{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"mount_file_id":"1s8m2lB66czUKMvvLpGGUkni5lzVTjz3d","authorship_tag":"ABX9TyOq5yrxD9fl6zae70hb1FO5"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["# **Studi Kasus Heart Disease 5 Fitur Part 2**"],"metadata":{"id":"s8B30LBor6qp"}},{"cell_type":"markdown","source":["Implementasi dengan Menggunakan Model `Bagging Classifier` , `Random forest` dan `Stacking clasifier` Dengan data tanpa `Normalisasi`."],"metadata":{"id":"9CEPTpErkvJw"}},{"cell_type":"markdown","source":["## Membaca data"],"metadata":{"id":"EAF-vi3ksEd5"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"9xrzLAOwrx-Q","executionInfo":{"status":"ok","timestamp":1669559083659,"user_tz":-420,"elapsed":541,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"3d2b3a56-a2ec-4992-8cae-3432497e37fd"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["   age  sex   BP  cholestrol  heart disease\n","0   70    1  130         322              1\n","1   67    0  115         564              0\n","2   57    1  124         261              1\n","3   64    1  128         263              0\n","4   74    0  120         269              0"],"text/html":["\n","  <div id=\"df-b03ea58f-321f-43e7-adae-9db3121491ce\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>age</th>\n","      <th>sex</th>\n","      <th>BP</th>\n","      <th>cholestrol</th>\n","      <th>heart disease</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>70</td>\n","      <td>1</td>\n","      <td>130</td>\n","      <td>322</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>67</td>\n","      <td>0</td>\n","      <td>115</td>\n","      <td>564</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>57</td>\n","      <td>1</td>\n","      <td>124</td>\n","      <td>261</td>\n","      <td>1</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>64</td>\n","      <td>1</td>\n","      <td>128</td>\n","      <td>263</td>\n","      <td>0</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>74</td>\n","      <td>0</td>\n","      <td>120</td>\n","      <td>269</td>\n","      <td>0</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    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Visit the ' +\n","            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n","            + ' to learn more about interactive tables.';\n","          element.innerHTML = '';\n","          dataTable['output_type'] = 'display_data';\n","          await google.colab.output.renderOutput(dataTable, element);\n","          const docLink = document.createElement('div');\n","          docLink.innerHTML = docLinkHtml;\n","          element.appendChild(docLink);\n","        }\n","      </script>\n","    </div>\n","  </div>\n","  "]},"metadata":{},"execution_count":12}],"source":["from scipy.io import arff\n","import pandas as pd\n","from sklearn.preprocessing import MinMaxScaler\n","import joblib\n","\n","data = pd.read_csv('https://raw.githubusercontent.com/soumya-mishra/Heart-Disease_DT/main/heart_v2.csv')\n","df = data\n","df.head()"]},{"cell_type":"markdown","source":["### Memisahkan Label"],"metadata":{"id":"o1L0ks0tsVYE"}},{"cell_type":"code","source":["y = df['heart disease'].values\n","y[0:5]"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-3wWRpqJsXta","executionInfo":{"status":"ok","timestamp":1669559083994,"user_tz":-420,"elapsed":6,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"0e711dd9-ebe8-4d0b-cdcb-46a4066d634e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([1, 0, 1, 0, 0])"]},"metadata":{},"execution_count":13}]},{"cell_type":"code","source":["X = df.drop(columns=['heart disease'])\n","X"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":424},"id":"Bn2sNGbYsuaJ","executionInfo":{"status":"ok","timestamp":1669559083995,"user_tz":-420,"elapsed":6,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"66cd951b-5b16-41bb-a830-40c611be7ed5"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["     age  sex   BP  cholestrol\n","0     70    1  130         322\n","1     67    0  115         564\n","2     57    1  124         261\n","3     64    1  128         263\n","4     74    0  120         269\n","..   ...  ...  ...         ...\n","265   52    1  172         199\n","266   44    1  120         263\n","267   56    0  140         294\n","268   57    1  140         192\n","269   67    1  160         286\n","\n","[270 rows x 4 columns]"],"text/html":["\n","  <div id=\"df-4c46711a-5ca5-4638-8692-1d51f2096d43\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","       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<tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>265</th>\n","      <td>52</td>\n","      <td>1</td>\n","      <td>172</td>\n","      <td>199</td>\n","    </tr>\n","    <tr>\n","      <th>266</th>\n","      <td>44</td>\n","      <td>1</td>\n","      <td>120</td>\n","      <td>263</td>\n","    </tr>\n","    <tr>\n","      <th>267</th>\n","      <td>56</td>\n","      <td>0</td>\n","      <td>140</td>\n","      <td>294</td>\n","    </tr>\n","    <tr>\n","      <th>268</th>\n","      <td>57</td>\n","      <td>1</td>\n","      <td>140</td>\n","      <td>192</td>\n","    </tr>\n","    <tr>\n","      <th>269</th>\n","      <td>67</td>\n","      <td>1</td>\n","      <td>160</td>\n","      <td>286</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>270 rows × 4 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-4c46711a-5ca5-4638-8692-1d51f2096d43')\"\n","              title=\"Convert this dataframe to an interactive table.\"\n","              style=\"display:none;\">\n","        \n","  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n","  </svg>\n","      </button>\n","      \n","  <style>\n","    .colab-df-container {\n","      display:flex;\n","      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document.querySelector('#df-4c46711a-5ca5-4638-8692-1d51f2096d43 button.colab-df-convert');\n","        buttonEl.style.display =\n","          google.colab.kernel.accessAllowed ? 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scaled_features.head(10)"],"metadata":{"id":"CJG72qsluDIF"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Split Data"],"metadata":{"id":"kGWWCZQ0vDU4"}},{"cell_type":"code","source":["from sklearn.model_selection import train_test_split\n","\n","X_train, X_test, y_train, y_test=train_test_split(X, y, test_size=0.2, random_state=1)\n"],"metadata":{"id":"SlLAZXahvAYm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Eksekusi Pada Model `Bagging Clasifier`"],"metadata":{"id":"Gf1redlcuafX"}},{"cell_type":"markdown","source":["### Bagging Clasifier Dengan SVC"],"metadata":{"id":"OxWXISa4pXc0"}},{"cell_type":"markdown","source":["Mencari akurasi tertinggi dengan N_estimators dari 2 sampai 100"],"metadata":{"id":"YAiGEWnupcN2"}},{"cell_type":"code","source":["# import model\n","from sklearn.naive_bayes import GaussianNB\n","from sklearn.svm import SVC\n","from sklearn.ensemble import BaggingClassifier\n","from sklearn.datasets import make_classification\n","from sklearn.metrics import accuracy_score\n","# eksekusi data pada model\n","X, y = make_classification(n_samples=100, n_features=4,\n","                           n_informative=2, n_redundant=0,\n","                           random_state=0, shuffle=False)\n","# bagging clasifier menggunakan SVC dan Gaussian(naive bayes)\n","# # SVC\n","n_estimator = range(2,101)\n","akurasi_bags_1 = []\n","for n in n_estimator:\n","  # inisialisasi model\n","  clf = BaggingClassifier(base_estimator=SVC(),\n","                          n_estimators=n, random_state=40).fit(X_train, y_train)\n","  # predict x_test\n","  y_pred = clf.predict(X_test)\n","  # akurasi count\n","  akurasi_bags_1.append(accuracy_score(y_test,y_pred))\n","\n","  \n"],"metadata":{"id":"FmuXMRYGumNd"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Visualisasi Akurasi Bagging dengan SVC"],"metadata":{"id":"NQz5QQcYrDmC"}},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","plt.plot(n_estimator,akurasi_bags_1)\n","plt.xlabel('Value of N')\n","plt.ylabel('Testing Accuracy')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"L6HE-TdBrKhz","executionInfo":{"status":"ok","timestamp":1669559103628,"user_tz":-420,"elapsed":13,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"4017a1a2-394e-41cf-a82f-108c16cb25c8"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Mencari n_estimator dengan Akurasi Tertinggi"],"metadata":{"id":"6mb8XVulreQY"}},{"cell_type":"code","source":["akurasi_bags_1.index(max(akurasi_bags_1))+1 , max(akurasi_bags_1)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"63wpBduBrDJv","executionInfo":{"status":"ok","timestamp":1669559103629,"user_tz":-420,"elapsed":11,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"7b06661c-eb0a-4e65-f59d-54057690fc6e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(24, 0.6296296296296297)"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","source":["### Bagging Clasifier Dengan GaussianNB"],"metadata":{"id":"LH0PLsNhuHik"}},{"cell_type":"markdown","source":["Mencari akurasi tertinggi dengan N_estimators dari 2 sampai 100"],"metadata":{"id":"YtwZWjBZubXW"}},{"cell_type":"code","source":["akurasi_bags_2= []\n","for n in n_estimator:\n","  # inisialisasi model\n","  clf2 = BaggingClassifier(base_estimator=GaussianNB(),\n","                          n_estimators=n, random_state=40).fit(X_train, y_train)\n","  # predict x_test\n","  y_pred2 = clf2.predict(X_test)\n","  # akurasi count\n","  akurasi_bags_2.append(accuracy_score(y_test,y_pred2))"],"metadata":{"id":"qgfbA1GOuJqc"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["import joblib\n","clf2 = BaggingClassifier(base_estimator=GaussianNB(),\n","                          n_estimators=6, random_state=40).fit(X_train, y_train)\n","filenameBCG = '/content/drive/MyDrive/datamining/tugas/cobamodel/bagginggaussian_1.pkl'\n","joblib.dump(clf2,filenameBCG)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"WfqxMOgPYj8g","executionInfo":{"status":"ok","timestamp":1669559115332,"user_tz":-420,"elapsed":529,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"59630481-9919-4615-b441-650295ca336b"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/datamining/tugas/cobamodel/bagginggaussian_1.pkl']"]},"metadata":{},"execution_count":21}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/drive')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"_LcGYbAqeGwg","executionInfo":{"status":"ok","timestamp":1669559118459,"user_tz":-420,"elapsed":3129,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"8efa2761-da43-4b2b-8478-0952875a6f55"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]}]},{"cell_type":"markdown","source":["Visualisasi Hasil Akurasi "],"metadata":{"id":"u9Vlgo_eub2j"}},{"cell_type":"code","source":["plt.plot(n_estimator,akurasi_bags_2)\n","plt.xlabel('Value of N')\n","plt.ylabel('Testing Accuracy')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"nmqKUXhVuXYy","executionInfo":{"status":"ok","timestamp":1669559118460,"user_tz":-420,"elapsed":12,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"7333ca92-8a2e-42d3-94e9-05e8bff8eb96"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Mencari Akurasi Tertinggi"],"metadata":{"id":"MzaBZqEpudqq"}},{"cell_type":"code","source":["akurasi_bags_2.index(max(akurasi_bags_2))+1 , max(akurasi_bags_2)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PYj_oNYkugfq","executionInfo":{"status":"ok","timestamp":1669559118461,"user_tz":-420,"elapsed":11,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"e64c0f0e-dce3-4f66-ada6-85e0f85dc6ed"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(7, 0.7777777777777778)"]},"metadata":{},"execution_count":24}]},{"cell_type":"markdown","source":["## Eksekusi Pada Model `Random Forest`"],"metadata":{"id":"sxR5a0x-wUqZ"}},{"cell_type":"markdown","source":["Mencari akurasi tertinggi dengan N_estimators dari 2 sampai 100"],"metadata":{"id":"Xo10W7C7xwp7"}},{"cell_type":"code","source":["from sklearn.ensemble import RandomForestClassifier\n","akurasirf= []\n","for n in n_estimator:\n","  # inisialisasi model\n","  rf = RandomForestClassifier(\n","                          n_estimators=n,max_depth=2, random_state=40).fit(X_train, y_train)\n","  # predict x_test\n","  y_predrf = rf.predict(X_test)\n","  # akurasi count\n","  akurasirf.append(accuracy_score(y_test,y_predrf))"],"metadata":{"id":"0DXRtRnkxxFN"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["rf = RandomForestClassifier(\n","                          n_estimators=13,max_depth=2, random_state=40).fit(X_train, y_train)\n","filenameRF = '/content/drive/MyDrive/datamining/tugas/cobamodel/randomforest_1.pkl'\n","joblib.dump(rf,filenameRF)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xgbKAWqtZLXN","executionInfo":{"status":"ok","timestamp":1669559128444,"user_tz":-420,"elapsed":24,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"c4505ab6-5ef6-4875-e808-ad8620d2ca6f"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["['/content/drive/MyDrive/datamining/tugas/cobamodel/randomforest_1.pkl']"]},"metadata":{},"execution_count":26}]},{"cell_type":"markdown","source":["Visualisasi Hasil Akurasi "],"metadata":{"id":"cvvBwCB1yOG7"}},{"cell_type":"code","source":["plt.plot(n_estimator,akurasirf)\n","plt.xlabel('Value of N')\n","plt.ylabel('Testing Accuracy')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":279},"id":"sJBi98r0ySH6","executionInfo":{"status":"ok","timestamp":1669559128445,"user_tz":-420,"elapsed":24,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"4bd5c686-fc1c-4f6f-dd32-5954143bbaf7"},"execution_count":null,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 432x288 with 1 Axes>"],"image/png":"iVBORw0KGgoAAAANSUhEUgAAAYgAAAEGCAYAAAB/+QKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nO29e7wkdX3n/f72daZ7mBkuA4EZbqsDBCMBHYlKVASNJPERN+siPMnGJJuQm4mXmLzwyfPywj7JvhJXXV2JG6KsJlEIYSOZZDF4VwJiGBRBBoFxBBlEZoAZYLpnuk93f58/qqq7Tp+qrktXdffp/r5fr/M6p6vr8utT1fWtz+97E1XFMAzDMIYpTHsAhmEYxmxiBsIwDMMIxAyEYRiGEYgZCMMwDCMQMxCGYRhGIKVpDyArjjnmGD3llFOmPQzDMIxVxZ133vmEqm4Kem9uDMQpp5zCjh07pj0MwzCMVYWIPBz2nk0xGYZhGIGYgTAMwzACMQNhGIZhBGIGwjAMwwjEDIRhGIYRiBkIwzAMIxAzEIZhGEYgZiBWAff+8GnufHj/tIdhGMaCYQZiFfC+m+/nyn/eOe1hGIaxYJiBWAU8e7hDa6k77WEYhrFgmIFYBTRaHdrd3rSHYRjGgjE3tZjmmWa7i2KtYQ3DmCxmIFYBzXaHUsHEnmEYkyXXu46IXCQi94vILhG5IuD9D4rIXe7PAyJywF1+toh8XUTuFZG7ReSNeY5z1mm0uizZFJNhGBMmNwUhIkXgKuDVwB7gDhHZrqr9cBxVfZtv/d8DznFfNoFfVtUHReQE4E4RuVlVD+Q13lml21MOLXUpFWXaQzEMY8HIU0GcC+xS1d2q2gauAy4esf5lwLUAqvqAqj7o/v1DYC8Q2NBi3jnkRi+ZgjAMY9LkaSA2A4/4Xu9xl61ARE4GTgW+FPDeuUAF+F7Ae5eLyA4R2bFv375MBj1rNFodAJa65qQ2DGOyzIrn81LgBlVdFuwvIscDfwP8qqqueIRW1atVdZuqbtu0aT4Fhmcguj2l2zMjYRjG5MjTQDwKnOh7vcVdFsSluNNLHiKyHvg/wB+r6u25jHAV0GwPbKZNMxmGMUnyNBB3AFtF5FQRqeAYge3DK4nIGcCRwNd9yyrAZ4C/VtUbchzjzOMpCMCS5QzDmCi5GQhV7QBvBm4G7gOuV9V7ReRKEXmdb9VLgetU1T9/cgnwcuBXfGGwZ+c11llmmYLomIEwDGNy5Joop6o3ATcNLXvX0Ov3BGz3t8Df5jm21UKjPVAQ5qg2DGOSzIqT2gih2TIfhGEY08EMxIzjVxDmgzAMY5KYgZhxLIrJMIxpYQZixvFHMS11zAdhGMbkMAMx4/gVhE0xGYYxScxAzDjLFIQZCMMwJogZiBnHfBCGYUwLMxAzTqPdQdxK32YgDMOYJGYgZpxmq8uGtWUA2uakNgxjgpiBmHEa7Q4bXQNhCsIwjEliBmLGaba7bKhVADMQhmFMFjMQM87BlikIwzCmgxmIGafZ6rCx5vogrFifYRgTxAzEDNPrKc2l7kBBWLlvwzAmiBmIGeZwp4sq5oMwDGMqmIGYYRpuqW/zQRiGMQ3MQMwwTbfU9/q15oMwDGPymIGYYTwFsa5apFIsmIIwDGOimIGYYTwFUauUKBfFnNSGYUwUMxAzTMMt1FevliiXTEEYhjFZzEDMME231He9WqRcLJgPwjCMiWIGYobpK4hKyXwQhmFMHDMQM8zAB1F0fBBmIAzDmCBmIGYYL4qpXi1RNgVhGMaEMQMxwzTbHQoC1VLB8UFYPwjDMCaIGYgZptHqUq+UEBGLYjIMY+KYgZhhmu0OtWoRgIr5IAzDmDC5GggRuUhE7heRXSJyRcD7HxSRu9yfB0TkgO+9fxGRAyLyz3mOcZY52OpQr5QAzAdhGMbEKeW1YxEpAlcBrwb2AHeIyHZV3emto6pv863/e8A5vl28D6gBv5nXGGedZrvbVxDlYoGGmxdhGIYxCfJUEOcCu1R1t6q2geuAi0esfxlwrfdCVb8IPJvj+GaeRqtDzacgLFHOMIxJkqeB2Aw84nu9x122AhE5GTgV+FKSA4jI5SKyQ0R27Nu3L/VAZ5Vmu0u94vogSuaDMAxjssyKk/pS4AZV7SbZSFWvVtVtqrpt06ZNOQ1tejTaHWpV80EYhjEd8jQQjwIn+l5vcZcFcSm+6SXDodkaKIhysWDVXA3DmCh5Gog7gK0icqqIVHCMwPbhlUTkDOBI4Os5jmVV0mibD8IwjOmRm4FQ1Q7wZuBm4D7gelW9V0SuFJHX+Va9FLhOVZfd/UTkFuDvgQtFZI+IvCavsc4iqur4INwopqolyhmGMWFyC3MFUNWbgJuGlr1r6PV7QrZ9WX4jm31anR7dnvoUhDmpDcOYLLPipDaGaLa9dqPmpDYMYzqYgZhRvKS4mt9J3VWGZuIMwzBywwzEjNL0tRsFqJScU7VkjmrDMCaEGYgZpdEeVhACYNNMhmFMDDMQM0qztVxBlIuegjADYRjGZDADMaOsVBDOqWqbgTAMY0KYgZhRvH7UXrnvStF8EIZhTBYzEDOK14+6X+675PogrNyGYRgTwgzEjOKFufobBoH5IAzDmByRBkJE3i8iz5vEYIwBDTfMdW3ZfBCGYUyHOAriPuBqEfmGiPyWiGzIe1AGNFsdapUihYIztWQ+CMMwJk2kgVDVj6nqecAvA6cAd4vIp0XklXkPbpFptLv9OkxgU0yGYUyeWD4It7/0Ge7PE8C3gbeLyHU5jm2habY7/Uqu4EuUMye1YRgTIrKaq4h8EHgtTjvQP1XVf3Pf+jMRuT/PwS0yjdaQgiiZD8IwjMkSp9z33cD/q6qNgPfOzXg8hkuz3el3kwPzQRiGMXniGIgD/vVEZCNwvqreqKpP5zayCXGw1eG92+/tvy6I8Ks/fQpn/Nj6xPt6+MkG//Or36MTcBOvV0u84zWn98t3B/E3tz/M3Y8cAOD+Hz3L8zYP4gGCfBC79j7Lx275Pt2ec7xNR1R5x8+c3ndsG7PNLQ/uY/tdP5z2MEayYW2ZP7rojH6xyDzZ+8xhPvTFB2kHTKNWywXecuFpbDqiGrr9jd96lFt3PRH43otOOYpLXnRi4HujUFX+6pbdvP7szRy7fk3i7ePQaHV4383390PbwxCBX3rxyZy1ZeOy5R+7ZTeHl7q8+YKtmY8tjoF4t6p+xnuhqgdE5N3AjZmPZgosdXrLLqofPn2YjbUy7/y55Abipnt+xLX/9gjHb1iD/xbd7vZ44mCbV55xLK84bVPo9n/+L9+l11M2rC1TLRV42XOP6b8XVKzvxm/9kOvueIQTNqyhudTlQHOJy849iROPqiUeuzF5rvnX7/Ovu55g07rwm940Odzp8VSjzcVnb+b5W/IPXvzKA/v41Dd+wHHrqxRl8A3q9JS9z7Z4wUlH8gsv2BK6/Ye++CA/evowR9bKy5YfOLTE1x7cl8pAPPb0Yf70pu9SLRV500tPSbx9HL71gwN84raHOGZdpT9TEMSPnjlMpVRYYSC+9N29tDu9qRmIoBHn2olukhxZr3DbOy/sv37hf/l8vw5SUprtDgWB2664APFd4N/90TNc9N9voTniCcFrMfrbr3gO73jN6Sve7+dB+J6uGu0OR6wpcds7L+Smex7jdz71zX6ZcGP2abS7vPDkI7nu8pdMeyiBfP17T3LZX92e+vuQFO/7cfNbX87GWqW/fN+zLV70J1/o5waF0Wh1eP05J/Bff+GsZcuv/Ked/P2OR9KNyf3sef4PvH1/8tfO5XknhBvil/35l/pFPJdv32Xj2nLAFuMTRzfuEJEPiMhz3J8PAHfmMpoZoFYtBp6EOBxsdahXSsuMAwyyoQ+OMBD9FqO+yCU/Qf0gmq1uf99eUb9JfZmN8XH8TLP7rOVF0UVNfWSFZwBqQ/+TuONotDortvW2b7Q7qZpteSVv0t4T4jBcdy2MeqUUeA9ptJZHPGZJHAPxe0Ab+Dv3pwX8bi6jmQHCTkIcmq1u4A3eu3mPerofLq0xTJAP4mC70z+eVxZ8Ul9mY3warS61ET6paePdbKOe3LOi0epQLsoKf8eaUhERRirwXk9pLnWXBXZ41ColegqHl5JHAHrfp7T3hDgcHKq7FkatUgy8hzRDDGMWRO7VjV66IpejzyBhJyEOjZAnwv7Ne8TTfbP/9BR8kQT5IJqtzkoFkeOTjpEtjVYn8IY2K3hPpaNuzFnSHEoO9SgUhFq5ONJQHe50USXQ4PYVSLvD2oT/b++YzRyVeTPi4dCjXg1REO1gw5gFcfIgNgF/BDwP6LvxVfWCXEY0ZcJOQhya7WAFUS0VKMhomeoZj3rIE2VQLSYn29pVEO7FleeFbGRL2A1xVpiGggi70dWqpZHXtvdgFKYgwP3+rUs2poEPIr//wXDdtTBqlSJ7n2mtWN5sd3JTonGmmD4FfBc4FXgv8BBwRy6jmQFqlfQ+iLA5UBGhXimNVBD98t6hCsKdYur4fBDtTt+g1PpPSaYgVgOq6ijOnOaOs6A/NTpJBRFyo6tXiiPVcbPfYCtAQYzhnxv4IPJVEP66a2EE3UPanR5LXc1NQcQxEEer6seBJVX9qqr+GjCX6gGCT0JcmiOkXpTzuxmhIIoFoVgQ2t3BPpqtAAVhPohVweGlnjMlMsMKolwsUCkVJqcg2iMURCWmggjyAVbTq+tJKYg410GtunL6e5RhzII4BmLJ/f2YiPy8iJwDHJXLaGaAoJMQl8YIqTeuggDHD+GPYvL7PDx5agpidTCYUpxdBQHO0/ekpi2brfAbZb2agYJIMTPQVxB5+iBiKsl6pbQiCMX7vud1HcUxO/+fW+L7D4D/AawH3pbLaGaAoJMQl2ary7rQC7w00vDECXUrFwvL8iD8UVOFgrjTY6YgVgPN/pz57CoIcG64kwp8aLQ7HL8hOFu5Xi2xv9EesW23v94wtTH8c942eYa5Nnzh6qOoVUq0Oj063R4ld8rZ+75PRUG4VVy3qurTqvodVX2lqr5QVbfH2bmIXCQi94vILhFZEQklIh8UkbvcnwdE5IDvvTeJyIPuz5sSf7KU+E9CUhq+sNOV+y2ONDz9GPARTwKVYqEfxdSfw/ZdGLVKyRTEKmHVKIjqBBXEiKmWesS13Y8ECvh/DvIoUiiICSTKxVYQXlTZ0uBz5K0gRhoIVe0Cl6XZsWtcrgJ+FjgTuExEzhza/9tU9WxVPRtHnfyDu+1RwLuBn8IpCPhuETkyzTiSEnQS4uBlQoc9CUQqCPcCH1WrqewzEK1Oj54uf2JaN8EvszEeec8dZ0W9OrmHDifhK/j/EaWO+zfKEWHmqRTEBBLl4vog+p+j5fdDTt8HcauIfEREXiYiL/B+Ymx3LrBLVXerahu4Drh4xPqXAde6f78G+LyqPqWq+4HPAxfFOObYBJ2EOERlQtcqxdE+iHYXEScpKIxyaeCDaAQ8MU1yOsAYj1FO1VmiXilNNIopzEkdZagGBjdAQYwRrutXEGkysePQjJkJHVQtYZRhzII4ez3b/X2lb5kSHcm0GfAXQNmDowhWICIn44TRfmnEtpsDtrscuBzgpJNOihhOPNKWrGhGnCjnizZaQdTKo0PdKsVCPw9ikFg3ON4kpwOM8VgtCqJWKfLkiLn/rPCmTMOCPGoRzvKBwV25/ZpyITITOwzve9ZT5yFwTUSuQhri5sMMIhV9CsK7jqblpFbVSbQWvRS4wZ3Sio2qXg1cDbBt27ZMzHvQSYhDoxX+BAPOCYxSEFHJLuViod9Rrj+HXVmuIA4cWgrc1pgtGqvESV2PSFDLCi/sd5SCWOoq7U4vsPS4VyizGvDeIA8pTRRTZ9nfeRiIUeG9fga5Tv4xTVlBiMi7gpar6pVBy308Cvjr625xlwVxKcvrOz0KnD+07VcijpcJQSchDlGZ0F50lKquKOYHKxsEBVEpDXwQ/bDY6nIF8cMDhxKN25gOeT/5ZUVUcEVWeNULRikIcG7SlVJlxfthhTL926eLYuou+/voxHuIcYyYNbmCqiXkfR3F8UE0fD9dHKfzKTG2uwPYKiKnikgFxwisiH4SkTOAI4Gv+xbfDPyMiBzpOqd/xl2WO2lLVkTlMdSqxb5MDds+SmY6TmpdNr5hBWHlvlcHec8dZ0W9Ohm/VtD1vGwcfT9C8PcyrFBmf/uUn6PR7rCmXBh57HFod3q0u71YCiIoGmvqCkJV3+9/LSL/jRg3a1XtiMib3XWLwDWqeq+IXAns8IXKXgpcpz4PkKo+JSL/hUFJjytV9alYn2hM0obERWVC9y/wEJkaJ9StXJS+D2JgkHwKIsIRbswOzVbHCUoo59+pbRxqlSKHlrp0e0oxx06FQdfzsnF40YUhD0BhhTL726dVEK0uxx6xhh881czFUB4KKXEeRFA+R9M1YHmdmzRmp4Yz5ROJqt4E3DS07F1Dr98Tsu01wDUpxjcWaZNqIhWEr+R3kEyN0/SjXCz0pfjAIPkURHW0I9yYHRpuSHTYlMis4N10Dy11R4Zgj0vQ9Rw0jrDprrBCmf7t0yqIU46p84Onmrn4YpLkwwz+B/48iHx7isTxQdyDE7UEjhLYxPKIprli0NwnpYIYkQcB4TK10eqweePonrf+RLmg5ir1SpF2txfqyDNmh4ZboG3W6T+5tzq5GoiwZkH9cUT0VAkrlNnfvlrkqYTRWJ1uj8NLvX5L2Dx8MY0EeQxrfX6YwfajDeO4xDnjr/X93QEeV9W5ncfwTkLSkLioTOiofg1xmn44UUyj8yDAMVZBjjxjdmi0u6HTkbPEODkESRiVCe0sj1YQx6wLv+brlRKPPNVMNiY3WXbTEZ6ByP5/kCQTulIqUCkuL57YaOWrIOI8Zh4PPKWqD6vqo8BaEQnMZ5gHgk5CHKKafkRlc8Zp+lH2RTH157B9iXWDxig2zTTrNFeLggh4as2DKKd9pIKI6ImQphGYN13rGYg8ppiSZkLXhnKdmr6eMHkQx0B8FDjoe91wl80twychDlFNPyIVRIymH8uc1O3uisS6QWOUuRV4c0Pec8dZMXiwyVlBjMiE9o9jVBTTqAcsJ4opXeh6X0Hk8D9IGs027EtptMPLk2RBHAMhQxFGPdI5t1cNaRxaTbcbVlgm9LoRCiJu0w+/DyLIoJiCWD00292ZL7MB6SsLJGVUJrR/eVgQRtSN0lMQScpleMc6ql5JnYkdeYyERRuHo7H8PWHyII6B2C0ivy8iZffnLcDu3EY0A6QJiYvKhB7VvjFu2QV/HkQj4InJFMTqodHKr01klqStTZaUUZnQ4O93svLajiqUCc7n6PR0WcveKLxjHVEtRfbETkuUYRymNlSTKm8lGsdA/BbwUpzsZq+e0uW5jWgGGD4JcYjKhB7VAD6uo8pfaqPZXunUnpRD0RifUYXpZolJKohRYb/FgrC2HOxHiCqUCf72qfG/G4Ms5VJkT+y0RE2tDVMfqmobFd47LnES5fbiJLMtDMMnIQ5RmdBrSkVEQhRETEdVubQ8UW7YoAySiUxBzDpRYZmzwqRa2TZH9FLpj6UaXPYjqlCm/71Gu8OR9XgRfoMs5WJkT+y0RCUIDlOrlNjfHJTTmXoUk4h8UkQ2+l4fKSITT2CbJGka70RlQhcKQq0cbHjiKogVPogwBWHJcjNNf0pkNfggJuTXakRMEUF4KZmoQpkQnYkdxDIFEdETOy1JM6H9FZs73R6tTi/XB404U0xnqWq/05vbn+Gc3EY0A6RpvBOn6UfY1FVUiKxHuVigp9DtKY32yszWuimIVUG726PT01WhIKqlIuWi5H5NNUc0C/IIKxzYVxAjto/KowhimYKI6ImdlqQ+BH9NKS9PI88HjTgGouDv5uZ2e5v9K3sMaikKezViNP2oh1zgo/rp+im7fWiXur3AOPqaKYhVQdN341kNTKIR1cEYeSFO06CV35+DMRTEoAhnCgVRKeVW9jyqyOAwdV8ATTPh9FQa4uz5/cDXReTvAQHeAPxpbiOaAeopopjiZEKHydS4jqpy0ZGh7W4vMBO3WBDWlAumIGachm/qYjUQ9mCTJc12t59vEEatUuTZw+Hfn6gwV0ioINpdKsUClVKBeqXEnv3Zl9JPqiC8abZeTyfS1zyOk/qvRWQHgw5yv6CqO3Mb0QzgPwmjOrz5iZMJHSZT44a6efWVljo91wcR3F7RKrrONnGcqrNELaKfehY02h1OrtRGrlOvlHj8mcMrt40olAnpEv6arYHjPKondlqSZkJ7xuDQUnciCiJWRTdV3amqHwE+C/wHEbk3txHNAP6TEJc4mdDjK4iCu37XSawLOF6tWrSKrjNO36m6CpzUMJky8s1WDCd1yANWVKFM573k4bp+x3lUT+y0NGL4XvwM8qk6E1EQcaKYThCRt4nIHcC97jZzHfbqPwlxiJsJXa8GJ9vEDXXzDMSB5pK7vimI1ciqUxAR/dSzoBEnzDXkASuqUKbzXvKEP79KT9tPIvoY6RREs9WNZRjHJdRAiMjlIvJlnFafRwP/GXhMVd+rqvfkNqIZwH8S4hA3E9r5ogUriDihbp4P4sAhp2xx0IWRpiiZMVnihGXOEvWIfurjEicTGry+7umiAEdlYofR8LUC9ffEzpI0Pghvu8HU9HR8EB/BaQP6f6vqDgARiV/IZBWTVEHEzWNwpHqAgoh5kVSGFUTA8dIUJTMmS5ywzFki71a2cTKhwTEAjlrv9dU0RBfKhNGZ2GH4qyMMqslmW0o/eRTTwJcS98F0HEZNMR0PXAu8X0Tud1uAjm55NickTTgb1LIffaLCQuXiNv3oTzEdWlo2Tj+1nDI+jew42Mp/7jhLwjKYsyLulFuYozmqUOZg+2L/fx+Hg77qCINGYtn+Hw4m9UG418zBVqff1GwqU0yq+qSq/k9VfQVwIXAAeFxE7hORuQ5zHWSPJlQQMS7wIJkaN12+7EYxHXA7Y5kPYnUyibnjLMlbQcSdcqv7nuKXbR9RKNMjbIo3DH91hDSZ2FF4mdCJEuUqA1+K91nW5jhVGTeKaY+qvl9VtwEXAytjzeYI/0mIQzPmBV4LucDjOqo8H8R+d4opNIrJfBAzjafwRk2JzBJeFFOSUtlJiDvlVqsGK/uoQpn97UOmeMNoBCiILJWUlwmdxBflL57oz9PIi8R7VtUHVHVue1JD8gqWcTOhw6qtxm360fdBHIpQEOaDmGm86Ji4OTbTplYtoQqHl7J10Hr0EwfTKoiIQpn97RNmQwf7ILJ7+GrGzH/yMyi/3olV4HBcrLN9AP6TEIe4eQz+BvDLto/Z9MPzQTw9SkFUSrQ6PToJ6t4bkyVO3a5ZIk0OQRLi3ijDSslEFcocbB/fP9frqVtKexDF5Bw7u/9BXMPoZ/Dw2u2XSM8TMxAB+E9CHOJmQo9UEHF8EENO6kAF4RmhBEl+xmRpxqjbNUvUEk65JiW2gggpRhnX4IblUQThJclOREEkuMlXS044fLPdCa2mkCWRIxORFwQsfhp4WFXnci7DfxLiENfJFlYPJm7Tj0rJzYNoelNMwQoCnItv/ZqFCDpbdaw6BeGLnMmDRow8Bhhc28PjaLQ6bN64JvI4YZnYgWMaqpcV1RM7DYNjxL/Ji0hfCcV1zo9DnL3/BfAC4G6cYn0/gZNRvUFEfltVP5fj+KaC/yTEYfAEFC9Mb9hAxI5i8qaYDi2FJtbVE0ZgGZMnrlN1Vug/dOR0TcXJhAa/glgZ5pq1ghiuuJumI13kMVJGs3mfwwvvzZM4U0w/BM5R1W2q+kKcXhC7gVcDf57n4KZJooup3Y2VCR0kU5M0/fCiFQ40l0IvqrynA4zx8WforgYGDx35XFNx+6HUQiKJ4hTKhPBM7CCGH/qSJs/GOkbKTGjvc0xCicYxEKepar84n1vJ9QxV3Z3fsKZPWN2kIOIqgCCZmqTph6cgOr3gQn3+/ZiCmF1Wm4JIGrSRlEa7i0h02G+YHyBOoUyAdb5M7CgGobfOMQel9LNXEElv8uuqpX4UU96+rDgG4l4R+aiIvML9+Qtgp4hUgaVRG4rIRW4W9i4RuSJknUtEZKeI3Csin/Yt/zMR+Y7788ZEnyoD6tX4STVxfQhBMjVJqJu/vECYv6Oe83SAMT5xwzJnhbDgiqxotjrUytFhv2U35t//8BO3UCb4CvbF+Bx9v4jve7ku4zI2jZSZ0F4+xySuozh7/xXgd4C3uq9vBd6BYxxeGbaRiBSBq3CmovYAd4jIdn8vCRHZCrwTOE9V94vIse7yn8fxe5wNVIGviMhnVfWZZB8vPUmSauIqiCCZmiTUreIzEJEKwqaYZpZJPPllSViCZ1YkcbbWK8vL2Sd5CvfnUWxYOzqAI6j8R9YZ5d7Yk2ZC1yslHn/2MM12h3U5X0dxGgYdwukq9/6Atw+O2PRcYJc3FSUi1+FkYfubDf0GcJXb5xpV3esuPxP4mhsl1RGRu4GLgOujxpsV3kmIQ9xM6CCZmiTUzcukhnCDkrdD0Rif1RfFlKw2WVKSTLnVhkrJxC2UCeGZ2EEERSaG9cROS9pM6Fq1xMF9Hfe+M2UfhIicJyKfF5EHRGS39xNj35uBR3yv97jL/JwGnCYit4rI7SJykbv828BFIlITkWNwlMqJAWO7XER2iMiOffv2xRhSfGrV+DXw42ZCw8pM5yShbsWCIDLYT9j+wRTErLLU7dHu9FaVD6JaKlCQHBVEgqmS+lBDrEGZm2QKIoqg8h/1jDvr+TvWJaFeKfLkQbfk/7QVBPBx4G3AnUDWd50SsBU4H9gCfE1Enq+qnxORFwG3Aftwyo6vOLaqXg1cDbBt27ZMC8Uk6aLVbHU57ojoOGxYeZElCXUTEcrFAu1OL/TCWpvzdIAxHqut1Dc4153zYJOjgoh5o3M6u61UEOtiFuuDmAoiYOq3VklWDTb6GOkyoWuVEs8mMIzjEEfbPK2qn1XVvW6F1ydV9ckY2z3K8qf+Le4yP3uA7aq6pKrfBx7AMRio6p+o6tmq+mqc/IsHYhwzM5J00YrTDWuw3+UyNWmom+eHCLuwKqUClWIhN4eiMR79B4JV5IMArwhkjj6IuApiyA8Qt1AmhGdiB9FsdSkWhKpv+qeecWe9tL4o/zazEDsvxhMAAByJSURBVMX0ZRF5n4i8RERe4P3E2O4OYKuInCoiFZw2pduH1rkRRz3gTiWdBuwWkaKIHO0uPws4C5hoQp7XRStOBcs43bAG+w1WEHG/IJ4fYnR7xXwarBvjE7e97KzhlJHPL4op7o1uxQNWAkU2CBKJpyBqlSIiPr9fxp310kYh1YYc53kSZ+8/5f7e5lumwAWjNlLVjoi8GbgZKALXqOq9InIlsENVt7vv/YyI7MSZQvpDVX1SRNYAt7gn5xnglyZd1qNWKdFzK1hGRRk0EswlDsvUpE0/yhEKwnvvoPkgZpJB+OQqVBA5PXQkcbaGP2DFVxBxHM1BkYlZV0pupKzJtUxBTNtAqGpoKGuMbW8Cbhpa9i7f3wq83f3xr3MYJ5JpavgTzkYZiKRNP+qVEnufafVfJ2364RmIUV+IvBqsG+MTtyzLrDEcPZQljURRTMuv7biFMp1t41dkbQTkNiXJxI5Do93lyHry9qXLFMS0nNQi8kuq+rci8vag91X1A/kNa/osK1mxLny9pE0/hmVq0lA3b71RX4haNb/pAGM80lTwnAXqlSJPup0Ms6aZoPSI03M9nYJIUpG1GaIggnpipyVtRr1/m2mW+667v48I+Blxy5wP4tbAD8q4HL3f5TI1adOPvg9ixIXlJBOZgphF0lTwnAVq1VIu1VzbnR7tbvyw31qlyKGlLt2e4xs8mCCaJygTO4xGQG5T1iW/09bk8m8ztXLfqvqX7p9fUNVb/e+JyHm5jmoGGKTlRxmINApicIElbfoRxwdRq5TY3zwUe5/G5AjK0F0NDGcwZ0XSIA1/KZkj1pRjF8ocbB/vczTbHY4dCl2v++4JUZnYcchEQeQcLh1HJ/2PmMvmir6CiLiYkpbs9ctUb/skTwF9H8SIJ9B6jiGJxnj0M3RXm4LIyQeRJBMafF0Z3e3ilrnpbx/zcwR1eazFvCfEod+xbuwopun5IF4CvBTYNOSHWI8TlTTXxC1Z0VcQCaKYnP122bC2kLjpR1QehHOM/JKajPHwbmy1iMqls4bz0NFFVZeFfo5Lkkxo8FcKcLaLWyizv301noII6vKYZSHMQwmqOA/jrzBbTVimIymj9l7B8TWUWO5/eAZ4Q66jmgHiFr1LrCCGpq6SNv0ou13lRl1YdYtimlka7Q7VUoFSBk7OSVKrlOj2lFYn217niRVEZYIKIiCKyTnm+A9f40SzDXpUFDM11kGM8kF8FfiqiHxCVR8GEJECsG6SVVWnRWwFkfIC9y6yRrvLxlr8ULdBmOvoKKZmu0uvp5EllI3J0mx1V1WZDY+678a8JkP1k1hBVAMURIIHLE8JjUJVc1cQzYQVFJaNw91mEn6sOI8x/1VE1otIHfgOTi+IP8x5XFPHq+0SFS6aViL3FUTCkr19J/WIm4y3P0/GGrNDI6HPaVYIa5c7LklqKUGAgkhQKBPiJbu1Oj16uvI7Vo95T4jDOApibbmIyGT8WHEMxJmuYng98FngVOA/5TqqGWBNuYBIdBetJKn+/vX6CiJhqFslVqLcyr4TxmzQbHVj3wxnicHUaLYPHUnyGMD/4OZN0SaLAoxTkTUs271fyykDI9lMaBj9eMUTJ3EdxTEQZREp4xiI7aq6hFNqY67pV7CMrSDiVqNcXjAsaahbuShUSoWRiTqDC9kUxKyxWhVEf2o044eOJJnQ4As/bw0URJIn6ThVBvqBBEOGJ0ktpyiC+k0koVYpTuQ6inNW/hJ4CKdHw9dE5GQcR/XcU6sUuemex3jg8WdD13noyUbkDXv5Pp1/+ftuvp9P3PZQ4lC3crEQaVC8/b39+rtGfvEKIrz5gufyolOOin38IPY+e5gPfeFB3vV/nUm1lM9F++1HDvDBLzzQT5CKQ7lY4I8uOp0zfmx96Dr/eNejqMLrzxluVZIeVeW9/7ST7+1b2U/r248c4CdP3JjZsSaFdx29Z/u9meQAeDx6wMnXif2A5a73l1/7Hv909w/Z+0wrsYLY31ziP338G6HrDHJVgsNc/+brD/GV+/cOb5aIJ/r9HNKpgHq1NBEfRJxaTB8GPuxb9LCIpK7PtJq4ZNuJ3Pq9J0ZmkB6zrsoFpx8be58nHrWWV/34cTzZaHGw1WHbyUfy8tOOib39zz3/eLYcWRu5zk9u2ch5zz2aZrs7cux373ma5x67bmwDceuuJ/jUN37AZeeexE9s3jDWvsL4wn2P85X793HOSfFurj11bsbnnnrUSAPxydseQsnWQLQ6PT5x20Ns3riWY9dXl733nGPX8dqzjs/sWJPitOOO4OWnbeLZw0uZZlRvWFvm9WefEHu6ZP2aMq/7yRN4ZH+Tg60OP7F5PRf+ePzv3/mnb+LOh/dHfoaXPufoFYa8XCzwxm0n8sDeZ8f+H6wpF7jwjGM56ajR3+UwfvGnTuLY9fF60IxD5FkRkeOAPwVOUNWfFZEzgZfgNBKaa97xmtN5B6dnus9qqcjH3rQtesUQXnnGsbzyjNFfiB/bsIZP/fqLI/f1oj/5QiYRGd40Qdbz08PHWFct8ZnfiZfE3+spz/njmyLni53Y/ixGuHyfAL/xslP5lfNOzXbnU2LD2jJ//WvnTnsYFArChy87J/X2L33OMbz0t+M/kA3zZ284K/W2WfLrL/t3EzlOnHmRT+CU5T7Bff0A8Na8BmRMjnqlmElMt2dk8nSKJ804LxSEWjm6+maj3clhXt3Lll59zmjD8BNqIETEu7qPUdXrgR44fR7IvvWoMQVqlVK2CiJHp3ijnTx/wMkHiVAQrW4OkTmrs96SYQwzSkH8m/u74XZ3UwAReTHwdN4DM/KnXl1FCqKVPPonjkJqtDs5xPavznpLhjHMqEccLwX37TitQp8jIrcCm1iAUhuLQK1S4kBz/Br/3jROniXGgzJbo4hSSN2ecnjJKR3R6fYyK3+xWns+GMYwo65gf5G+z+B0hhOgBbwKuDvnsRk5s65a6ocZjoNnGPJsUtRsdzk6YfetKIXkNx7NpS7rMzIQnoJYbW1FDWOYUd+IIk6xviNwmgeV3GU1d5mxyqll1FioryBynGJy+n4neyKvR/gglvU2ztB/krSAo2HMKqOu4MdU9cqJjcSYOPWMWpP2fRA5Oqmb7W7i5ir1Sok9+8MVkt/3kKX/JGkJeMOYVUYpCCsDOufEKTsQh0EeRM4KIrEPYrRCMgVhGKMZZSAunNgojKlQr5ZY6irtMWv8D6KY8lEQqk73raRz+lEKKW8FsXaVNQUyjGFCDYSqPjXJgRiTZ9CbYryb4yAPIh8F0er06PQ0nYKI64PI1EA4IbnWi8NY7ayutlZGptQzKguet4IIK54WRZRC8n/uLP0njZS9hg1j1jADscAMN4BPS95RTGlLVwyaywSPy+93yHLszXbHQlyNucAMxAIz3AA+DUvdXv8JPa8oprSlK7z1wypv+pcfzFJBtExBGPNBrgZCRC4SkftFZJeIXBGyziUislNE7hWRT/uW/7m77D4R+bDk3Z17ARlu35gG/1N41iUr+vtNWboiSiEtS5TLcOxJm0AZxqyS22OOiBSBq4BXA3uAO0Rku6ru9K2zFXgncJ6q7heRY93lLwXOA7zauv8KvAL4Sl7jXUSy6DPczxquRDeDT0va0hVRCqnR7lIuCoJk6j9ptLuZNtUxjGmRp4I4F9ilqrtVtQ1cB1w8tM5vAFep6n4AVfXaNCmwBqgAVaAMPJ7jWBeSTBSEayA2HVGl0e6gWTdXwN/gPaGCiPh8TTe3olbNJh/Ev19TEMY8kKeB2Aw84nu9x13m5zTgNBG5VURuF5GLAFT168CXgcfcn5tV9b7hA4jI5SKyQ0R27Nu3L5cPMc/Uq+NHMXl+h01HVFGlX/wuS/qJZylKbcBoBVGvFJ3e45kmypkPwpgPpu2kLgFbgfOBy4C/EpGNIvJc4MeBLThG5QIRednwxqp6tapuU9VtmzZtmuCw54P6UAP4NDR8CsL/OksGje0zVhBtp75TVhnlHo12h3UWxWTMAXkaiEeBE32vt7jL/OwBtqvqkqp+H6db3Vbg3wO3q+pBVT0IfBanzamRIV6m7zg3dc+4bFpXXfY6S9KWrohSSI2WoyBqGdWk8mi2utZNzpgL8jQQdwBbReRUEakAl+L0lfBzI456QESOwZly2g38AHiFiJREpIzjoF4xxWSMR7EgrC2P51yepIJIWrqiryBCjJbTxrTkONgzimJqd3q0uz3zQRhzQW4Gwm1N+macftb3Ader6r0icqWIvM5d7WbgSRHZieNz+ENVfRK4AfgecA/wbeDbqvpPeY11kXF6JoyhINoDH4TzOnsD4fWjTlq6ohaRKd5oOfWdapXsFMQhdz/mgzDmgVyvYlW9CafRkH/Zu3x/K07HurcPrdMFfjPPsRkOTte1MRSEa1yOPWKN+zr7Kaa0pSuKBWFNuTDaB1EpIZKdYbNmQcY8YY85C06tsgoURCt96Yp11dLoKKaqYyCyMmzNfkiufbWM1Y9dxQuOUxJ7vES5aqnA+jVOYlgeCuLgGKUrRikkL18hSwVxMGXElWHMImYgFpxapcizh8eLYqpXS76yFvn4INI6fcMUUq+nNJecaCPBUUK9no5dottzdpuCMOaBaedBGFOmXhndtzmKhutAHpQOz8kHkTJs1OlLvXJMhztdVJ0SId7T/qGl8cfeSFlY0DBmETMQC06tWhxrWqjZ6lKvlFhTLjhTNTkU7BundEWtUgycQhv0jS5FRjsloe+DsCkmYw4wA7HgZKIgqkVExClZkYOCGKd0Rb1SCsyDGCTfDRREFkl+jZSFBQ1jFrGreMGpVYtj3dSb7W7/Zph1yQqPxhgNeJzPN0JBuGGu3nHGxRSEMU+Yglhw6pUS7U6PpW66Inte/2Vw5vuzbLzj0RwjiskpxBdgIHz5CoOy4NkpiFrCrG/DmEXMQCw4/YJ9KVWE83TvUxAZ+yC80hVpi9+FKaSGL9rIe9rPSkGsKRcoFe2rZax+7CpecOoRfZujcJ7uXQVRGS+nIohxS1eEKaR+G1OfgsjEB9HumP/BmBvMQCw4tep40yvLFEQ1+65y45auCCv57SmIeqXUXycTBdHqmv/BmBvMQCw44yiIbk85vNRbriAynmIat3TFYApt+biafWVS9PXFGH/spiCMecKu5AWnNoaDdrhPQy2HvtRpmwV59NXB0OcbKJOSb9n4Y3dCck1BGPOBGYgFpz5GiYz+U3h1EMWUtYJojKsgKiEKotWlIFAtOSK6WJBMQnQbrU7i1qiGMavYFNOC4914D6a4sfvn8Z19OQrCqeKeDeMmnvUjlAIURL1SQkQQEbdmUzZhrqYgjHnBDMSCM1AQaaaYBvP4zr5KdHpKO2VORfAxxks8G6Ug/PscN6Pcw3wQxjxhBmLBGfgg0iuIde6USj2ixWcaxlUQg77UAQrCNxVUHzOj3KPZtigmY34wA7HghIWBxmHggygt+51lLsTYCqJfZ2llFJPf6NSrpWyimFqmIIz5wQzEglMuFqiUCqlu6v1IIF+YK6TPyg48xpilKwaVWlfmQfh9BU7V1/HG3en2aHV61gvCmBvMQBjUK8VU00LN1rCC8BzC2SqIail96Yq+QgpSEP4ppgx8EM0l6yZnzBdmIAxqKUtkTERBtMcLGx0opJU+iGUKohpcFjwJTV+FWMOYB8xAGNSrKRXEUJ2kQVJahgoig7DRekAZcq/RkX+dcX0n45YFMYxZwwyE4SS4pVEQrQ7lolBxk83GrQwbeIx2px8llZZapRSYB+F3fNdCGgsloWnNgow5wwyE4c6/p1MQtaGncMg6iikDBVFdriBUNSCKyVEQ4yT5NcaMuDKMWcMMhOFmESe/qR8c6hU9qAybnYHIonRFrVJaline6vTo9nSFgugpHF5Kn+Q3XJvKMFY7ZiAMJwcglYLo9I0CwNpycFmLcchOQQzGNFwixFsHxlM/4xYWNIxZwwyEkbqXdKPVXaYgigVhbTnbvtRZlK6oDZUhHy4R4q0D42WBj1ua3DBmjVwNhIhcJCL3i8guEbkiZJ1LRGSniNwrIp92l71SRO7y/RwWkdfnOdZFxuklnaaaa2fFzTCrkhUejQwa8NSHypAHlfrOwn9y0JzUxpyR25UsIkXgKuDVwB7gDhHZrqo7fetsBd4JnKeq+0XkWABV/TJwtrvOUcAu4HN5jXXRqVWKHF5y5uWLBYm9XaPV5YSN5aF9ZVOyYnCMDBREdXkSXD87O8B/Mo768T73WqvmaswJeSqIc4FdqrpbVdvAdcDFQ+v8BnCVqu4HUNW9Aft5A/BZVW3mONaFJqziaRRBCiKLkhUeWZWuqA+V8m6OUhBjTDE12l0qbmKeYcwDeV7Jm4FHfK/3uMv8nAacJiK3isjtInJRwH4uBa4NOoCIXC4iO0Rkx759+zIZ9CJSS1nyu9HurnDI1qvZlM2G7EpX1ColDi116facENZABZHSSPppDuVWGMZqZ9qPOiVgK3A+cBnwVyKy0XtTRI4Hng/cHLSxql6tqttUddumTZsmMNz5xFMQScNTm60QBZFRFFNWpSs8A3PINThB4aj1kMZCSWgMZWcbxmonTwPxKHCi7/UWd5mfPcB2VV1S1e8DD+AYDI9LgM+o6lKO41x40pT87vWU5tLyKCbIrvEOZFe6YhCh1HH3u7xV6rJ1xlUQ5n8w5og8DcQdwFYROVVEKjhTRduH1rkRRz0gIsfgTDnt9r1/GSHTS0Z21FMkuB3udFFlRRKb05d6thTEuqGmQc2hRkdB66Sh0e4uywsxjNVObgZCVTvAm3Gmh+4DrlfVe0XkShF5nbvazcCTIrIT+DLwh6r6JICInIKjQL6a1xgNhzQKojFU6ttjuKzFOAxXi03LcBHBRruLCKwpDfa7plxAZGVZ8CQ0hzLLDWO1k+vjjqreBNw0tOxdvr8VeLv7M7ztQ6x0ahs5sC5FJ7hmyM3bKR2ekYIIiDZKw3ARwWarQ61cpOAL6RUR6mOOvdHucmS9MtZYDWOWmLaT2pgB+jkACaaGGiHTP/VKkXanx1I3fU2j4WOM74NYngQXNhWUNqPco9k2BWHMF2YgjFRZxM0QB3Itw5LfWZWuqA8ZwLAb+bj+Eyfr23wQxvxgBsLwRfAkUBDtcAXh7Gt8P0Qjo9IVKxREqxtodExBGMZyzEAYVEoFykVJFMXkOXPDFEQWkUzezXrc0hX1oTDXZrsTOG1VD2gsFJdeT1f0xzCM1Y4ZCANwayilUBDDT/eZKoiMSlfU+qW8u/3fgQpijAisQxllfRvGLGGPOwbg3Nj/4Zt7uHXXE7HWP3DIyV0cfrr3bry/86lv9vtDpGXfwVYmhe8qxQKlgvDxf/0+N37rUR5+qsmFZxy7Yr16pcRtu57k1R9IHlntlfFYawrCmCPsajYA+K3zn8Ptu59MtM3mjWs5eiis86wtG/iPL9ySSdvRrcet45wTjxx7PyLCW1+1lZ2PPdPf76UvOmnFepeeeyJK+pajz9+ygfNPs5Ivxvwg4/TgnSW2bdumO3bsmPYwDMMwVhUicqeqbgt6z3wQhmEYRiBmIAzDMIxAzEAYhmEYgZiBMAzDMAIxA2EYhmEEYgbCMAzDCMQMhGEYhhGIGQjDMAwjkLlJlBORfcDDEasdA8SrJTF/LOpnt8+9WNjnTs7JqhpYAmBuDEQcRGRHWMbgvLOon90+92JhnztbbIrJMAzDCMQMhGEYhhHIohmIq6c9gCmyqJ/dPvdiYZ87QxbKB2EYhmHEZ9EUhGEYhhETMxCGYRhGIAtjIETkIhG5X0R2icgV0x5PXojIiSLyZRHZKSL3ishb3OVHicjnReRB9/f4rdpmEBEpisi3ROSf3denisg33PP+dyJSidrHakNENorIDSLyXRG5T0ResgjnW0Te5l7j3xGRa0VkzbyebxG5RkT2ish3fMsCz7E4fNj9H9wtIi9Ie9yFMBAiUgSuAn4WOBO4TETOnO6ocqMD/IGqngm8GPhd97NeAXxRVbcCX3RfzyNvAe7zvf4z4IOq+lxgP/CfpzKqfPkQ8C+qegbwkziff67Pt4hsBn4f2KaqPwEUgUuZ3/P9CeCioWVh5/hnga3uz+XAR9MedCEMBHAusEtVd6tqG7gOuHjKY8oFVX1MVb/p/v0szs1iM87n/aS72ieB109nhPkhIluAnwc+5r4W4ALgBneVufvcIrIBeDnwcQBVbavqARbgfAMlYK2IlIAa8Bhzer5V9WvAU0OLw87xxcBfq8PtwEYROT7NcRfFQGwGHvG93uMum2tE5BTgHOAbwHGq+pj71o+A46Y0rDz578AfAT339dHAAVXtuK/n8byfCuwD/pc7tfYxEakz5+dbVR8F/hvwAxzD8DRwJ/N/vv2EnePM7neLYiAWDhFZB/xv4K2q+oz/PXVim+cqvllEXgvsVdU7pz2WCVMCXgB8VFXPARoMTSfN6fk+EudJ+VTgBKDOyimYhSGvc7woBuJR4ETf6y3usrlERMo4xuFTqvoP7uLHPZnp/t47rfHlxHnA60TkIZwpxAtw5uY3ulMQMJ/nfQ+wR1W/4b6+AcdgzPv5fhXwfVXdp6pLwD/gXAPzfr79hJ3jzO53i2Ig7gC2uhEOFRxn1vYpjykX3Hn3jwP3qeoHfG9tB97k/v0m4B8nPbY8UdV3quoWVT0F5/x+SVV/Efgy8AZ3tXn83D8CHhGR091FFwI7mfPzjTO19GIRqbnXvPe55/p8DxF2jrcDv+xGM70YeNo3FZWIhcmkFpGfw5mjLgLXqOqfTHlIuSAiPw3cAtzDYC7+/8HxQ1wPnIRTFv0SVR12es0FInI+8A5Vfa2I/DscRXEU8C3gl1S1Nc3xZY2InI3jmK8Au4FfxXn4m+vzLSLvBd6IE7n3LeDXceba5+58i8i1wPk4Zb0fB94N3EjAOXYN5kdwptyawK+q6o5Ux10UA2EYhmEkY1GmmAzDMIyEmIEwDMMwAjEDYRiGYQRiBsIwDMMIxAyEYRiGEYgZCGOhcSvfvmZo2VtFJLTAmYh8RUQybxA/dIxr3Uqcbxta/h4RaYrIsb5lB/Mci7G4mIEwFp1rcRLr/FzqLp8KIvJjwItU9SxV/WDAKk8AfzDhYRkLiBkIY9G5Afh5r2+AW+DwBOAWEfmoiOxwew68N2hj/9O7iLxBRD7h/r1JRP63iNzh/pwXsO0aEflfInKPW2jvle5bnwM2i8hdIvKygMNeA7xRRI5K/7ENIxozEMZC42YX/xtODX1w1MP1bvGzP1bVbcBZwCtE5KwEu/4QTl+CFwH/AbcE+RC/6wxBnw9cBnxSRNYArwO+p6pnq+otAdsdxDESb0kwHsNIjBkIw1g+zeSfXrpERL6JU7LheTjNpuLyKuAjInIXTm2c9W6FXT8/DfwtgKp+F6dcwmkx9/9h4E0ickSCMRlGIkrRqxjG3POPwAfd1ow1Vb1TRE4F3oHjC9jvTh2tCdjWX6vG/34BeLGqHs5jwKp6QEQ+jaNCDCMXTEEYC4+qHsSpAnoNA/WwHqe3wtMichyDKahhHheRHxeRAvDvfcs/B/ye98ItqDfMLcAvuu+fhlN07f4EQ/8A8JvYg56RE2YgDMPhWpx+ztcCqOq3caaWvgt8Grg1ZLsrgH8GbsPpbObx+8A2N1R1J/BbAdv+BVAQkXuAvwN+JUnlUVV9AvgMUI27jWEkwaq5GoZhGIGYgjAMwzACMQNhGIZhBGIGwjAMwwjEDIRhGIYRiBkIwzAMIxAzEIZhGEYgZiAMwzCMQP5/vCTbYDRVoSgAAAAASUVORK5CYII=\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"markdown","source":["Mencari Akurasi Tertinggi dari N"],"metadata":{"id":"QPwR_ERIyR3a"}},{"cell_type":"code","source":["akurasirf.index(max(akurasirf))+1 , max(akurasirf)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"M6-f_7ZsyYyN","executionInfo":{"status":"ok","timestamp":1669559128445,"user_tz":-420,"elapsed":23,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"49269cb9-2399-4403-89b2-2fd46cc8677e"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(14, 0.7222222222222222)"]},"metadata":{},"execution_count":28}]},{"cell_type":"markdown","source":["## Eksekusi Pada Model `Stacking clasifier`"],"metadata":{"id":"j-G1UqBkyzje"}},{"cell_type":"code","source":["from sklearn.ensemble import StackingClassifier\n","from sklearn.tree import DecisionTreeClassifier\n","\n","# estimator menggunakan Random Forest, SVC GaussianNB\n","## untuk n_estimators menggunakan n dengan akurasi tertinggi \n","estimators = [\n","    ('rf', RandomForestClassifier(n_estimators=38, max_depth=2, random_state=40)),\n","    ('svc', SVC()),\n","     ('gnb', GaussianNB()),\n","     ('bagsvc', BaggingClassifier(base_estimator=SVC(),\n","                          n_estimators=14, random_state=40)),\n","    ('baggnb' ,BaggingClassifier(base_estimator=GaussianNB(),\n","                          n_estimators=9, random_state=40))\n","]\n","\n","sc = StackingClassifier(\n","      estimators=estimators, final_estimator=SVC()).fit(X_train, y_train)\n","\n","y_predsc = sc.predict(X_test)\n","akurasi = accuracy_score(y_test,y_predsc)"],"metadata":{"id":"xNsAZ2Ag1qT-"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Hasil Akurasi Dan Score dari Stacking Clasifier"],"metadata":{"id":"QAkUH2ex38_T"}},{"cell_type":"code","source":["print(f'Akurasi Untuk Stacking Clasifier =  {akurasi}')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"nuq9t-_d4DP5","executionInfo":{"status":"ok","timestamp":1669559129586,"user_tz":-420,"elapsed":5,"user":{"displayName":"Caca Erha","userId":"13359221303846732984"}},"outputId":"5c6670b3-3a53-4f8f-a72d-e75bf5194e99"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Akurasi Untuk Stacking Clasifier =  0.7592592592592593\n"]}]}]}