwJEGAymDB3GIsLUuvBwIKISKaslCyUTSqD2WT22XNhMphgNplRNqms1SfHotaFgQURkQK5mbn4x/h/oEtCF6/f7xLfBf8Y/w+m86ZWh5k3iYgUKD9Ujts33g6b3XtOi2P1x3D7xtsRHxvP4IJaFfZYEBHJ1LRWiLcCZIAzKVaDrQF5JXmwWC06t5AodBhYEBHJpKRWCFFrwcCCiEgGa70VpQdKffZUNGdz2LB+/3rUnGZVT2odGFgQEcmgtFbI37/4u0YtIgovDCyIiGRQWitk7vtzUX6oXOXWEIUfBhZERDIorRVid9g5kZNaBQYWREQyKKkVAnAiJ7UeDCyIiGSQWyukKU7kpNaAgQURkUyuWiFK2Bw2VFRVqNsgojDCwIKISCZXrRAlQyIAUNtQq3KLiMIHAwsiIgVyM3OxZOQSRfsmmhNVbg1R+GBgQUSk0LQB02T3WpgMJuRk5GjTIKIwwMCCiEghuRM5TQYTCvoVIDkuWeOWEYUOAwsioiC4JnIKEPxuJ0CAyWDC3GFzdWoZUWgwsCAiCoJrIqfZZPbZc2EymGA2mVE2qQxZKVk6t5BIXwwsiIiClJuZi8qZlSjoX9BizoVr+KNyZiVyM3ND1EIi/QiiKPqv+6uy2tpaJCUl4dSpU0hM5MxoIoouNadrUFFVgdqGWiSaE5GTkcM5FRQVpN6/lS3CJiIir5LjkjGx/8RQN4MoZDgUQkRERKphYEFERESqYWBBREREquEcCyKKCNZ6KyqqKlDXWIeE2ATkZOQgJT4l1M0iomYYWBBRWLNYLViybQlKD5TC5rC5nzcZTMjvm4/C4YXMDUEURjgUQkRhq/xQObLXZKN0v2dQATjLj5ceKEX2mmyUHyoPUQuJqDkGFkQUlixWC/JK8tBga4BNtHndxuawocHWgLySPFisFp1bSETeMLAgorC0ZNsS2Ow2iPCfw0+ECJvDhuLtxTq1jIj8YeZNIgo71nor0laktRj+8MdkMKH64WrZWS45KZRIGmbeJKKIVVFVISuoAJzDIhVVFZKzXnJSKJE2OBRCRGGnrrFO0X61DbWStuOkUCLtMLAgorCTEJugaL9Ec+DhVU4KJdIWAwsiCjs5GTktyo8HYjKYkJORE3A7Tgol0hYDCyIKOynxKcjvmw+TIC24MBlMKOhXEHDiprXe6pxT4aOnojmbw4b1+9ej5nSNpO2JiIEFEYWpwuGFMBlNECD43U6AAJPBhLnD5gY8ZjCTQon0ZQVQAmDN+X+tQW6nHwYWRBSWslKyUDapDGaT2WfPhclggtlkRtmkMkkrOLSeFEoUPAuAKQDSAEwGMOv8v2nnn7fI3E5/DCyIKGzlZuaicmYlCvoXtJhz4Rr+qJxZidzMXEnH03JSKFHwygFkAygF0LxnzXb++WwAiyVuF5pVTUyQRUSaUisBVc3pGlRUVaC2oRaJ5kTkZOQoSoalV+ItInkscAYDDYDficVCgO83ZQawG4A6+ViYIIuIQkrtBFTJccl+k19JCWBck0JL90ubwCl1UihR8JbA2dsQKGiQ0xfQAOAWAJugVnAhRVA9Fn/6058wd+5cPPDAA1i5cqWkfdhjQRT9yg+VI68kDza7zesN3GQwwWQwoWxSmeRhDF/kBjAWqwXZa7LRYGvwu+RUgACzyYzKmZXMwEkqsgKoAFAHIAFAzvnn09ByWEMtZgBvAAjud03q/VvxHIvdu3fjhRdewOWXX670EEQUhfRMQKUkg6YWk0KJAvM32fI2aBdUAEAjgDzoNaFTUWBRX1+PqVOnYvXq1ejQoYPabSKiCKZXAqpgAhi1J4VSKIXfcsuWAk3K3KXx+cXz59En2ZuioZDp06ejY8eOWLFiBXJycnDFFVf4HAppaGhAQ0OD++va2lqkp6dzKIQoCuk5OXJK6RTZcyXWTVjX4ntqTAqlULDAOS+h+c3aBCAfQCH0nFfgm9RJmXowAagGoOz9rdlQyKuvvoo9e/aguFha5FNcXIykpCT3Iz09Xe4piShC6JWASs0Mmq5JoTMHzsTE/hMZVESEQD0ArwEYBOBVndvljdRJmXqwwTm/Q1uyAovvvvsODzzwAP75z3+iTZs2kvaZO3cuTp065X589913ihpKROFPrwRUzKDZmlUAGAvgLHzPS3AAOAfnnIYx8D+3QMuhFCu8Bz+hpH2yN1nLTT/77DPU1NRg4MCB7ufsdju2bt2KP//5z2hoaIDRaPTYx2w2w2w2q9NaIgpreiWgUhrA7D+xX9F+FA4sAOYDKJO5XzmcwUgZPFdF6DGUUgH1gwo5eSy80X4KgqweixtuuAEWiwV79+51PwYPHoypU6di7969LYIKImpdtKxK2pTSAOa9/7ynaD/Sg7+eg1UAroD8oAJw3oQb4LkqQmqGy2AzVyoLgH3fmk1wLh194vy/cplwYXmrdmT9BUhISMBll13m8VxcXBwuuuiiFs8TUeujVwKqnIwcGAUj7KJd1n67vt+FmtM1nEcRVgL1HGTCeSMNRtNVEXPhDDL8Taa0AbCf364SynsulAXAwDUAPkHL61EAZ/uzAIyDM/nVUYnHdO2v/XuftUKISFVaVCVtLiU+BVd1vUr2fnbRznkWYSVQz8F6BB9UND/efEjPcBnsEs0cyL/NmgBsgHP1RgmA1ef/rQawDheCnCw4M2qagQC/a87vm+AMSrQXdErviooKFZpBRNHClYBKauZNpQmoRvUahZ3f75S9HyuVhgsLAvccyOuRCswG4E04J3dK3X49gJVQ9kl/u4xzAc4gpGmvgu8U9k5ZcGbUzINzsqq362U6/yiDXstv2WNBRKrTIwFV34v7KtqPlUrDRaiWYcq50QPONi5By9UiUlaTPKygbXJ7FbrA2TPi7XUJAEbBOZyjX7I3VjclIk1plYCKlUojmRXa1sbQgmvORz6cQzfNh28McM57+COcPQP7oKyH4CkAvwYgpQJwOZy9FTZ4v5ZGADFouSJGGan3bwYWRBSx1Mq+SXqywtkD8GyoG6KAEc7hBn9LPgUAf4JzToTS12iAM8FXAYBp8B5kyCmzbkZwk1CdGFgQUdRjpdJw17SS538B7IRzwmEk9VQo1RHAzyocxwDnXIvmeTWmQHryLdeKkOCCagYWRNQq6FminaTytYSUlGk+pKFkKCm4OiGADmXTiYjCASuVhht/S0hJGTs8k3xVQP611adOCMAeCyKKIqxUGmrhVMkzGrmGNEYCmKVg/9UAZio+u9T7d9B5LIiIwoWrUimFSjhV8oxGrrwaOQr31+fDPAMLIiLyoenkywQ4b2i+lkGGYyXPaOS6vibIn2ORo3prfJ2JiIioCSWVP/8OBhV6MeBCPg05q0L0GRbk5E0iImpCSeXPcuhVh4IA55BGIZwBQ3jVCQEYWBARkVvT+h2+Pgnb4LlCwQJnxkm163qQd64hjSw4l5+a4XvwwVVmvQx61QkBGFgQEZGb1MmXTSt/LoGzAJYaAn36jkRGqPe6mg9p5MKZUbMALYML17b61gkBuNyUiIgAKEu6ZIQzyJBb2Msbf2myI5UrnXYOgPcQ3ByUQKm5a+CcaFsL51BJDtSeU8HlpkRECljrraioqkBdYx0SYhOQk5GDlHgpBaEiXQXk3/jUGv5w1eCIJk3LlXeBc16KHcqCJymlz5MRuMy6PhhYEFFUkxooWKwWLNm2BKUHSj0qppoMJuT3zUfh8MIorzNSF6LzmuC88VYjeoIL1zDEXFwIBMrgvxJp0+GSpsGHt2OFNwYWRBSV5AQK/uqN2Bw2lB4oRdnBsiivN5Kg8/lmA7gBQD8AVyJ6gorZABah5TCEaz5EMZxJrpov43UFDynQekhDa5xjQURRR05hsi4JXVghFYCyORZKGQA8f/5fCyKzhLovJQg8JKH9fAgtsLopEbVKckup53TPwXv/ec9rANKcq6jZugnBlZ8OX3JKcQcjGidqAmpUEA1nDCyIKCzoPRlySukUlO4vlRQoGAUjHKLDbwDSnMlgQvXD1SEsbiYnzbZcLCKmnGs4I1qDTq4KIaIQC8VkSGu91Xk+CUEFANhF+eP6NocNFVUVISh2piTNtlyupEt58D3JkLzTN7tlOGOCLCJSXfmhcmSvyXb2HDi8T4bMXpON8kPlPo6gTEVVRYvzaaG2oVbzc3hSkmZbqaZJl3iLCMwAoA30zm4ZzviuISJVWawW5JXkocHW4LPnwOawocHWgLySPFisFtXOXdeoz5LJRLOew7hK0mwHYoVzkuGa8/9am30/C84u/adkt7Z1MQKYhFBktwxnDCyISFVLti2BzW4LOG9BhAibw4bi7cWqnTshVvslkyaDCTkZOZqfx8kK4G4AjZCWZrsRwEx4DxYAZ9AxBc7VH5MBzDr/b9r55y3Ntn09iLZHMwHAiwCOwRmAsaeiKQYWRKQauXMcbA4b1u9fj5rTNaqcPycjByaDvKljAgQYBaOkbV2rQrSfuOkKALoC2AnpKbMdcH569hYsyBlOcW27S/EriG4igCRE6+qPYDGwICLVKJnj4JoMqYaU+BTk982HSZAWXJgMJozOHI0YYwyEAIWiBAgwGUyYO0zrCXpNA4BgkkY1DRZWQfpwyjgAtwA4C3VqgEQrvefZRA4GFkSkGqVzHNScDFk4vBAmo0lyoLD0xqUom1QGs8nsMyAxGUwwm8wom1SmcXIsKfMp5HAFC/fCWYFU6nAKV4MExnQJvjCwICLVKJ3joOZkyKyULNmBQm5mLipnVqKgf0GLoRTX8EflzErZ6byt9VaU7CvBmj1rULKvBNZ6b/MempJatlwOV/XRaEmZrYTatzoTnPlDyBsmyCIi1VjrrUhbkSZrOESrhFMWqwXF24uxfv/6Fnk0CvoVYO6wuV57H2pO16CiqgK1DbVINCciJyNHdtuU5fDQM6V2uFM7M+dQAJ9AnWsb/YmwfGHmTSIKCTmZL/VIka1GoCCHnDolnj0gJXBOuoxUJgCjzv9/C+TfxF2lwf8XwBNQL0GXK802cKE+x244V3XIJQAwwzlBtvWtBGFgQUQhIbdWRzQV9Qruta+Bc/lnJHoQzqyTroCtBMCv4Rx+kTIBtHlpcAt8VwGVU2LdV++C0iAuBsBbaK05K6TevznHgohUpWSOQ7QILoeH3mXL1WKAZ1BhATADzoDAX1AhwHnjfwrOQKFpPghXgq5qOIOA1ef/rQawCc4bvP/JuReO720VTw7kV7QQALyL1hpUyMHAgohUp9VkyHAWfA6PHERm+aZr4JnPQeoEVNf398B3PohkOEuQzzz/bzIu1DMxw/f1Mp3/fhm8D1mkwFlfRer1NsLZw5EjcfvWjUMhRKQpteY46F0lVa6SfSWY/Lr87vWS/JImBc30Kluupg9x4YarZAKq0lLj/oZLmg6r+NtfSiXX1j2voilWNyWisJAclxxUJdBQVEmVy2K14OmPn1a0r2cOj0I4P2XbERlly3Pg+Sm+AvKDItv5/eS+R1zDJStxYVJm4vn2SAlSpFRydU0oLUNrDyrk4FAIEYWtUFVJlcPVxs+Of6Zof88cHlK6+dX4s938GAICz1loLhbAs82eU1oELpgEad6GS6RqWsm1+fV29XywwJhcDCyIKCyFskqqVE3b6BDlp7/2XtAs0M3uaiVNbeYpeE6K/ADOYEZqcBEL4E20/BSvdAJqKIfF/U0UZYExJTgUQkRhSckKCy3zYXgjtY3e+C9o5q+bX0TwibTS0HLooQz+hwVccuDsqfB2w82B87Yid45FjoztteLq+aBgsceCiMJOqKukSiG3jU1JL2jmrZtf7oqG5nzdyP31lBjgzF754fmHr0/xctvmGm5gldBowsCCiMJOqKukSqGkjQBgEAwq5PAohPOmLHdeRKAbua9hgeMAtkNaz4LUtvnLM0GRjEMhRBR2wqFKaiBK2zi482CsGbcmyJUsTVc0nIO0LJRybuTBDAtwtUVrxx4LIgo74VAlNRClbfz9tb9XaXmsa+hiIgL/KQ+UMEptXG3RmjGwIKKwk5OR0yJjZyDeV1hoJzza6Bq6OA7nSo9stPyzHqobOVdbtFYMLIgo7KTEpyC/b77PWiPN+V9hoY3wamMygDlwlgY/jvC6kQeTZ4IiEQMLIgpLhcMLYTKaIASYBCh9hYX6wrONvJFTaDGwIKKwFAlVUiOhjUR6Y2BBRLqx1ltRsq8Ea/asQcm+EljrrX63j4QqqZHQRiI9sbopEWlOjUJikVAlVa02EoUjqfdvBhZEpKnyQ+XIK8mDzW7zmqXSZDDBZDChbFKZpp/qK45U4PEPHseu6l0edT3CqUoqUThjYEFEIWexWpC9JhsNtga/9TQECDCbzKicWan6zd1iteD+zffjo28/8rmNXsENUSSTev/mHAsi0oySQmJqKj9UjsGrB/sNKoDQV0kliibssSAiTVjrrUhbkSarnobJYEL1w9WqzJ1IiUvBmHVjcNZ2Vtb5C/oV6F4llSgSSL1/s1YIEWkimEJiE/tLr1Pha2KoAEF2OXNXldSVo1dy0iWRQhwKISJN6FFIrPxQObLXZKN0f2mLIEZuUOGid5VUomjDHgsi0oTWhcQsVgvySvICTgxVQs8qqUTRhj0WRKQJrYt0SZ0YqoSeVVKJog0DCyLShJZFuqz1VuecCi95MYKld5VUomjDwIKINKNVkS4lE0OlMApG3aukEkUbBhZEpBmtinQpnRgaSIwxJiRVUomiiazA4vnnn8fll1+OxMREJCYm4pprrsHmzZu1ahsRRQEtinQpnRjqT6wxNioqkMot9EakNlkzq9LS0vCnP/0JvXv3hiiKeOWVV3Drrbfi888/R//+/bVqIxFFuKyULKybsA4rR69UpUiXa2KoWsMhAgSMzBiJLgldVDleKKhR6I1IDUFn3uzYsSOeeuop/OY3v5G0PTNvErVOalcVnVI6xZm/QsIETinJsiK5Xki4FHqj6KZ55k273Y7169fj9OnTuOaaa3xu19DQgIaGBo+GEVHrodUn6cLhhSg7WAa7zR6wwFmMMQYQgXOOcz63tTlssDvsyCvJ06QYmlak5POI1NdGkUn25E2LxYL4+HiYzWbcfffd2LhxI/r16+dz++LiYiQlJbkf6enpQTWYiCKHv8yYNocNpQdKkb0mG+WHymUfW87E0GvSroFDdISsGJqWQl3ojag52UMhjY2NOHr0KE6dOoXS0lKsWbMGH330kc/gwluPRXp6OodCiKKcXiXTLVYLircXY/3+9S16RAr6FWD2wNkY9Y9RuhVD05Pehd6oddOsbHpsbCwyMzMxaNAgFBcXY8CAAXjmmWd8bm82m92rSFwPIop+en2Sdk0MrX64GiX5JVg9djVK8ktQ/XA11k1YB+tpq+JiaOEumEJvRFoJulaIw+Hw6JEgIpKbGVONqqLJccleq6LqUQwtVNR4bWpPqiWSFVjMnTsXY8aMQbdu3VBXV4d169ahoqIC5eXyx0eJKHrpVTJdCq2LoYVSMK+Ny1NJK7ICi5qaGkybNg3Hjx9HUlISLr/8cpSXl2PUqFFatY+IIlA49RIoyXkR7vVCXL0M1XXVMAgGOESH5H1NBhNEUUT2mmyvy1Ndk2rLDpZxeSopIiuweOmll7RqBxFFkXDqJXAVQ5Oa80JOMTS9+eplkMpkMGFUz1GY8cYMLk8lzbBWCBGpTuuS6S5S01drVQxNT/6W7krhem2iKHJ5KmmKgQURqU7LkumA85P7lNIpSFuRhsmvT8ast2Zh8uuTkbYiDVNKp8BitXhsr1UxNL00TYKlpFS867WtHbcW7x15T/ak2prTNbLPSa0XAwsi0oRWvQRKk25pUQxNL1KX7nrT9LUJgsDlqaS5oJebEhF54+olkFrDIislK+DSx2DTV6tdDE0PcpfuAoBBMOCpUU8hLTHN47V9Uv2JojZEwtJbCh8MLIhIM65eAn+ZMV09FVNKpwRc+qgk6da6CetafN9XzotwpGTprkN0IC0xrcVrDKdJtRS9GFgQkaYC9RL4q8zZdOnj2nFrdU+6pSdfvTVqLt2NxqW3FH4YWBCRLrz1EsgZ2phWNi1skm6pKVCiqoGdByo6rrdehmhaekvhi4EFEenOWm9F2ddleGrnUwGLlAHOoQ27aFd0Ltcn93BMXS2lt2bj1xsVJcHy1csgp9x8uC69pfDGwIKIdGOxWvDolkdRfrhc9goHOTfWpk7+clLS/A29Se6tgR2CIMAII+wIHFwF6mVQMqmWSA7ZZdODJbXsKhFFl/JD5Rj7r7E45zin2zkNggExhhjYHfaAN1C9l5lOKZ0ieUjCKBghQoQoiqqVoA9Ubn7usLkMKsiD1Ps3Awsi0pzFasHg1YPRaG/U7Zxa3IzVYq23Im1Fmqw5I1oFSTWnayJm6S2FltT7N4dCiEhzS7YtwTm7fj0VAgSIECGIAhzwP4QSaGmqFpQuIX3i+iew54c9qvYyRNLSW4oMDCyISFPWeivW71+vKGukEiaDCUbBiHOOc5LmJAD6L01VuoS0/lx9xCX4otaHgQURaaqiqkLxig65XJ/cr0y9Eo++96isffVcmqo0UdWSbUtwTdo1yM3MZS8DhS3WCiEiTSn9dC7Xg1c9iOqHq7Fuwjp0aNtB0TH0Sl2tpPorAJxznENeSV6LImtE4YSBBRHJJrVcOaD807lc/ZP7u4cDwj11tdzqr02xlDmFOw6FEJFkgbJEessJkZORA6Ng1Hw4pGlQEAmpq6Umqmou0lKVU+vDHgsikkRpufKU+BQU9CsIWD49GM2DArk9AqFIXe1KVKVkSISlzCmcMbAgooCaZon0ldDJ5rChwdbgdQ5A4fBCxBhjJJ9PgACjYJS0ra+goHB4IUxGU8CAJpSpq3Mzc1E4vFDRvixlTuGKgQURBaSkXHlTWSlZeHPym4gxBA4uYgwx+OvNf0WMMSaooMDVI2A2mX32XJgMJphN5pCmru57cV9F+7GUOYUrBhZE5Je13qqoXHnN6RqP53Mzc/HZ7M8wJnOM14BBgIAxmWPw2ezPcPeQu1UJCnIzc1E5sxIF/QtaDDm4ejoqZ1bqns67KSUrRFjKnMIZU3oTkV8l+0ow+fXJ8vfLL/GZa6HmdA3e+PoNfHr8UwDA4M6Dceult7YYzlCznkU4p66WUzfE9dr1yhJK5MJaIUSkijV71mDWW7Nk77d67GrMHDhTlTaEc1CgBovVguw12QFLyIeirgmRC2uFEJEqQpETwlpvRUVVBeoa65AQm4CcjJyozjTJUuYUTRhYEJFfeuaEUJInI1q45oOwlDlFOg6FEFFAeswBKD9ULvkTeygnW+oh2od+KDJxjgURqUbrOQCcY0AU/qTev7nclIgC0jonRLB5MogofDCwICJJtMoJoVaeDCIKD5y8SUSSZaVkYd2EdVg5eqVqcwAqqipkTQwFLtTKiOaVIkSRioEFEcmWHJes2k29rrFO0X6slUEUnjgUQkQhFYo8GUSkHQYWRBRSrJVBFF0YWBBRSKXEpyC/b77P1SbN+SqTTkThgYEFEYVc4fBCmIymoMqkE1F4YGBBRCGndZ4MItIPAwsiCgta5ckgIn0xpTcRhZ1orZXhrWprSnxKqJtFJAnLphORZnzdINW6caqZJyMctOaqrdT6sMeCiCTzdYM0CkZ0TeiK6rpq2EW7+3neOFm1laIHq5sSkaoC3SB9UfPGGY5DCf7axKqtFE04FEJEqrFYLcgryQt4g/TG5rDB7rAjryRP8Y0zHIcSpLRJSdXWdRPWad10Ik2xx4KIAppSOgWl+6VXIPXGtbJD7o0zHIcSpLTJKBhxznEODtEh+bgmgwnVD1dHxURVij5S799cbkpEfskta+6LknLnTXtKfJ3f5rChwdaAvJI8WKyWoNqoapvsDbKCCtd+FVUVKrSSKHQYWBCRX0rKmvsi98apZChBa1LbpBSrtlKkY2BBRH4pLWvui9Qbp9yeEiU9InKp1XvjD6u2UqRjYEFEfikta+6L1Bunkp4SrYcS1Oy98YZVWykacFUIEfnlKmuuxg010I2z6dLN3dW7FZ0j2KEEa70VZV+XYc/xPQCAgZ0HIu/SPKTEp6jee9MUq7ZStGBgQUR+ucqaq7UqxNuN09fSTSWUDiVYrBY8uuVRlB8ubzF/4rdv/xa5vXIxssdIRccWIATMY8GqrRQtuNyUiAKSmujJF38JoJQm3vJG6XLN8kPlGPuvsTjnOOd3uxhDDOyiXdZqD4NgcO7nsIfNclkiJbjclIhUI6WsuS/+yp1LWbop5zxKhhIsVgvGvTouYFABAOcc5yCKIowwSm7TpP6TsHvWblZtpVaDPRZEJJnFakHx9mKs37++Za2QxK6orm1ZK6SgXwHmDpvrNTOmGom3gOBSYk8pnYKSr0pk9cQYBANEUZSdpjtaq7ZS68BaIUSkGV83SDk3Tmu9FWkr0oKeUxHMUIK13oquy7t6BENSCBAQa4zl8Aa1KqwVQkSa8VXWXE65czWWbgbqEZHSBrlBBeBMyPXE9U9gzw97WvTeBNsmrYRjATeKTgwsiCgklC7dnD1oNoZ0GaLKUEIwy0fbt22PdRPWYeXolWE9vBGOBdwoujGwIKKQUJp464YeN0juFdGqDcCFZa1yemn05m/Fjc1hQ+mBUpQdLOOQDamKq0KIKCRcibfkUDszZU5GDoyCtBUeTQkQcPLsSVjrraq1RW3hWMCNWgcGFkQUEq7EW1KXr2qRmTIlPgUF/QogQJC1nwgRd226C2kr0jCldEpY3pTDsYAbtQ5cFUJEISM18VYwy0mltGHw6sFotDcq2l/vFSBSJmEqWXGjNLkYtR6aJMgqLi7GkCFDkJCQgOTkZOTl5eHgwYNBN5aIWicpibf8JdhSqw1vTn4TMYYYRfvrNZxgsVowpXQK0lakYfLrkzHrrVmY/Ppkr70m4VjAjVoPWYHFRx99hHvvvRe7du3Cli1bcO7cOdx00004ffq0Vu0joiiXm5mLypmVIc1MmZuZi89mf4YxmWNkD4sA2g8nlB8qR/aabGcyMYf3SZjZa7JRfqgcgPLVLsEWcCMCghwKOXHiBJKTk/HRRx9hxIgRkvbhUAgR+RIOmSlrTtfgja/fwNajW/HPL/8pKyOnFsMJSoaL9p/Yj8mvT5Z9rpL8krBd4UKhp0uCrFOnTgEAOnbs6HObhoYGNDQ0eDSMiMibcFi6mRyXjFmDZiHRnIh/fPkPWfu6hhPUfA1KJmGuyF0hu9S92ituqPVSvCrE4XDgwQcfxNChQ3HZZZf53K64uBhJSUnuR3p6utJTEhHpJhyGE6z1VmdiK4m1VGwOG9bvXw9BEEK+4oZaL8WBxb333ot9+/bh1Vdf9bvd3LlzcerUKffju+++U3pKIiLdKE2e5UqcpYZgJmEWDi+EyWgKOGdEgACTwYS5w+YG01QiN0WBxX333YdNmzbhww8/RFpamt9tzWYzEhMTPR5EROGuf6f+uu7nTTC9JuGw4oZaJ1mBhSiKuO+++7Bx40Z88MEH6NGjh1btIiIKqa9OfKXrft4E22sSDituqPWRNXnz3nvvxbp16/DGG28gISEBP/zwAwAgKSkJbdu21aSBREShEA5zLFxpz4OZhJmVkhURxdIoesgKLJ5//nkAQE5Ojsfza9euxYwZM9RqExFRyIXDHAtX2vPS/dImcPqbhBkOK26odZAVWOic/ZuIKGTU6C1QQ+HwQpQdLIPdZg+Yx4KTMCkcsAgZEZEX4VAkDQiPtOdEcjCwICLyoXB4IQyGwH8mte4t4CRMiiRBZd4kItKClAqeejhWd0zSELCruqmWvQWchEmRgoEFEYUNi9WCJduWOLNNNpnbYDKYkN83H4XDC3Xr6rdYLcgryZM0x0IQBHRJ6KJDqzgJk8Ifh0KIKCzIreCpNak1OgDAITo0q2xKFGkYWBBRyFmsFtz66q1osDX4XFZpc9jQYGtAXkkeLFaLpu1RWqOj5nSNpu0iigQMLIgopCxWC25Zdwsa7P7LggOeFTy1FEyNDqLWjoEFEYVM+aFyDFk9BEdrj0reR4/egXDIukkUqRhYEFFIuCZHNtgbZO+rde9AOGTdJIpUDCyIKCRckyOV0rJ3wJV1Uw4tsm4SRSIGFkSkO7mTI73RsncgXLJuEkUiBhZEpDslkyOb0qN3oHB4IUxGEwQIfrdjjQ4iTwwsiEh3SidHAvr1DrBGB5EyDCyISHdKJ0fq3TvQtEaHUTB6fM8oGFmjg8gLBhZEpDslkyMBINYYG5LeAV/1QqTUESFqbRhYEJHu5E6OBIBuSd2we9ZuXXsHmqYZt4t2j+/ZRbvuacaJIoEg6hxy19bWIikpCadOnUJiItd8E7UWzSuWpsSlYMy6MWiwBc64aTaasXvWbl17KixWC7LXZAdsnwABZpMZlTMrOc+CoprU+zermxKRpvxVLB2aPhQff/8xHA6H16WnJoNJl5Lk3kgtQtY0zfi6Cet0ah1R+OJQCBFpJlDF0h3f7QAADOs2rMWcC9fqj1BMjmQRMiLl2GNBRJpwp+z2M5Rgc9ggQMCu6l3Y8ustqDldg9qGWiSaE5GTkROyhFPBFCGb2H+iRq0iigwMLIhIE3KHEl787MWwGUpgETIi5TgUQkSqi/ShBBYhI1KOPRZEpLpIGkpovlolJyPHnWdDzmtgETIiJwYWRKQ6vYcSvAUHKfEpfvfxt1olv28+buxxI977z3uSel1YhIzoAgYWRKQ6vYYSAgUHhcMLvS5TLT9UjrySPNjsthaBg81hQ+mBUhgEAwRBgCAKAfNYsAgZ0QWcY0FEqlOSslvuUEKgpay+smI2Xa3iqzfC5rDhnP0cACDGGMMiZEQyMLAgItXJTdktdyhBanDQYGtAXkkeLFaL+3k5q1VEiLihxw0o6F8QVnk2iMIZU3oTkSa0TIk9pXSKs6dCxvyHdRPWwVpvRdqKNNmTMqsfrgbgnJQaDnk2iEJB6v2bPRZEpImslCyUTSqD2WRWdSghmKWswaxWSY5LxsT+EzFz4ExM7D+RQQWRDwwsiEgzuZm5qJxZqepQQjDBARNfEWmPq0KISFNZKVlYN2EdVo5eqcpQQjDBQTCrVZQsaW0q2P2JIgUDCyLShWsoIVjBBAfXdb9OduIro2DEvyz/wtQNU2UtaXVRuiSWKFJxKISIIkowS1nlrlYxCkaIELHp35tkLWl1UbokliiSMbAgoogS7FLWwuGFMBlNECD43U+AALtoh0N0yF7SCgS3JJYokjGwIKKIUzi8EAZD4D9f3rJiSl2tIggCDBL+RLqqsxZvL/Z4Xm511+b7E0UqBhZEFHGO1R2DlBQ8JoPJ61LWQKtVxl4yFgIEOOCQ1J7m1VkjvborUTAYWBBRRHENMUiZgCkIArokdPH6PddqleqHq1GSX4LVY1ejJL8E1Q9XY1L/SbCLdlntci1pBYJbEksU6bgqhIgiitQhBgBwiA4Uby/GugnrfG7jbbVKsPkumC+DWjP2WBBRxNBriCHY6qx6VXclCkcMLIgoYug1xBBsdVY9qrsShSsGFkQUMfQaYgh2SavW1V2JwhkDCyKKGHoOMcjJd9F8Sasa+xNFKgYWRBQx9BxiCLY6q1bVXYnCHQMLIooYeg8xBFudVYvqrkThThClZJlRUW1tLZKSknDq1CkkJnIGNBHJY7FakL0mGw22Br9LTgUIMJvMqJxZqUpvQM3pmqCqswa7P1GoSb1/M7AgoohTfqjcmSTLbvO69NQgGGAQDHjkmkfwwNUPsDw5kQqk3r85FEJEEcffEAPgTIxlc9hQvKMYaSvSMKV0Cot8EemEPRZEFNFqTtfgmV3P4MmdT8LhcHit72EymNx1QzifgUgZ9lgQUatgrbdi+a7lsDvsPouGsTw5kX4YWBBRRGN5cqLwwsCCiCIWy5MThR8GFkQUsVienCj8MLAgoojF8uRE4YeBBRFFLJYnJwo/8pLuExGFEVftEDnDIa7aIdZ6KyqqKlDXWIeE2ATkZOQwkRaRChhYEFHEctUOKd0vbQKnyWDCqJ6j8MDmB5yTPpsEJCaDCfl981E4vJAFwYiCwARZRBTR5NQOMRlMEAQBDofDayDCRFpEvjFBFhG1ClLLk8cYYwAA5+znfPZuMJEWUfBkBxZbt27F2LFj0aVLFwiCgLKyMg2aRUQknZTy5CMzRkIURSbSItKY7KGQzZs3Y8eOHRg0aBBuu+02bNy4EXl5eZL351AIEWnJW3lyURSRtiJN9iTP6oerWdqc6Dyp92/ZkzfHjBmDMWPGBNU4IiKtJMclY2L/iR7PlewrUZxIq/mxiMg/zVeFNDQ0oKGhwf11bS0T0xCRvphIi0g/mgcWxcXFKCoq0vo0REQ+RWIiLebZoEileWAxd+5cPPzww+6va2trkZ6ervVpiYjcgkmkpTeL1YIl25YwzwZFLM2Xm5rNZiQmJno8iIj05Eqk5Ws5anOulSR6T9wsP1SO7DXZzoRfzYIgm8OG0gOlyF6TjfJD5bq2i0gO5rEgolahcHghTEYTBAh+t3Ml0po7bK5OLXOyWC3IK8lDg62BeTYooskOLOrr67F3717s3bsXAHDkyBHs3bsXR48eVbttRESqkZpIy2wyo2xSme7DDUu2LYHNbmOeDYp4svNYVFRU4Prrr2/x/PTp0/Hyyy8H3J95LIgolCxWC4q3F2P9/vUt5jAU9CvA3GFzdQ8qrPVW5tmgsCf1/s1aIUTUKnlLpBWqm3TJvhJMfn2y/P3yS5hng3SjWYIsIqJo4C2RVqgwzwZFE07eJCIKsUjMs0HkC3ssiIhUpCSxVSTl2SAKhIEFEZEKgkls5cqzUbq/1OdS06ZClWeDSAoOhRARBUmNxFbhnmeDSCoGFkREQVArsVW459kgkoqBBRFRENRMbJWbmYvKmZUo6F8Ak8EzuHANf1TOrERuZq4qbSfSAvNYEBEppGViq3DKs0EEMI8FEZHmKqoqZAUVgHNYpKKqImAOjXDKs0EkB4dCiIgUYmIropYYWBARKcTEVkQtMbAgIlLIldhKDia2omjHwIKISCFXYitfy0ObY2Irag0YWBARBYGJrYg8MbAgIgoCE1sReWJgQUQUJCa2IrqACbKIiFTExFYUrZggi4goBJjYilo7DoUQERGRahhYEBERkWoYWBAREZFqGFgQERGRahhYEBERkWoYWBAREZFqGFgQERGRanTPY+HKx1VbW6v3qYmIiEgh1307UF5N3QOLuro6AEB6errepyYiIqIg1dXVISkpyef3dU/p7XA4cOzYMSQkJEAQ/FcDDGe1tbVIT0/Hd999x9Tk4PVoitfCE6+HJ16PC3gtPIX79RBFEXV1dejSpQsMBt8zKXTvsTAYDEhLS9P7tJpJTEwMyzdAqPB6XMBr4YnXwxOvxwW8Fp7C+Xr466lw4eRNIiIiUg0DCyIiIlINAwuFzGYzFixYALPZHOqmhAVejwt4LTzxenji9biA18JTtFwP3SdvEhERUfRijwURERGphoEFERERqYaBBREREamGgQURERGphoEFERERqYaBRQBbt27F2LFj0aVLFwiCgLKyMo/vb9iwATfddBMuuugiCIKAvXv3hqSdevB3Lc6dO4c//OEPyMrKQlxcHLp06YJp06bh2LFjoWuwxgK9NxYuXIhLL70UcXFx6NChA2688UZ88sknoWmsDgJdj6buvvtuCIKAlStX6tY+PQW6FjNmzIAgCB6P0aNHh6axOpDy3jhw4ADGjRuHpKQkxMXFYciQITh69Kj+jdVBoOvR/L3hejz11FOhabBMDCwCOH36NAYMGIC//OUvPr8/bNgwLF26VOeW6c/ftThz5gz27NmDefPmYc+ePdiwYQMOHjyIcePGhaCl+gj03rjkkkvw5z//GRaLBdu3b0dGRgZuuukmnDhxQueW6iPQ9XDZuHEjdu3ahS5duujUMv1JuRajR4/G8ePH3Y9//etfOrZQX4Gux+HDhzFs2DBceumlqKiowJdffol58+ahTZs2OrdUH4GuR9P3xfHjx/G3v/0NgiBgwoQJOrdUIZEkAyBu3LjR6/eOHDkiAhA///xzXdsUKv6uhUtlZaUIQPz222/1aVQISbkep06dEgGI7733nj6NCiFf1+P7778Xu3btKu7bt0/s3r27uGLFCt3bpjdv12L69OnirbfeGpL2hJq36zFp0iTx9ttvD02DQkzK345bb71VHDlypD4NUgF7LEgzp06dgiAIaN++faibEnKNjY148cUXkZSUhAEDBoS6OSHhcDjw61//Go888gj69+8f6uaEXEVFBZKTk9GnTx/89re/xU8//RTqJoWEw+HA22+/jUsuuQS5ublITk7GVVdd5XcorTWxWq14++238Zvf/CbUTZGMgQVp4uzZs/jDH/6AKVOmhG2VPj1s2rQJ8fHxaNOmDVasWIEtW7bg4osvDnWzQmLp0qUwmUz43e9+F+qmhNzo0aPx97//He+//z6WLl2Kjz76CGPGjIHdbg9103RXU1OD+vp6/OlPf8Lo0aPx7rvvYvz48bjtttvw0Ucfhbp5IffKK68gISEBt912W6ibIpnuZdMp+p07dw4TJ06EKIp4/vnnQ92ckLr++uuxd+9e/Pjjj1i9ejUmTpyITz75BMnJyaFumq4+++wzPPPMM9izZw8EQQh1c0Ju8uTJ7v9nZWXh8ssvR69evVBRUYEbbrghhC3Tn8PhAADceuuteOihhwAAV1xxBXbu3IlVq1bhuuuuC2XzQu5vf/sbpk6dGlHzTdhjQapyBRXffvsttmzZ0qp7KwAgLi4OmZmZuPrqq/HSSy/BZDLhpZdeCnWzdLdt2zbU1NSgW7duMJlMMJlM+Pbbb/H73/8eGRkZoW5eyPXs2RMXX3wxDh06FOqm6O7iiy+GyWRCv379PJ7v27dv1K4KkWrbtm04ePAgZs6cGeqmyMIeC1KNK6j45ptv8OGHH+Kiiy4KdZPCjsPhQENDQ6ibobtf//rXuPHGGz2ey83Nxa9//WvccccdIWpV+Pj+++/x008/oXPnzqFuiu5iY2MxZMgQHDx40OP5f//73+jevXuIWhUeXnrpJQwaNCji5mUxsAigvr7e41PEkSNHsHfvXnTs2BHdunXDzz//jKNHj7rzNbh+OVJTU5GamhqSNmvF37Xo3Lkz8vPzsWfPHmzatAl2ux0//PADAKBjx46IjY0NVbM14+96XHTRRVi8eDHGjRuHzp0748cff8Rf/vIXVFdXo6CgIISt1k6g35XmgWZMTAxSU1PRp08fvZuqOX/XomPHjigqKsKECROQmpqKw4cP49FHH0VmZiZyc3ND2GrtBHpvPPLII5g0aRJGjBiB66+/Hu+88w7eeustVFRUhK7RGgp0PQCgtrYW69evx9NPPx2qZioX6mUp4e7DDz8UAbR4TJ8+XRRFUVy7dq3X7y9YsCCk7daCv2vhWm7r7fHhhx+Guuma8Hc9fvnlF3H8+PFily5dxNjYWLFz587iuHHjxMrKylA3WzOBfleai+blpv6uxZkzZ8SbbrpJ7NSpkxgTEyN2795dnDVrlvjDDz+EutmakfLeeOmll8TMzEyxTZs24oABA8SysrLQNVhjUq7HCy+8ILZt21Y8efJk6BqqkCCKoqhFwEJEREStDydvEhERkWoYWBAREZFqGFgQERGRahhYEBERkWoYWBAREZFqGFgQERGRahhYEBERkWoYWBAREZFqGFgQERGRahhYEBERkWoYWBAREZFq/j/U2AGL+gSRkQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Hierarchical clustering Accuracy for Seed dataset\n",
        "import sklearn.metrics as sm\n",
        "\n",
        "\n",
        "target = pd.DataFrame(seed.target)\n",
        "#based on the dendrogram we have two clusetes \n",
        "k =3 \n",
        "#build the model\n",
        "HClustering = AgglomerativeClustering(n_clusters=k , affinity=\"euclidean\",linkage=\"ward\")\n",
        "#fit the model on the dataset\n",
        "HClustering.fit(X)\n",
        "#accuracy of the model\n",
        "sm.accuracy_score(target,HClustering.labels_)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5B-FojR49XDD",
        "outputId": "dd7e0da3-a49b-4586-ce81-f1056b84f7e7"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.9/dist-packages/sklearn/cluster/_agglomerative.py:983: FutureWarning: Attribute `affinity` was deprecated in version 1.2 and will be removed in 1.4. Use `metric` instead\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.3761904761904762"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "CxwaUiMD9bG9"
      },
      "execution_count": 39,
      "outputs": []
    }
  ]
}