{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.11.11","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":4289678,"sourceType":"datasetVersion","datasetId":2527538}],"dockerImageVersionId":31011,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"import library and read data","metadata":{}},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport matplotlib.pyplot as plt # librarie de plotare\nimport seaborn as sns # librărie construită peste matplotlib\nimport pandas as pd ","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:18:15.605707Z","iopub.execute_input":"2025-04-15T16:18:15.606320Z","iopub.status.idle":"2025-04-15T16:18:16.625549Z","shell.execute_reply.started":"2025-04-15T16:18:15.606294Z","shell.execute_reply":"2025-04-15T16:18:16.624843Z"}},"outputs":[],"execution_count":2},{"cell_type":"code","source":"dataset = pd.read_csv(\"/kaggle/input/diabetes-dataset/diabetes.csv\")\ndataset.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:18:21.244752Z","iopub.execute_input":"2025-04-15T16:18:21.245144Z","iopub.status.idle":"2025-04-15T16:18:21.286506Z","shell.execute_reply.started":"2025-04-15T16:18:21.245123Z","shell.execute_reply":"2025-04-15T16:18:21.285769Z"}},"outputs":[{"execution_count":3,"output_type":"execute_result","data":{"text/plain":"   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n0            6      148             72             35        0  33.6   \n1            1       85             66             29        0  26.6   \n2            8      183             64              0        0  23.3   \n3            1       89             66             23       94  28.1   \n4            0      137             40             35      168  43.1   \n\n   DiabetesPedigreeFunction  Age  Outcome  \n0                     0.627   50        1  \n1                     0.351   31        0  \n2                     0.672   32        1  \n3                     0.167   21        0  \n4                     2.288   33        1  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n      <th>Outcome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":3},{"cell_type":"markdown","source":"EDA(EXPLORATY DATA ANALYSIS)","metadata":{}},{"cell_type":"code","source":"dataset.shape","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:19:25.055352Z","iopub.execute_input":"2025-04-15T16:19:25.056072Z","iopub.status.idle":"2025-04-15T16:19:25.060825Z","shell.execute_reply.started":"2025-04-15T16:19:25.056046Z","shell.execute_reply":"2025-04-15T16:19:25.060080Z"}},"outputs":[{"execution_count":5,"output_type":"execute_result","data":{"text/plain":"(768, 9)"},"metadata":{}}],"execution_count":5},{"cell_type":"code","source":"dataset.info","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:19:45.753506Z","iopub.execute_input":"2025-04-15T16:19:45.753774Z","iopub.status.idle":"2025-04-15T16:19:45.766118Z","shell.execute_reply.started":"2025-04-15T16:19:45.753755Z","shell.execute_reply":"2025-04-15T16:19:45.765584Z"}},"outputs":[{"execution_count":6,"output_type":"execute_result","data":{"text/plain":"<bound method DataFrame.info of      Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n0              6      148             72             35        0  33.6   \n1              1       85             66             29        0  26.6   \n2              8      183             64              0        0  23.3   \n3              1       89             66             23       94  28.1   \n4              0      137             40             35      168  43.1   \n..           ...      ...            ...            ...      ...   ...   \n763           10      101             76             48      180  32.9   \n764            2      122             70             27        0  36.8   \n765            5      121             72             23      112  26.2   \n766            1      126             60              0        0  30.1   \n767            1       93             70             31        0  30.4   \n\n     DiabetesPedigreeFunction  Age  Outcome  \n0                       0.627   50        1  \n1                       0.351   31        0  \n2                       0.672   32        1  \n3                       0.167   21        0  \n4                       2.288   33        1  \n..                        ...  ...      ...  \n763                     0.171   63        0  \n764                     0.340   27        0  \n765                     0.245   30        0  \n766                     0.349   47        1  \n767                     0.315   23        0  \n\n[768 rows x 9 columns]>"},"metadata":{}}],"execution_count":6},{"cell_type":"code","source":"dataset.describe()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:21:09.027740Z","iopub.execute_input":"2025-04-15T16:21:09.028033Z","iopub.status.idle":"2025-04-15T16:21:09.078473Z","shell.execute_reply.started":"2025-04-15T16:21:09.028013Z","shell.execute_reply":"2025-04-15T16:21:09.077940Z"}},"outputs":[{"execution_count":10,"output_type":"execute_result","data":{"text/plain":"       Pregnancies     Glucose  BloodPressure  SkinThickness     Insulin  \\\ncount   768.000000  768.000000     768.000000     768.000000  768.000000   \nmean      3.845052  120.894531      69.105469      20.536458   79.799479   \nstd       3.369578   31.972618      19.355807      15.952218  115.244002   \nmin       0.000000    0.000000       0.000000       0.000000    0.000000   \n25%       1.000000   99.000000      62.000000       0.000000    0.000000   \n50%       3.000000  117.000000      72.000000      23.000000   30.500000   \n75%       6.000000  140.250000      80.000000      32.000000  127.250000   \nmax      17.000000  199.000000     122.000000      99.000000  846.000000   \n\n              BMI  DiabetesPedigreeFunction         Age     Outcome  \ncount  768.000000                768.000000  768.000000  768.000000  \nmean    31.992578                  0.471876   33.240885    0.348958  \nstd      7.884160                  0.331329   11.760232    0.476951  \nmin      0.000000                  0.078000   21.000000    0.000000  \n25%     27.300000                  0.243750   24.000000    0.000000  \n50%     32.000000                  0.372500   29.000000    0.000000  \n75%     36.600000                  0.626250   41.000000    1.000000  \nmax     67.100000                  2.420000   81.000000    1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n      <th>Outcome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n      <td>768.000000</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>3.845052</td>\n      <td>120.894531</td>\n      <td>69.105469</td>\n      <td>20.536458</td>\n      <td>79.799479</td>\n      <td>31.992578</td>\n      <td>0.471876</td>\n      <td>33.240885</td>\n      <td>0.348958</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>3.369578</td>\n      <td>31.972618</td>\n      <td>19.355807</td>\n      <td>15.952218</td>\n      <td>115.244002</td>\n      <td>7.884160</td>\n      <td>0.331329</td>\n      <td>11.760232</td>\n      <td>0.476951</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>0.078000</td>\n      <td>21.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.000000</td>\n      <td>99.000000</td>\n      <td>62.000000</td>\n      <td>0.000000</td>\n      <td>0.000000</td>\n      <td>27.300000</td>\n      <td>0.243750</td>\n      <td>24.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>3.000000</td>\n      <td>117.000000</td>\n      <td>72.000000</td>\n      <td>23.000000</td>\n      <td>30.500000</td>\n      <td>32.000000</td>\n      <td>0.372500</td>\n      <td>29.000000</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>6.000000</td>\n      <td>140.250000</td>\n      <td>80.000000</td>\n      <td>32.000000</td>\n      <td>127.250000</td>\n      <td>36.600000</td>\n      <td>0.626250</td>\n      <td>41.000000</td>\n      <td>1.000000</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>17.000000</td>\n      <td>199.000000</td>\n      <td>122.000000</td>\n      <td>99.000000</td>\n      <td>846.000000</td>\n      <td>67.100000</td>\n      <td>2.420000</td>\n      <td>81.000000</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":10},{"cell_type":"code","source":"dataset.duplicated().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:23:02.618066Z","iopub.execute_input":"2025-04-15T16:23:02.618597Z","iopub.status.idle":"2025-04-15T16:23:02.625726Z","shell.execute_reply.started":"2025-04-15T16:23:02.618572Z","shell.execute_reply":"2025-04-15T16:23:02.625070Z"}},"outputs":[{"execution_count":16,"output_type":"execute_result","data":{"text/plain":"0"},"metadata":{}}],"execution_count":16},{"cell_type":"markdown","source":"detect missing values and handle it ","metadata":{}},{"cell_type":"code","source":"dataset.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:22:09.945913Z","iopub.execute_input":"2025-04-15T16:22:09.946167Z","iopub.status.idle":"2025-04-15T16:22:09.952382Z","shell.execute_reply.started":"2025-04-15T16:22:09.946148Z","shell.execute_reply":"2025-04-15T16:22:09.951660Z"}},"outputs":[{"execution_count":12,"output_type":"execute_result","data":{"text/plain":"Pregnancies                 0\nGlucose                     0\nBloodPressure               0\nSkinThickness               0\nInsulin                     0\nBMI                         0\nDiabetesPedigreeFunction    0\nAge                         0\nOutcome                     0\ndtype: int64"},"metadata":{}}],"execution_count":12},{"cell_type":"markdown","source":"Explore Data Ballance","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (12, 6))\nsns.countplot(x=\"Outcome\", data=dataset)\nplt.show();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:24:58.822543Z","iopub.execute_input":"2025-04-15T16:24:58.822873Z","iopub.status.idle":"2025-04-15T16:24:59.034657Z","shell.execute_reply.started":"2025-04-15T16:24:58.822851Z","shell.execute_reply":"2025-04-15T16:24:59.033840Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x600 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":17},{"cell_type":"code","source":"print(f\"% of patients having diabetes: {sum(dataset['Outcome']) / len(dataset) * 100:.2f}%\")","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:25:49.749227Z","iopub.execute_input":"2025-04-15T16:25:49.749522Z","iopub.status.idle":"2025-04-15T16:25:49.754221Z","shell.execute_reply.started":"2025-04-15T16:25:49.749501Z","shell.execute_reply":"2025-04-15T16:25:49.753569Z"}},"outputs":[{"name":"stdout","text":"% of patients having diabetes: 34.90%\n","output_type":"stream"}],"execution_count":18},{"cell_type":"markdown","source":"visualise dtaset distribution","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (12, 12))\nfor i, col in enumerate(dataset.columns):\n    plt.subplot(3, 3, i+1)\n    sns.histplot(x=col, data=dataset, kde=True)\nplt.show();","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:28:29.257064Z","iopub.execute_input":"2025-04-15T16:28:29.257841Z","iopub.status.idle":"2025-04-15T16:28:30.838976Z","shell.execute_reply.started":"2025-04-15T16:28:29.257784Z","shell.execute_reply":"2025-04-15T16:28:30.838198Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n/usr/local/lib/python3.11/dist-packages/seaborn/_oldcore.py:1119: FutureWarning: use_inf_as_na option is deprecated and will be removed in a future version. Convert inf values to NaN before operating instead.\n  with pd.option_context('mode.use_inf_as_na', True):\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x1200 with 9 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\n"},"metadata":{}}],"execution_count":19},{"cell_type":"markdown","source":"Visualize Feature Correlations","metadata":{}},{"cell_type":"code","source":"dataset.corr()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:30:30.594919Z","iopub.execute_input":"2025-04-15T16:30:30.595229Z","iopub.status.idle":"2025-04-15T16:30:30.609925Z","shell.execute_reply.started":"2025-04-15T16:30:30.595208Z","shell.execute_reply":"2025-04-15T16:30:30.609356Z"}},"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"                          Pregnancies   Glucose  BloodPressure  SkinThickness  \\\nPregnancies                  1.000000  0.129459       0.141282      -0.081672   \nGlucose                      0.129459  1.000000       0.152590       0.057328   \nBloodPressure                0.141282  0.152590       1.000000       0.207371   \nSkinThickness               -0.081672  0.057328       0.207371       1.000000   \nInsulin                     -0.073535  0.331357       0.088933       0.436783   \nBMI                          0.017683  0.221071       0.281805       0.392573   \nDiabetesPedigreeFunction    -0.033523  0.137337       0.041265       0.183928   \nAge                          0.544341  0.263514       0.239528      -0.113970   \nOutcome                      0.221898  0.466581       0.065068       0.074752   \n\n                           Insulin       BMI  DiabetesPedigreeFunction  \\\nPregnancies              -0.073535  0.017683                 -0.033523   \nGlucose                   0.331357  0.221071                  0.137337   \nBloodPressure             0.088933  0.281805                  0.041265   \nSkinThickness             0.436783  0.392573                  0.183928   \nInsulin                   1.000000  0.197859                  0.185071   \nBMI                       0.197859  1.000000                  0.140647   \nDiabetesPedigreeFunction  0.185071  0.140647                  1.000000   \nAge                      -0.042163  0.036242                  0.033561   \nOutcome                   0.130548  0.292695                  0.173844   \n\n                               Age   Outcome  \nPregnancies               0.544341  0.221898  \nGlucose                   0.263514  0.466581  \nBloodPressure             0.239528  0.065068  \nSkinThickness            -0.113970  0.074752  \nInsulin                  -0.042163  0.130548  \nBMI                       0.036242  0.292695  \nDiabetesPedigreeFunction  0.033561  0.173844  \nAge                       1.000000  0.238356  \nOutcome                   0.238356  1.000000  ","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n      <th>Outcome</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Pregnancies</th>\n      <td>1.000000</td>\n      <td>0.129459</td>\n      <td>0.141282</td>\n      <td>-0.081672</td>\n      <td>-0.073535</td>\n      <td>0.017683</td>\n      <td>-0.033523</td>\n      <td>0.544341</td>\n      <td>0.221898</td>\n    </tr>\n    <tr>\n      <th>Glucose</th>\n      <td>0.129459</td>\n      <td>1.000000</td>\n      <td>0.152590</td>\n      <td>0.057328</td>\n      <td>0.331357</td>\n      <td>0.221071</td>\n      <td>0.137337</td>\n      <td>0.263514</td>\n      <td>0.466581</td>\n    </tr>\n    <tr>\n      <th>BloodPressure</th>\n      <td>0.141282</td>\n      <td>0.152590</td>\n      <td>1.000000</td>\n      <td>0.207371</td>\n      <td>0.088933</td>\n      <td>0.281805</td>\n      <td>0.041265</td>\n      <td>0.239528</td>\n      <td>0.065068</td>\n    </tr>\n    <tr>\n      <th>SkinThickness</th>\n      <td>-0.081672</td>\n      <td>0.057328</td>\n      <td>0.207371</td>\n      <td>1.000000</td>\n      <td>0.436783</td>\n      <td>0.392573</td>\n      <td>0.183928</td>\n      <td>-0.113970</td>\n      <td>0.074752</td>\n    </tr>\n    <tr>\n      <th>Insulin</th>\n      <td>-0.073535</td>\n      <td>0.331357</td>\n      <td>0.088933</td>\n      <td>0.436783</td>\n      <td>1.000000</td>\n      <td>0.197859</td>\n      <td>0.185071</td>\n      <td>-0.042163</td>\n      <td>0.130548</td>\n    </tr>\n    <tr>\n      <th>BMI</th>\n      <td>0.017683</td>\n      <td>0.221071</td>\n      <td>0.281805</td>\n      <td>0.392573</td>\n      <td>0.197859</td>\n      <td>1.000000</td>\n      <td>0.140647</td>\n      <td>0.036242</td>\n      <td>0.292695</td>\n    </tr>\n    <tr>\n      <th>DiabetesPedigreeFunction</th>\n      <td>-0.033523</td>\n      <td>0.137337</td>\n      <td>0.041265</td>\n      <td>0.183928</td>\n      <td>0.185071</td>\n      <td>0.140647</td>\n      <td>1.000000</td>\n      <td>0.033561</td>\n      <td>0.173844</td>\n    </tr>\n    <tr>\n      <th>Age</th>\n      <td>0.544341</td>\n      <td>0.263514</td>\n      <td>0.239528</td>\n      <td>-0.113970</td>\n      <td>-0.042163</td>\n      <td>0.036242</td>\n      <td>0.033561</td>\n      <td>1.000000</td>\n      <td>0.238356</td>\n    </tr>\n    <tr>\n      <th>Outcome</th>\n      <td>0.221898</td>\n      <td>0.466581</td>\n      <td>0.065068</td>\n      <td>0.074752</td>\n      <td>0.130548</td>\n      <td>0.292695</td>\n      <td>0.173844</td>\n      <td>0.238356</td>\n      <td>1.000000</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":20},{"cell_type":"code","source":"plt.figure(figsize=(12, 12))\nsns.heatmap(dataset.corr(), vmin=-1.0, center=0, cmap='RdBu_r', annot=True)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:30:55.963085Z","iopub.execute_input":"2025-04-15T16:30:55.963682Z","iopub.status.idle":"2025-04-15T16:30:56.339665Z","shell.execute_reply.started":"2025-04-15T16:30:55.963660Z","shell.execute_reply":"2025-04-15T16:30:56.338960Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/matplotlib/colors.py:721: RuntimeWarning: invalid value encountered in less\n  xa[xa < 0] = -1\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 1200x1200 with 2 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":21},{"cell_type":"markdown","source":"feature engineering - pca","metadata":{}},{"cell_type":"code","source":"X = dataset.drop(\"Outcome\", axis = 1)\nX","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:38:22.936045Z","iopub.execute_input":"2025-04-15T16:38:22.936707Z","iopub.status.idle":"2025-04-15T16:38:22.948995Z","shell.execute_reply.started":"2025-04-15T16:38:22.936685Z","shell.execute_reply":"2025-04-15T16:38:22.948265Z"}},"outputs":[{"execution_count":22,"output_type":"execute_result","data":{"text/plain":"     Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n0              6      148             72             35        0  33.6   \n1              1       85             66             29        0  26.6   \n2              8      183             64              0        0  23.3   \n3              1       89             66             23       94  28.1   \n4              0      137             40             35      168  43.1   \n..           ...      ...            ...            ...      ...   ...   \n763           10      101             76             48      180  32.9   \n764            2      122             70             27        0  36.8   \n765            5      121             72             23      112  26.2   \n766            1      126             60              0        0  30.1   \n767            1       93             70             31        0  30.4   \n\n     DiabetesPedigreeFunction  Age  \n0                       0.627   50  \n1                       0.351   31  \n2                       0.672   32  \n3                       0.167   21  \n4                       2.288   33  \n..                        ...  ...  \n763                     0.171   63  \n764                     0.340   27  \n765                     0.245   30  \n766                     0.349   47  \n767                     0.315   23  \n\n[768 rows x 8 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>6</td>\n      <td>148</td>\n      <td>72</td>\n      <td>35</td>\n      <td>0</td>\n      <td>33.6</td>\n      <td>0.627</td>\n      <td>50</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>85</td>\n      <td>66</td>\n      <td>29</td>\n      <td>0</td>\n      <td>26.6</td>\n      <td>0.351</td>\n      <td>31</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>8</td>\n      <td>183</td>\n      <td>64</td>\n      <td>0</td>\n      <td>0</td>\n      <td>23.3</td>\n      <td>0.672</td>\n      <td>32</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1</td>\n      <td>89</td>\n      <td>66</td>\n      <td>23</td>\n      <td>94</td>\n      <td>28.1</td>\n      <td>0.167</td>\n      <td>21</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0</td>\n      <td>137</td>\n      <td>40</td>\n      <td>35</td>\n      <td>168</td>\n      <td>43.1</td>\n      <td>2.288</td>\n      <td>33</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    </tr>\n    <tr>\n      <th>763</th>\n      <td>10</td>\n      <td>101</td>\n      <td>76</td>\n      <td>48</td>\n      <td>180</td>\n      <td>32.9</td>\n      <td>0.171</td>\n      <td>63</td>\n    </tr>\n    <tr>\n      <th>764</th>\n      <td>2</td>\n      <td>122</td>\n      <td>70</td>\n      <td>27</td>\n      <td>0</td>\n      <td>36.8</td>\n      <td>0.340</td>\n      <td>27</td>\n    </tr>\n    <tr>\n      <th>765</th>\n      <td>5</td>\n      <td>121</td>\n      <td>72</td>\n      <td>23</td>\n      <td>112</td>\n      <td>26.2</td>\n      <td>0.245</td>\n      <td>30</td>\n    </tr>\n    <tr>\n      <th>766</th>\n      <td>1</td>\n      <td>126</td>\n      <td>60</td>\n      <td>0</td>\n      <td>0</td>\n      <td>30.1</td>\n      <td>0.349</td>\n      <td>47</td>\n    </tr>\n    <tr>\n      <th>767</th>\n      <td>1</td>\n      <td>93</td>\n      <td>70</td>\n      <td>31</td>\n      <td>0</td>\n      <td>30.4</td>\n      <td>0.315</td>\n      <td>23</td>\n    </tr>\n  </tbody>\n</table>\n<p>768 rows × 8 columns</p>\n</div>"},"metadata":{}}],"execution_count":22},{"cell_type":"code","source":"Y = dataset[\"Outcome\"]\nY","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:39:11.671936Z","iopub.execute_input":"2025-04-15T16:39:11.672212Z","iopub.status.idle":"2025-04-15T16:39:11.677930Z","shell.execute_reply.started":"2025-04-15T16:39:11.672192Z","shell.execute_reply":"2025-04-15T16:39:11.677318Z"}},"outputs":[{"execution_count":28,"output_type":"execute_result","data":{"text/plain":"0      1\n1      0\n2      1\n3      0\n4      1\n      ..\n763    0\n764    0\n765    0\n766    1\n767    0\nName: Outcome, Length: 768, dtype: int64"},"metadata":{}}],"execution_count":28},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\n\nscaler = StandardScaler()\nscaler.fit(X)\n\nX_scaled = scaler.transform(X)\nX_scaled","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:39:32.790022Z","iopub.execute_input":"2025-04-15T16:39:32.790294Z","iopub.status.idle":"2025-04-15T16:39:32.854350Z","shell.execute_reply.started":"2025-04-15T16:39:32.790276Z","shell.execute_reply":"2025-04-15T16:39:32.853827Z"}},"outputs":[{"execution_count":29,"output_type":"execute_result","data":{"text/plain":"array([[ 0.63994726,  0.84832379,  0.14964075, ...,  0.20401277,\n         0.46849198,  1.4259954 ],\n       [-0.84488505, -1.12339636, -0.16054575, ..., -0.68442195,\n        -0.36506078, -0.19067191],\n       [ 1.23388019,  1.94372388, -0.26394125, ..., -1.10325546,\n         0.60439732, -0.10558415],\n       ...,\n       [ 0.3429808 ,  0.00330087,  0.14964075, ..., -0.73518964,\n        -0.68519336, -0.27575966],\n       [-0.84488505,  0.1597866 , -0.47073225, ..., -0.24020459,\n        -0.37110101,  1.17073215],\n       [-0.84488505, -0.8730192 ,  0.04624525, ..., -0.20212881,\n        -0.47378505, -0.87137393]])"},"metadata":{}}],"execution_count":29},{"cell_type":"code","source":"\nX_scaled = pd.DataFrame(X_scaled)\nX_scaled.columns = X.columns\nX_scaled","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:40:04.972743Z","iopub.execute_input":"2025-04-15T16:40:04.973488Z","iopub.status.idle":"2025-04-15T16:40:04.985414Z","shell.execute_reply.started":"2025-04-15T16:40:04.973465Z","shell.execute_reply":"2025-04-15T16:40:04.984820Z"}},"outputs":[{"execution_count":30,"output_type":"execute_result","data":{"text/plain":"     Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n0       0.639947  0.848324       0.149641       0.907270 -0.692891  0.204013   \n1      -0.844885 -1.123396      -0.160546       0.530902 -0.692891 -0.684422   \n2       1.233880  1.943724      -0.263941      -1.288212 -0.692891 -1.103255   \n3      -0.844885 -0.998208      -0.160546       0.154533  0.123302 -0.494043   \n4      -1.141852  0.504055      -1.504687       0.907270  0.765836  1.409746   \n..           ...       ...            ...            ...       ...       ...   \n763     1.827813 -0.622642       0.356432       1.722735  0.870031  0.115169   \n764    -0.547919  0.034598       0.046245       0.405445 -0.692891  0.610154   \n765     0.342981  0.003301       0.149641       0.154533  0.279594 -0.735190   \n766    -0.844885  0.159787      -0.470732      -1.288212 -0.692891 -0.240205   \n767    -0.844885 -0.873019       0.046245       0.656358 -0.692891 -0.202129   \n\n     DiabetesPedigreeFunction       Age  \n0                    0.468492  1.425995  \n1                   -0.365061 -0.190672  \n2                    0.604397 -0.105584  \n3                   -0.920763 -1.041549  \n4                    5.484909 -0.020496  \n..                        ...       ...  \n763                 -0.908682  2.532136  \n764                 -0.398282 -0.531023  \n765                 -0.685193 -0.275760  \n766                 -0.371101  1.170732  \n767                 -0.473785 -0.871374  \n\n[768 rows x 8 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Pregnancies</th>\n      <th>Glucose</th>\n      <th>BloodPressure</th>\n      <th>SkinThickness</th>\n      <th>Insulin</th>\n      <th>BMI</th>\n      <th>DiabetesPedigreeFunction</th>\n      <th>Age</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.639947</td>\n      <td>0.848324</td>\n      <td>0.149641</td>\n      <td>0.907270</td>\n      <td>-0.692891</td>\n      <td>0.204013</td>\n      <td>0.468492</td>\n      <td>1.425995</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-0.844885</td>\n      <td>-1.123396</td>\n      <td>-0.160546</td>\n      <td>0.530902</td>\n      <td>-0.692891</td>\n      <td>-0.684422</td>\n      <td>-0.365061</td>\n      <td>-0.190672</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1.233880</td>\n      <td>1.943724</td>\n      <td>-0.263941</td>\n      <td>-1.288212</td>\n      <td>-0.692891</td>\n      <td>-1.103255</td>\n      <td>0.604397</td>\n      <td>-0.105584</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-0.844885</td>\n      <td>-0.998208</td>\n      <td>-0.160546</td>\n      <td>0.154533</td>\n      <td>0.123302</td>\n      <td>-0.494043</td>\n      <td>-0.920763</td>\n      <td>-1.041549</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>-1.141852</td>\n      <td>0.504055</td>\n      <td>-1.504687</td>\n      <td>0.907270</td>\n      <td>0.765836</td>\n      <td>1.409746</td>\n      <td>5.484909</td>\n      <td>-0.020496</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    </tr>\n    <tr>\n      <th>763</th>\n      <td>1.827813</td>\n      <td>-0.622642</td>\n      <td>0.356432</td>\n      <td>1.722735</td>\n      <td>0.870031</td>\n      <td>0.115169</td>\n      <td>-0.908682</td>\n      <td>2.532136</td>\n    </tr>\n    <tr>\n      <th>764</th>\n      <td>-0.547919</td>\n      <td>0.034598</td>\n      <td>0.046245</td>\n      <td>0.405445</td>\n      <td>-0.692891</td>\n      <td>0.610154</td>\n      <td>-0.398282</td>\n      <td>-0.531023</td>\n    </tr>\n    <tr>\n      <th>765</th>\n      <td>0.342981</td>\n      <td>0.003301</td>\n      <td>0.149641</td>\n      <td>0.154533</td>\n      <td>0.279594</td>\n      <td>-0.735190</td>\n      <td>-0.685193</td>\n      <td>-0.275760</td>\n    </tr>\n    <tr>\n      <th>766</th>\n      <td>-0.844885</td>\n      <td>0.159787</td>\n      <td>-0.470732</td>\n      <td>-1.288212</td>\n      <td>-0.692891</td>\n      <td>-0.240205</td>\n      <td>-0.371101</td>\n      <td>1.170732</td>\n    </tr>\n    <tr>\n      <th>767</th>\n      <td>-0.844885</td>\n      <td>-0.873019</td>\n      <td>0.046245</td>\n      <td>0.656358</td>\n      <td>-0.692891</td>\n      <td>-0.202129</td>\n      <td>-0.473785</td>\n      <td>-0.871374</td>\n    </tr>\n  </tbody>\n</table>\n<p>768 rows × 8 columns</p>\n</div>"},"metadata":{}}],"execution_count":30},{"cell_type":"code","source":"from sklearn.decomposition import PCA\npca = PCA()\nX_pca = pca.fit_transform(X_scaled)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:41:22.125617Z","iopub.execute_input":"2025-04-15T16:41:22.126218Z","iopub.status.idle":"2025-04-15T16:41:22.363324Z","shell.execute_reply.started":"2025-04-15T16:41:22.126197Z","shell.execute_reply":"2025-04-15T16:41:22.362753Z"}},"outputs":[],"execution_count":32},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\n# ✅ Cumulative Explained Variance Calculate Karo\ncumulative_variance = pca.explained_variance_ratio_.cumsum()\n\n# ✅ Plot Banao\nplt.figure(figsize=(8,5))\nplt.plot(range(1, len(cumulative_variance)+1), cumulative_variance, marker='o', linestyle='--')\nplt.xlabel('Number of Components')\nplt.ylabel('Cumulative Explained Variance')\nplt.title('Explained Variance vs Number of Components')\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:41:28.336076Z","iopub.execute_input":"2025-04-15T16:41:28.336641Z","iopub.status.idle":"2025-04-15T16:41:28.495261Z","shell.execute_reply.started":"2025-04-15T16:41:28.336620Z","shell.execute_reply":"2025-04-15T16:41:28.494540Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x500 with 1 Axes>","image/png":"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\n"},"metadata":{}}],"execution_count":33},{"cell_type":"code","source":"print(pca.explained_variance_ratio_.cumsum())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:42:16.935639Z","iopub.execute_input":"2025-04-15T16:42:16.936329Z","iopub.status.idle":"2025-04-15T16:42:16.940410Z","shell.execute_reply.started":"2025-04-15T16:42:16.936306Z","shell.execute_reply":"2025-04-15T16:42:16.939829Z"}},"outputs":[{"name":"stdout","text":"[0.26179749 0.47819876 0.60690249 0.71634362 0.81163667 0.89696522\n 0.94944224 1.        ]\n","output_type":"stream"}],"execution_count":34},{"cell_type":"code","source":"pca = PCA(n_components = 6)\nX_reduced = pca.fit_transform(X_scaled)\nX_reduced","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:42:31.977326Z","iopub.execute_input":"2025-04-15T16:42:31.978002Z","iopub.status.idle":"2025-04-15T16:42:31.998973Z","shell.execute_reply.started":"2025-04-15T16:42:31.977980Z","shell.execute_reply":"2025-04-15T16:42:31.998351Z"}},"outputs":[{"execution_count":35,"output_type":"execute_result","data":{"text/plain":"array([[ 1.06850273,  1.23489499,  0.09592984,  0.4969902 , -0.10998491,\n         0.35718251],\n       [-1.12168331, -0.73385167, -0.71293816,  0.28505622, -0.38950719,\n        -0.40632934],\n       [-0.39647671,  1.59587594,  1.76067844, -0.07039464,  0.90647385,\n        -0.04001752],\n       ...,\n       [-0.28347525,  0.09706503, -0.07719194, -0.68756106, -0.52300926,\n        -0.53826993],\n       [-1.06032431,  0.83706234,  0.42503045, -0.20449292,  0.95759303,\n         0.15330712],\n       [-0.83989172, -1.15175485, -1.00917817,  0.0869288 , -0.08265082,\n        -0.15009639]])"},"metadata":{}}],"execution_count":35},{"cell_type":"code","source":"X_reduced = pd.DataFrame(X_reduced, columns=[f'PC{i+1}' for i in range(6)])\nX_reduced","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:42:57.686900Z","iopub.execute_input":"2025-04-15T16:42:57.687445Z","iopub.status.idle":"2025-04-15T16:42:57.699040Z","shell.execute_reply.started":"2025-04-15T16:42:57.687421Z","shell.execute_reply":"2025-04-15T16:42:57.698321Z"}},"outputs":[{"execution_count":36,"output_type":"execute_result","data":{"text/plain":"          PC1       PC2       PC3       PC4       PC5       PC6\n0    1.068503  1.234895  0.095930  0.496990 -0.109985  0.357183\n1   -1.121683 -0.733852 -0.712938  0.285056 -0.389507 -0.406329\n2   -0.396477  1.595876  1.760678 -0.070395  0.906474 -0.040018\n3   -1.115781 -1.271241 -0.663729 -0.579123 -0.356060 -0.412520\n4    2.359334 -2.184819  2.963107  4.033099  0.592684  1.078341\n..        ...       ...       ...       ...       ...       ...\n763  1.562085  1.923150 -0.867408 -0.390926 -2.541527 -0.077322\n764 -0.100405 -0.614181 -0.764353 -0.134859  0.499290  0.529339\n765 -0.283475  0.097065 -0.077192 -0.687561 -0.523009 -0.538270\n766 -1.060324  0.837062  0.425030 -0.204493  0.957593  0.153307\n767 -0.839892 -1.151755 -1.009178  0.086929 -0.082651 -0.150096\n\n[768 rows x 6 columns]","text/html":"<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>PC1</th>\n      <th>PC2</th>\n      <th>PC3</th>\n      <th>PC4</th>\n      <th>PC5</th>\n      <th>PC6</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1.068503</td>\n      <td>1.234895</td>\n      <td>0.095930</td>\n      <td>0.496990</td>\n      <td>-0.109985</td>\n      <td>0.357183</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>-1.121683</td>\n      <td>-0.733852</td>\n      <td>-0.712938</td>\n      <td>0.285056</td>\n      <td>-0.389507</td>\n      <td>-0.406329</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>-0.396477</td>\n      <td>1.595876</td>\n      <td>1.760678</td>\n      <td>-0.070395</td>\n      <td>0.906474</td>\n      <td>-0.040018</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>-1.115781</td>\n      <td>-1.271241</td>\n      <td>-0.663729</td>\n      <td>-0.579123</td>\n      <td>-0.356060</td>\n      <td>-0.412520</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>2.359334</td>\n      <td>-2.184819</td>\n      <td>2.963107</td>\n      <td>4.033099</td>\n      <td>0.592684</td>\n      <td>1.078341</td>\n    </tr>\n    <tr>\n      <th>...</th>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n      <td>...</td>\n    </tr>\n    <tr>\n      <th>763</th>\n      <td>1.562085</td>\n      <td>1.923150</td>\n      <td>-0.867408</td>\n      <td>-0.390926</td>\n      <td>-2.541527</td>\n      <td>-0.077322</td>\n    </tr>\n    <tr>\n      <th>764</th>\n      <td>-0.100405</td>\n      <td>-0.614181</td>\n      <td>-0.764353</td>\n      <td>-0.134859</td>\n      <td>0.499290</td>\n      <td>0.529339</td>\n    </tr>\n    <tr>\n      <th>765</th>\n      <td>-0.283475</td>\n      <td>0.097065</td>\n      <td>-0.077192</td>\n      <td>-0.687561</td>\n      <td>-0.523009</td>\n      <td>-0.538270</td>\n    </tr>\n    <tr>\n      <th>766</th>\n      <td>-1.060324</td>\n      <td>0.837062</td>\n      <td>0.425030</td>\n      <td>-0.204493</td>\n      <td>0.957593</td>\n      <td>0.153307</td>\n    </tr>\n    <tr>\n      <th>767</th>\n      <td>-0.839892</td>\n      <td>-1.151755</td>\n      <td>-1.009178</td>\n      <td>0.086929</td>\n      <td>-0.082651</td>\n      <td>-0.150096</td>\n    </tr>\n  </tbody>\n</table>\n<p>768 rows × 6 columns</p>\n</div>"},"metadata":{}}],"execution_count":36},{"cell_type":"markdown","source":"split data to train and test","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import StandardScaler","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:46:14.449238Z","iopub.execute_input":"2025-04-15T16:46:14.449504Z","iopub.status.idle":"2025-04-15T16:46:14.453351Z","shell.execute_reply.started":"2025-04-15T16:46:14.449484Z","shell.execute_reply":"2025-04-15T16:46:14.452602Z"}},"outputs":[],"execution_count":38},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X_reduced, Y, random_state=42, test_size=0.2)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:46:19.411327Z","iopub.execute_input":"2025-04-15T16:46:19.411897Z","iopub.status.idle":"2025-04-15T16:46:19.417474Z","shell.execute_reply.started":"2025-04-15T16:46:19.411873Z","shell.execute_reply":"2025-04-15T16:46:19.416808Z"}},"outputs":[],"execution_count":39},{"cell_type":"code","source":"scaler = StandardScaler()\nX_train_scaled = scaler.fit_transform(X_train)\nX_test_scaled = scaler.transform(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:47:31.718926Z","iopub.execute_input":"2025-04-15T16:47:31.719371Z","iopub.status.idle":"2025-04-15T16:47:31.728944Z","shell.execute_reply.started":"2025-04-15T16:47:31.719342Z","shell.execute_reply":"2025-04-15T16:47:31.728105Z"}},"outputs":[],"execution_count":40},{"cell_type":"code","source":"print(X_train.shape)  # Check the shape of training data\nprint(X_test.shape)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:47:57.506581Z","iopub.execute_input":"2025-04-15T16:47:57.506907Z","iopub.status.idle":"2025-04-15T16:47:57.510982Z","shell.execute_reply.started":"2025-04-15T16:47:57.506883Z","shell.execute_reply":"2025-04-15T16:47:57.510243Z"}},"outputs":[{"name":"stdout","text":"(614, 6)\n(154, 6)\n","output_type":"stream"}],"execution_count":41},{"cell_type":"markdown","source":"GaussianNB model","metadata":{}},{"cell_type":"code","source":"from sklearn.naive_bayes import GaussianNB\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder\nfrom sklearn.metrics import classification_report","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:49:14.437441Z","iopub.execute_input":"2025-04-15T16:49:14.438099Z","iopub.status.idle":"2025-04-15T16:49:14.452517Z","shell.execute_reply.started":"2025-04-15T16:49:14.438078Z","shell.execute_reply":"2025-04-15T16:49:14.451852Z"}},"outputs":[],"execution_count":42},{"cell_type":"code","source":"model = GaussianNB()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:49:31.009029Z","iopub.execute_input":"2025-04-15T16:49:31.009314Z","iopub.status.idle":"2025-04-15T16:49:31.013019Z","shell.execute_reply.started":"2025-04-15T16:49:31.009295Z","shell.execute_reply":"2025-04-15T16:49:31.012224Z"}},"outputs":[],"execution_count":43},{"cell_type":"code","source":"model.fit(X_train_scaled, y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:49:54.725191Z","iopub.execute_input":"2025-04-15T16:49:54.725747Z","iopub.status.idle":"2025-04-15T16:49:54.737093Z","shell.execute_reply.started":"2025-04-15T16:49:54.725726Z","shell.execute_reply":"2025-04-15T16:49:54.736473Z"}},"outputs":[{"execution_count":44,"output_type":"execute_result","data":{"text/plain":"GaussianNB()","text/html":"<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GaussianNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">GaussianNB</label><div class=\"sk-toggleable__content\"><pre>GaussianNB()</pre></div></div></div></div></div>"},"metadata":{}}],"execution_count":44},{"cell_type":"code","source":"y_pred = model.predict(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:50:17.468232Z","iopub.execute_input":"2025-04-15T16:50:17.468888Z","iopub.status.idle":"2025-04-15T16:50:17.475504Z","shell.execute_reply.started":"2025-04-15T16:50:17.468865Z","shell.execute_reply":"2025-04-15T16:50:17.474928Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/sklearn/base.py:432: UserWarning: X has feature names, but GaussianNB was fitted without feature names\n  warnings.warn(\n","output_type":"stream"}],"execution_count":45},{"cell_type":"code","source":"print(classification_report(y_test, y_pred))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:50:47.732180Z","iopub.execute_input":"2025-04-15T16:50:47.732455Z","iopub.status.idle":"2025-04-15T16:50:47.743551Z","shell.execute_reply.started":"2025-04-15T16:50:47.732435Z","shell.execute_reply":"2025-04-15T16:50:47.742961Z"}},"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.80      0.83      0.82        99\n           1       0.67      0.64      0.65        55\n\n    accuracy                           0.76       154\n   macro avg       0.74      0.73      0.74       154\nweighted avg       0.76      0.76      0.76       154\n\n","output_type":"stream"}],"execution_count":47},{"cell_type":"markdown","source":"decision tree model","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.metrics import mean_absolute_error, accuracy_score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:52:38.493406Z","iopub.execute_input":"2025-04-15T16:52:38.494009Z","iopub.status.idle":"2025-04-15T16:52:38.573372Z","shell.execute_reply.started":"2025-04-15T16:52:38.493989Z","shell.execute_reply":"2025-04-15T16:52:38.572843Z"}},"outputs":[],"execution_count":48},{"cell_type":"code","source":"dt_model = DecisionTreeClassifier(\n    criterion='entropy',\n    random_state=42\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:53:45.139423Z","iopub.execute_input":"2025-04-15T16:53:45.139694Z","iopub.status.idle":"2025-04-15T16:53:45.143281Z","shell.execute_reply.started":"2025-04-15T16:53:45.139676Z","shell.execute_reply":"2025-04-15T16:53:45.142605Z"}},"outputs":[],"execution_count":50},{"cell_type":"code","source":"dt_model.fit(X_train_scaled, y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:53:49.599511Z","iopub.execute_input":"2025-04-15T16:53:49.599831Z","iopub.status.idle":"2025-04-15T16:53:49.611510Z","shell.execute_reply.started":"2025-04-15T16:53:49.599781Z","shell.execute_reply":"2025-04-15T16:53:49.610678Z"}},"outputs":[{"execution_count":51,"output_type":"execute_result","data":{"text/plain":"DecisionTreeClassifier(criterion='entropy', random_state=42)","text/html":"<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(criterion=&#x27;entropy&#x27;, random_state=42)</pre></div></div></div></div></div>"},"metadata":{}}],"execution_count":51},{"cell_type":"code","source":"y_pred_dt = dt_model.predict(X_test_scaled)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:54:10.762502Z","iopub.execute_input":"2025-04-15T16:54:10.762766Z","iopub.status.idle":"2025-04-15T16:54:10.766736Z","shell.execute_reply.started":"2025-04-15T16:54:10.762747Z","shell.execute_reply":"2025-04-15T16:54:10.765958Z"}},"outputs":[],"execution_count":53},{"cell_type":"code","source":"print(classification_report(y_test, y_pred_dt))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:54:25.274878Z","iopub.execute_input":"2025-04-15T16:54:25.275430Z","iopub.status.idle":"2025-04-15T16:54:25.286280Z","shell.execute_reply.started":"2025-04-15T16:54:25.275408Z","shell.execute_reply":"2025-04-15T16:54:25.285553Z"}},"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.76      0.71      0.73        99\n           1       0.53      0.60      0.56        55\n\n    accuracy                           0.67       154\n   macro avg       0.65      0.65      0.65       154\nweighted avg       0.68      0.67      0.67       154\n\n","output_type":"stream"}],"execution_count":54},{"cell_type":"raw","source":"random forest model","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix, classification_report","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:56:16.249248Z","iopub.execute_input":"2025-04-15T16:56:16.249786Z","iopub.status.idle":"2025-04-15T16:56:16.343926Z","shell.execute_reply.started":"2025-04-15T16:56:16.249762Z","shell.execute_reply":"2025-04-15T16:56:16.343375Z"}},"outputs":[],"execution_count":55},{"cell_type":"code","source":"rf_model = RandomForestClassifier(\n    n_estimators=400,\n    criterion='entropy',\n    random_state=42,\n    verbose=1\n\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:56:40.968441Z","iopub.execute_input":"2025-04-15T16:56:40.968981Z","iopub.status.idle":"2025-04-15T16:56:40.972730Z","shell.execute_reply.started":"2025-04-15T16:56:40.968957Z","shell.execute_reply":"2025-04-15T16:56:40.971959Z"}},"outputs":[],"execution_count":56},{"cell_type":"code","source":"rf_model.fit(X_train_scaled, y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:56:59.513413Z","iopub.execute_input":"2025-04-15T16:56:59.513682Z","iopub.status.idle":"2025-04-15T16:57:00.368679Z","shell.execute_reply.started":"2025-04-15T16:56:59.513663Z","shell.execute_reply":"2025-04-15T16:57:00.367940Z"}},"outputs":[{"name":"stderr","text":"[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.1s\n[Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    0.4s\n","output_type":"stream"},{"execution_count":57,"output_type":"execute_result","data":{"text/plain":"RandomForestClassifier(criterion='entropy', n_estimators=400, random_state=42,\n                       verbose=1)","text/html":"<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=400, random_state=42,\n                       verbose=1)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(criterion=&#x27;entropy&#x27;, n_estimators=400, random_state=42,\n                       verbose=1)</pre></div></div></div></div></div>"},"metadata":{}}],"execution_count":57},{"cell_type":"code","source":"y_pred_rf = rf_model.predict(X_test_scaled)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:57:25.208920Z","iopub.execute_input":"2025-04-15T16:57:25.209212Z","iopub.status.idle":"2025-04-15T16:57:25.239677Z","shell.execute_reply.started":"2025-04-15T16:57:25.209190Z","shell.execute_reply":"2025-04-15T16:57:25.238992Z"}},"outputs":[{"name":"stderr","text":"[Parallel(n_jobs=1)]: Done  49 tasks      | elapsed:    0.0s\n[Parallel(n_jobs=1)]: Done 199 tasks      | elapsed:    0.0s\n","output_type":"stream"}],"execution_count":58},{"cell_type":"code","source":"print(classification_report(y_test, y_pred_rf))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:57:40.101786Z","iopub.execute_input":"2025-04-15T16:57:40.102084Z","iopub.status.idle":"2025-04-15T16:57:40.113521Z","shell.execute_reply.started":"2025-04-15T16:57:40.102065Z","shell.execute_reply":"2025-04-15T16:57:40.112759Z"}},"outputs":[{"name":"stdout","text":"              precision    recall  f1-score   support\n\n           0       0.77      0.76      0.77        99\n           1       0.58      0.60      0.59        55\n\n    accuracy                           0.70       154\n   macro avg       0.68      0.68      0.68       154\nweighted avg       0.70      0.70      0.70       154\n\n","output_type":"stream"}],"execution_count":59},{"cell_type":"markdown","source":"ANN model","metadata":{}},{"cell_type":"code","source":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:58:17.371064Z","iopub.execute_input":"2025-04-15T16:58:17.371340Z","iopub.status.idle":"2025-04-15T16:58:29.983555Z","shell.execute_reply.started":"2025-04-15T16:58:17.371321Z","shell.execute_reply":"2025-04-15T16:58:29.983006Z"}},"outputs":[{"name":"stderr","text":"2025-04-15 16:58:18.788957: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\nWARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nE0000 00:00:1744736298.981835      31 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\nE0000 00:00:1744736299.038917      31 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n","output_type":"stream"}],"execution_count":60},{"cell_type":"code","source":"model = keras.Sequential([\n    layers.BatchNormalization(input_shape=[X_train.shape[1]]),\n    layers.Dense(512, activation='relu'),\n    layers.Dropout(rate=0.3),\n    layers.Dense(512, activation='relu'),\n    layers.Dense(512, activation='relu'),\n    layers.Dense(512, activation='relu'),\n    layers.Dense(1, activation='sigmoid')\n])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:58:48.351294Z","iopub.execute_input":"2025-04-15T16:58:48.352153Z","iopub.status.idle":"2025-04-15T16:58:50.721824Z","shell.execute_reply.started":"2025-04-15T16:58:48.352127Z","shell.execute_reply":"2025-04-15T16:58:50.721276Z"}},"outputs":[{"name":"stderr","text":"/usr/local/lib/python3.11/dist-packages/keras/src/layers/normalization/batch_normalization.py:143: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n  super().__init__(**kwargs)\nI0000 00:00:1744736329.474592      31 gpu_device.cc:2022] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 15513 MB memory:  -> device: 0, name: Tesla P100-PCIE-16GB, pci bus id: 0000:00:04.0, compute capability: 6.0\n","output_type":"stream"}],"execution_count":61},{"cell_type":"code","source":"model.compile(\n    optimizer='adam',\n    loss='binary_crossentropy',\n    metrics=['binary_accuracy']\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:59:12.477531Z","iopub.execute_input":"2025-04-15T16:59:12.478153Z","iopub.status.idle":"2025-04-15T16:59:12.491362Z","shell.execute_reply.started":"2025-04-15T16:59:12.478128Z","shell.execute_reply":"2025-04-15T16:59:12.490595Z"}},"outputs":[],"execution_count":62},{"cell_type":"code","source":"early_stopping = keras.callbacks.EarlyStopping(\n    patience=5,\n    min_delta=0.01,\n    restore_best_weights=True,\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T16:59:35.752708Z","iopub.execute_input":"2025-04-15T16:59:35.753300Z","iopub.status.idle":"2025-04-15T16:59:35.757167Z","shell.execute_reply.started":"2025-04-15T16:59:35.753275Z","shell.execute_reply":"2025-04-15T16:59:35.756284Z"}},"outputs":[],"execution_count":63},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom IPython.display import clear_output\n\nclass LivePlotCallback(tf.keras.callbacks.Callback):\n    def __init__(self):\n        self.epochs = []\n        self.losses = []\n        self.val_losses = []\n    def on_epoch_end(self, epoch, logs=None):\n        self.epochs.append(epoch)\n        self.losses.append(logs['loss'])\n        self.val_losses.append(logs['val_loss'])\n        clear_output(wait=True)\n        plt.figure(figsize=(10, 6))\n        plt.plot(self.epochs, self.losses, label='Training Loss')\n        plt.plot(self.epochs, self.val_losses, label='Validation Loss')\n        plt.xlabel('Epoch')\n        plt.ylabel('Loss')\n        plt.legend()\n        plt.show()\n\nlive_plot = LivePlotCallback()\n\nhistory = model.fit(\n    X_train,\n    y_train,\n    validation_split=0.2,\n    batch_size=50,\n    verbose=1,\n    epochs=200,\n    callbacks = [early_stopping]\n)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T17:00:31.359294Z","iopub.execute_input":"2025-04-15T17:00:31.360016Z","iopub.status.idle":"2025-04-15T17:00:38.241267Z","shell.execute_reply.started":"2025-04-15T17:00:31.359992Z","shell.execute_reply":"2025-04-15T17:00:38.240670Z"}},"outputs":[{"name":"stdout","text":"Epoch 1/200\n","output_type":"stream"},{"name":"stderr","text":"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\nI0000 00:00:1744736434.058749      98 service.cc:148] XLA service 0x7fb78000e580 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\nI0000 00:00:1744736434.059288      98 service.cc:156]   StreamExecutor device (0): Tesla P100-PCIE-16GB, Compute Capability 6.0\nI0000 00:00:1744736434.388979      98 cuda_dnn.cc:529] Loaded cuDNN version 90300\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m 1/10\u001b[0m \u001b[32m━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m37s\u001b[0m 4s/step - binary_accuracy: 0.6200 - loss: 0.6858","output_type":"stream"},{"name":"stderr","text":"I0000 00:00:1744736435.616931      98 device_compiler.h:188] Compiled cluster using XLA!  This line is logged at most once for the lifetime of the process.\n","output_type":"stream"},{"name":"stdout","text":"\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m6s\u001b[0m 219ms/step - binary_accuracy: 0.6731 - loss: 0.6101 - val_binary_accuracy: 0.7317 - val_loss: 0.4667\nEpoch 2/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - binary_accuracy: 0.7809 - loss: 0.4627 - val_binary_accuracy: 0.7398 - val_loss: 0.4724\nEpoch 3/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - binary_accuracy: 0.7929 - loss: 0.4572 - val_binary_accuracy: 0.7398 - val_loss: 0.4587\nEpoch 4/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - binary_accuracy: 0.7415 - loss: 0.5020 - val_binary_accuracy: 0.7480 - val_loss: 0.4811\nEpoch 5/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - binary_accuracy: 0.7797 - loss: 0.4732 - val_binary_accuracy: 0.7561 - val_loss: 0.4736\nEpoch 6/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - binary_accuracy: 0.7564 - loss: 0.4897 - val_binary_accuracy: 0.7398 - val_loss: 0.4505\nEpoch 7/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - binary_accuracy: 0.7682 - loss: 0.4539 - val_binary_accuracy: 0.7480 - val_loss: 0.4874\nEpoch 8/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - binary_accuracy: 0.7668 - loss: 0.4330 - val_binary_accuracy: 0.7154 - val_loss: 0.4547\nEpoch 9/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - binary_accuracy: 0.7968 - loss: 0.4273 - val_binary_accuracy: 0.7561 - val_loss: 0.4775\nEpoch 10/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - binary_accuracy: 0.7816 - loss: 0.4406 - val_binary_accuracy: 0.7154 - val_loss: 0.4611\nEpoch 11/200\n\u001b[1m10/10\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - binary_accuracy: 0.8069 - loss: 0.4396 - val_binary_accuracy: 0.7398 - val_loss: 0.4857\n","output_type":"stream"}],"execution_count":64},{"cell_type":"code","source":"history_df = pd.DataFrame(history.history)\n# Start the plot at epoch 5\nhistory_df.loc[5:, ['loss', 'val_loss']].plot()\nhistory_df.loc[5:, ['binary_accuracy', 'val_binary_accuracy']].plot()\n\nprint((\"Best Validation Loss: {:0.4f}\" +\\\n      \"\\nBest Validation Accuracy: {:0.4f}\")\\\n      .format(history_df['val_loss'].min(), \n              history_df['val_binary_accuracy'].max()))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-04-15T17:02:02.687115Z","iopub.execute_input":"2025-04-15T17:02:02.687648Z","iopub.status.idle":"2025-04-15T17:02:03.053064Z","shell.execute_reply.started":"2025-04-15T17:02:02.687625Z","shell.execute_reply":"2025-04-15T17:02:03.052272Z"}},"outputs":[{"name":"stdout","text":"Best Validation Loss: 0.4505\nBest Validation Accuracy: 0.7561\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":"<Figure 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Z8GuZKKERURExA1YlsW06H28+uNfnElzUirIl/e61ef6aiXtDi1XKGEREREp4OKT03hx1kZ+2mjalLSqXopR3epRsoivzZHlHiUsIiIiBdiamBM8PnUtsSeT8fJw8NwtNRhwfWWXrVh7pQr2BS25pIiICMaMGZOtYx0OB999991F79+zZw8Oh+OSS5hFRCT/OJ0WHy/aSdfxy4k9mUyF4gF880gLHmzpmr2ArpZGWCRbypcvz8GDBylZ0j2uhYqIFGSHE88waMZ6lu48CsDt9cJ5485rCfLL//L6+UUJi2SLp6cnYWFhef48qamp+Pj45Pnz5CfLssjIyFAlXBHJFYu3H+F/X6/j6KlU/L09GX5Hbbo2KlcgyutfjcJ5SciyIPW0PbdsVkadMGEC4eHhOJ3OLPvvuOMO+vfvz65du7jjjjsIDQ2lSJEiNGnShAULFlzV23Lw4EFuvfVW/P39qVy5Mt98803mff+9JLRo0SIcDgdRUVE0btyYgIAAWrRowbZt2zIfk50YIyIieO211+jTpw/BwcE8+OCDtGnThoEDB2Y57siRI/j4+BAVFXXZ1zFlyhQaN25MUFAQYWFh3HPPPRw+fDjLMX/99RcdO3YkODiYoKAgbrjhBnbt2pV5/6RJk6hduza+vr6UKVMmM54LXRo7efIkDoeDRYsWZXlvfvnlFxo1aoSvry9Lly7N1vuRkpLC888/T/ny5fH19aVq1apMnDgRy7KoWrUq7777bpbj161bh8PhYOfOnZd9X0SkYEtNdzLi5y30nRTN0VOp1AwL4ofHr6Nb44LRC+hqFc4/+dKS4M1we577xQPgE3jZw7p27crjjz/OwoULadu2LQDHjx9n7ty5/Pzzz5w6dYrbbruNN954A19fX7744gs6derEtm3bqFChwhWF9sorrzBy5Ejef/99pkyZQo8ePdi4cSPXXHPNRR/z0ksvMWrUKEqVKsXDDz9M//79WbZsGUC2Y3z33XcZMmQIQ4cOBWDFihUMHDiQUaNG4etrZrh/+eWXlC1bljZt2lz2daSlpfHaa69Ro0YNDh8+zKBBg7jvvvv4+eefAYiNjaVly5a0bt2a3377jeDgYJYtW0Z6ejoA48aNY9CgQYwcOZJbb72V+Pj4zNeUEy+88ALvvvsulStXplixYuzbt++y70efPn1Yvnw5H3zwAfXq1WP37t0cPXoUh8NB//79mTx5Ms8880zmc0yePJmWLVuq2rCIm4s5lsTj09awfn88AH2aV+TF267Bz7tglde/KpYbiI+PtwArPj7+vPuSk5OtzZs3W8nJyed2ppyyrKHB9txSTmX7dd1xxx1W//79M//9f//3f1Z4eLiVkZFxweNr165tjR07NvPfFStWtN57771sPRdgPfzww1n2RUZGWo888ohlWZa1e/duC7DWrl1rWZZlLVy40AKsBQsWZB7/008/WUDW9zobMXbu3DnLMcnJyVaxYsWsGTNmZO6rW7euNWzYsGy9lv9auXKlBViJiYmWZVnW4MGDrUqVKlmpqakXPD48PNx66aWXLnjff98Hy7KsEydOWIC1cOFCy7LOvTfffffdZWP79/uxbds2C7Dmz59/wWNjY2MtT09Pa8WKFZZlWVZqaqpVsmRJ67PPPrvo+S/48y8iBcr362Kt2kPmWhWf/9GqO+xXa+6mg3aHlGsu9f39X4VzhMU7wIx02PXc2dSrVy8eeOABPv74Y3x9ffnqq6/o0aMHHh4enDp1imHDhvHTTz9x8OBB0tPTSU5OJiYm5opDa968+Xn/vtyqoLp162Zulylj+lMcPnyYChUqZDvGxo0bZ/m3n58fvXv3ZtKkSXTr1o01a9awadMm5syZk63XsXr1aoYNG8b69es5ceJE5mW1mJgYatWqxbp167jhhhvw9j5/ctrhw4c5cOBA5qjW1fjv67rc+7Fu3To8PT1p1arVBc8XHh5Ohw4dmDRpEk2bNuWHH34gJSWFrl27XnWsIuJ6klLTGTbnL75etR+AJhHFeL9HA8KL+tscmT0KZ8LicGTrsozdOnXqhGVZ/PTTTzRp0oQlS5bw3nvvAfDMM88wf/583n33XapWrYq/vz933303qamp+Rrjv7/0/7mG+k+CkN0YAwPP/78YMGAA9evXZ//+/UyePJk2bdpQsWLFy8Zz+vRp2rdvT/v27fnqq68oVaoUMTExtG/fPvN5/f0v/st+qfsAPDzMtC/rX3OR0tLSLnjsf1/X5d6Pyz03mPeld+/evPfee0yePJnu3bsTEFBwe4OIyIVtOZjAwKlr2HXkNA4HPN6mGk+0qYpXAS+vfzUKZ8JSQPj5+dGlSxe++uordu7cSY0aNWjYsCEAy5Yt47777uPOO+8EzF/ve/bsuarn+/PPP+nTp0+Wfzdo0OCKz3c1MdapU4fGjRvzySefMHXqVD788MNsPW7r1q0cO3aMkSNHUr58eQBWrVqV5Zi6devy+eefk5aWdt4oS1BQEBEREURFRXHjjTeed/5SpUoBZoLyP+9NdmvTXO79qFOnDk6nk8WLF9OuXbsLnuO2224jMDCQcePGMXfuXH7//fdsPbeIFAyWZTHlz728/tMWUtOdhAb7MqZ7A5pXKWF3aLYrvKlaAdGrVy9++uknJk2aRK9evTL3V6tWjVmzZrFu3TrWr1/PPffcc96KopyaOXMmkyZNYvv27QwdOpTo6OjzVuvkxNXGOGDAAEaOHIllWZlf8pdToUIFfHx8GDt2LH///Tdz5szhtddey3LMwIEDSUhIoEePHqxatYodO3YwZcqUzBVOw4YNY9SoUXzwwQfs2LGDNWvWMHbsWMCMgjRr1oyRI0eyZcsWFi9ezMsvv5wr70dERAR9+/alf//+fPfdd+zevZtFixbx9ddfZx7j6enJfffdx+DBg6lWrdp5l/FEpOA6mZTKQ1NWM+T7v0hNd9K2Zml+ebKlkpWzlLC4uDZt2lC8eHG2bdvGPffck7l/9OjRFCtWjBYtWtCpUyfat2+fOfpypYYPH8706dOpW7cuX3zxBdOmTaNWrVpXfL6rjbFnz554eXnRs2dP/Pz8svWYUqVK8dlnnzFz5kxq1arFyJEjz1sKXKJECX777TdOnTpFq1ataNSoEZ988knmaEvfvn0ZM2YMH3/8MbVr16Zjx47s2LEj8/GTJk0iPT2dRo0a8dRTT/H6669nK7bsvB/jxo3j7rvv5tFHH6VmzZo88MADnD59Ossx999/P6mpqfTr1y9bzysirm/lnuPc9v4S5m2Ow8fTgyEda/Fp38YUD3SvulRXw2FZ2SwM4sISEhIICQkhPj6e4ODgLPedOXOG3bt3U6lSpWx/6Ylr2LNnD1WqVGHlypVXnYy5kyVLltC2bVv27dtHaGjoJY/Vz7+Ia8twWny0cCdjFmzHaUGlkoGM7dmAa8uG2B1avrjU9/d/aQ6LuJy0tDSOHTvGyy+/TLNmzZSsnJWSksKRI0cYNmwYXbt2vWyyIiKu7VD8GZ6asZY//z4OQJeGZXn1jmsp4quv5gvRJaFC4KuvvqJIkSIXvNWuXdvu8M6zbNkyypQpw8qVKxk/fnyW+5YsWXLR11KkSBGbIs4f06ZNo2LFipw8eZK3337b7nBE5CpEbYnj1vd/58+/jxPg48nobvUY3a2+kpVL0CWhQiAxMZG4uLgL3uft7Z2t5cKuIjk5mdjY2Iver4qvWennX8S1pKRnMPKXrUxetgeAa8sGM7ZnQyqVdP1SG3lBl4Qki6CgIIKCguwOI1f4+/srKRGRAmn30dMMnLqGvw4kAND/uko8f2sNfL0KUXn9q1BoEhY3GEgSyTH93Iu4hllr9vPKd5s4nZpBsQBvRnWrR5uamoeWE26fsPyzVDUpKSlblURF3ElSUhLABdsQiEjeO5WSzpDvNjFrrbmU3axyccZ0b0BYiC7R5pTbJyyenp4ULVqUw4cPAxAQEFAo2nBL4WZZFklJSRw+fJiiRYvi6akhZ5H8tik2nsenrWX30dN4OODpdtV59MaqeHroO+hKuH3CAhAWFgaQmbSIFBZFixbN/PkXkfxhWRaTl+1h5C9bSc1wEh7ix/s9G9AkorjdoRVohSJhcTgclClThtKlS1+0UZ2Iu/H29tbIikg+O346lWdnridqq/kDuX3tUN66qy5FA1Sx9moVioTlH56envoAFxGRPLF81zGemrGWuIQUfLw8eKXDNdzbrKKmIeSSQpWwiIiI5Lb0DCcfRO1g7MKdWBZULV2EsT0bcE2ZS9cVkZxRwiIiInKFDpxM5snpa1m55wQA3RuXZ+jttQjw0ddrbtM7KiIicgV+/esQz32zgfjkNIr4evFmlzrcXi/c7rDclhIWERGRHDiTlsGbP2/hi+V7AahXvihjezSgQokAmyNzb0pYREREsmnn4UQGTl3L1kOJADzUsjL/u7kGPl7qJZzXlLCIiIhchmVZzFy1n6Fz/iI5LYOSRXwY1a0+raqXsju0QkMJi4iIyCUknknjpdmbmLP+AADXVy3J6O71KB2k8vr5SQmLiIjIRazfd5LHp60l5ngSXh4O/ndzDR5qWRkPldfPd0pYRERE/sPptPh06d+8PXcb6U6LcsX8+aBnAxpWKGZ3aIWWEhYREZF/OXoqhUFfr+f37UcA6FCnDG92qUOIv7qe20kJi4iIyFlLdxzl6a/XcSQxBT9vD4Z2qk2PJuVVXt8FKGEREZFCLy3Dyej52xm/eBeWBdVDi/DhPQ2pHhpkd2hylhIWEREp1PYdT+KJ6WtZG3MSgF6RFXilYy38vNUs15UoYRERkULrpw0HeWHWBhLPpBPs58Vbd9Xl1jpl7A5LLkAJi4iIFDrJqRm8+uNmpkXHANCoYjHe71GfcsVUXt9VKWEREZFCZXtcIgOnrmF73CkcDnisdVWealcNL0+V13dlSlhERKRQsCyLqdExvPrDZlLSnZQK8mVM9/pcV7Wk3aFJNihhERERtxefnMbgWRv4eeMhAFrXKMW7XetRsoivzZFJdilhERERt7Z67wmemLaW2JPJeHs6eP6WmvS/rpLK6xcwSlhERMQtOZ0W4xbvYvT87WQ4LSqWCGBszwbULVfU7tDkCihhERERt3M44QxPf72OZTuPAXBH/XBe73wtQX4qr19QKWERERG3smjbYf739XqOnU7F39uTV++ozd2Nyqm8fgGnhEVERNxCarqTd+dtY8LvfwNwTZlgxvZsQNXSRWyOTHKDEhYRESnw9h47zRPT1rJ+fzwA97WI4IVba6q8vhtRwiIiIgXa9+tieWn2Jk6lpFM0wJu376rLzbXD7A5LcpkSFpFCJOZYEjNX7yMl3Wl3KCK5IuZYEnP/MrVVmkYU5/2e9SkT4m9zVJIXlLCIFBIxx5LoMu4Pjp5KsTsUkVzl4YDH21Tj8TZVVV7fjSlhESkEjiSm0HvSCo6eSqFa6SK0qVna7pBEcoXD4eDm2qE0rFDM7lAkjylhEXFziWfSuG9yNHuPJVG+uD9fDYikdLCf3WGJiOSIxs5E3FhKegYPTVnNXwcSKBHowxf9layISMGkhEXETWU4LZ6esY4/dh0j0MeTz/o1pVLJQLvDEhG5IleUsHz00UdERETg5+dHZGQk0dHRFz22devWOByO824dOnTIPObUqVMMHDiQcuXK4e/vT61atRg/fvyVhCYigGVZDPl+Ez9vPISPpwcT+jSmTrkQu8MSEbliOU5YZsyYwaBBgxg6dChr1qyhXr16tG/fnsOHD1/w+FmzZnHw4MHM26ZNm/D09KRr166ZxwwaNIi5c+fy5ZdfsmXLFp566ikGDhzInDlzrvyViRRiYxbs4KsVMTgc8F73+lxXtaTdIYmIXJUcJyyjR4/mgQceoF+/fpkjIQEBAUyaNOmCxxcvXpywsLDM2/z58wkICMiSsPzxxx/07duX1q1bExERwYMPPki9evUuOXIjIhc25c+9vB+1A4BX77iWDnXL2ByRiMjVy1HCkpqayurVq2nXrt25E3h40K5dO5YvX56tc0ycOJEePXoQGHjuWnqLFi2YM2cOsbGxWJbFwoUL2b59OzfffPMFz5GSkkJCQkKWm4jATxsOMuT7TQA82bYavZtVtDkiEZHckaOE5ejRo2RkZBAaGpplf2hoKIcOHbrs46Ojo9m0aRMDBgzIsn/s2LHUqlWLcuXK4ePjwy233MJHH31Ey5YtL3ieESNGEBISknkrX758Tl6GiFv6Y+dRnp6xDsuCXpEVeKpdNbtDEhHJNfm6SmjixInUqVOHpk2bZtk/duxY/vzzT+bMmcPq1asZNWoUjz32GAsWLLjgeQYPHkx8fHzmbd++ffkRvojL2hQbzwNfrCI1w8ltdcJ49Y5rcTgcdoclIpJrclQ4rmTJknh6ehIXF5dlf1xcHGFhl240dfr0aaZPn86rr76aZX9ycjIvvvgis2fPzlw5VLduXdatW8e7776b5fLTP3x9ffH19c1J6CJua8/R09w3OZrTqRk0r1yC97rXx9NDyYqIuJccjbD4+PjQqFEjoqKiMvc5nU6ioqJo3rz5JR87c+ZMUlJSuPfee7PsT0tLIy0tDQ+PrKF4enridKpBm8ilHE44c7bkfiq1w4OZ0KcRvl6edoclIpLrclyaf9CgQfTt25fGjRvTtGlTxowZw+nTp+nXrx8Affr0oWzZsowYMSLL4yZOnEjnzp0pUaJElv3BwcG0atWKZ599Fn9/fypWrMjixYv54osvGD169FW8NBH3Fp+cRt/JK9l3PJmKJQL4rF9Tgvy87Q5LRCRP5Dhh6d69O0eOHGHIkCEcOnSI+vXrM3fu3MyJuDExMeeNlmzbto2lS5cyb968C55z+vTpDB48mF69enH8+HEqVqzIG2+8wcMPP3wFL0nE/Z1Jy+CBL1ax5WACpYJ8mdI/klJBukwqIu7LYVmWZXcQVyshIYGQkBDi4+MJDg62OxyRPJWe4eTRr9Ywb3McQb5ezHioObXC9XMvIgVPTr6/1UtIpACxLIuXv9vEvM1x+Hh58EnfxkpWRKRQUMIiUoCMmred6Sv34eGAD3o0oFnlEpd/kIiIG1DCIlJAfLZsNx8u3AnAG3fW4ZZrL11KQETEnShhESkA5qw/wPAfNwPwv5uq07NpBZsjEhHJX0pYRFzckh1H+N/XpuR+3+YVGdimqt0hiYjkOyUsIi5s/b6TPDRlNWkZFp3qhTO0U22V3BeRQkkJi4iL2nXkFP0+W0lSagY3VCvJqK718FDJfREppJSwiLigQ/Fn6DMxmuOnU6lbLoRx9zbCx0u/riJSeOkTUMTFxCel0XdSNLEnk6lcMpDJ9zWhiG+Oi1KLiLgVJSwiLiQ5NYP7P1/JtrhEQoN9+bx/U0oUUcl9ERElLCIuIj3DycCpa1i19wTBfl583r8p5YsH2B2WiIhLUMIi4gIsy2LwrI1EbT2Mr5cHE+9rQs0wldwXEfmHEhYRF/DW3G3MXL0fTw8HH97TkCYRxe0OSUTEpShhEbHZp0v+ZvziXQCM6FKHm2qF2hyRiIjrUcIiYqPZa/fz+k9bAHj+lpp0a1ze5ohERFyTEhYRmyzcdphnZ24A4P7rK/Fwq8o2RyQi4rqUsIjYYE3MCR79cg3pTos7G5TlpduuUcl9EZFLUMIiks92Hk6k/2crSU7LoFX1Urx9d12V3BcRuQwlLCL56MDJZHpPjOZkUhr1yxdl3L0N8fbUr6GIyOXok1Ikn5w4nUqfSdEcjD9DlVKm5H6Aj0rui4hkhxIWkXyQlJpO/89XsvPwKcqE+PHF/ZEUC/SxOywRkQJDCYtIHkvLcPLoV2tYG3OSogHefNG/KWWL+tsdlohIgaKERSQPOZ0Wz32zgUXbjuDn7cHEvk2oFhpkd1giIgWOEhaRPGJZFm/+vIXZa2Px9HAwrlcjGlUsZndYIiIFkhIWkTzyf7//zadLdwPwzt11ubFmaZsjEhEpuJSwiOSBmav2MfKXrQC8dNs1dGlYzuaIREQKNiUsIrlsweY4Xpi1EYCHWlbmgZYquS8icrWUsIjkolV7jvPY1DVkOC3ualiOF26taXdIIiJuQQmLSC7ZdsiU3E9Jd9K2ZmlG3lVH/YFERHKJEhaRXLD/RBJ9Jq0g4Uw6jSsW48N7VHJfRCQ36RNV5CodP1tyPy4hheqhRZjYtwn+Pp52hyUi4laUsIhchdMp6fSbHM3fR05Ttqg/X/SPJCTA2+6wRETcjhIWkSuUmu7k4S9Xs35/PMUCvPm8f1PCQvzsDktExC0pYRG5Ak6nxTMz17Nkx1ECfDyZ3K8pVUsXsTssERG3pYRFJIcsy+LVHzczZ/0BvDwcjL+3EfXLF7U7LBERt6aERSSHPl60i8/+2APAqG71aFm9lL0BiYgUAkpYRHJgenQM7/y6DYChnWpxR/2yNkckIlI4KGERyaZf/zrEi7NNyf3HbqxCv+sq2RyRiEjhoYRFJBtW/H2Mx6etxWlBjybleebmGnaHJCJSqChhEbmMzQcSGPDFKlLTndxcK5TXO1+rkvsiIvlMCYvIJcQcS6Lv5GgSz6TTNKI4H/RsgJdK7ouI5Dt98opcxNFTKfSZtIIjiSnUDAvik76N8fNWyX0RETsoYRG5gMQzadw3OZo9x5IoV8yfL/o3JcRfJfdFROyihEXkP1LSM3hoymo2xSZQItCHKfdHUjpYJfdFROykhEXkXzKcFoNmrOePXccI9PHks35NqVQy0O6wREQKPSUsImdZlsWwOX/x08aD+Hh6MKFPY+qUC7E7LBERQQmLSKYPonYy5c+9OBzwXvf6XFe1pN0hiYjIWUpYRIAv/9zLewu2A/Dq7bXpULeMzRGJiMi/KWGRQu/njQd55ftNADzRthq9m0fYG5CIiJxHCctlvPbjZr5Yvofjp1PtDkXywB87j/LU9HVYFtwTWYGn21WzOyQREbkAL7sDcGWH4s8wedlunJZJXFrXKM1dDctyY83S+HqpgFhBtyk2ngenrCY1w8mt14bx2h0quS8i4qqUsFyCv48nL3Woxey1+9kUm8D8zXHM3xxHiL83HeuWoUvDcjSsUFRfcgXQnqOnuW9yNKdS0mleuQTvda+Pp4f+H0VEXJXDsizL7iCuVkJCAiEhIcTHxxMcHJwnz7HtUCKz1u7n+7UHOJRwJnN/RIkA7mxQji4Ny1K+eECePLfkrsOJZ7h73HJijidROzyY6Q82I8hPVWxFRPJbTr6/lbDkUIbTYvmuY8xas59fNh0iOS0j876mEcW5s2FZbqtTRmXcXVTCmTS6/9+fbDmYQMUSAXzzcAtKBfnaHZaISKGkhCWfnE5J59e/DjFrTSzLdh3ln3fSx8uDm2qFclfDstxQrRTe6u7rEs6kZdB3UjQrdh+nZBFfZj3SggolNComImIXJSw2OBifzHdrDzBrzX52HD6Vub9EoA+31w/nroblqB0erPkuNslwWjz21Rrm/nWIIF8vpj/UjNrhqmIrImInJSw2siyLvw4k8O2a/cxZd4Bj/1oOXT20CHc2KEfnBuGUCfG3McrCxbIsXpy9iWnRMfh4efB5v6Y0r1LC7rBERAo9JSwuIi3DyZIdR/h2TSzzN8eRmu4EwOGA66qU5M4GZbnl2jACfbVYKy+NmreNsb/txMMBH/dqyC3XqoqtiIgrUMLiguKT0/hl40FmrYkles/xzP3+3p7cem0YXRqWo3mVElpam8s+W7abYT9sBuDNO+twT2QFmyMSEZF/KGFxcfuOJzF7bSyz1uxnz7GkzP2hwb50blCWLg3KUSMsyMYI3cMP6w/wxPS1WBb876bqPN5WVWxFRFyJEpYCwrIs1sScZPba/fyw/iDxyWmZ99UOD6ZLw3LcXi9cy26vwJIdR+j/2UrSMiz6Nq/IsNtra8KziIiLUcJSAKWkZ7Bw62FmrYll4bbDpGWY/xZPDwctq5WkS8Ny3FQrFD9vtQS4nA37T9Jjwp8kpWbQsW4ZPujRAA9dahMRcTlKWAq446dT+XHDAWatiWXdvpOZ+4N8vbitThm6NCxLk4ji+hK+gL+PnOLu8cs5fjqV66uWZOJ9jdX3SUTERSlhcSO7jpxi9ppYZq+NJfZkcub+csX86dKgLHc2LEelkoE2Rug64hLO0OXjP4g9mUzdciFMfaAZRbQCS0TEZSlhcUNOp0X0nuPMWrOfnzce4lRKeuZ9DSoUpUvDcnSqW4aiAT42Rmmf+KQ0uv3fcrbFJVKpZCDfPNycEkU090dExJUpYXFzyakZzNt8iNlrY/l9+xGcZ/8HvT0dtKlZmi4Ny3FjjdL4eBWOlgBn0jLoPXEFK/ecoHSQL98+0kKNKEVECgAlLIXI4cQzzFln5rtsPpiQub9ogDed6obTpWFZ6pcv6rYrZNIznDz85WoWbDlMkJ8XXz/UnGvKFK6fARGRgkoJSyG15WACs9fG8t3aWA4npmTur1wykDsblKVzg7JuNfJgWRbPf7uBr1ftx9fLgyn3R9K0UnG7wxIRkWxSwlLIZTgtlu08yqw1+/n1rziS0zIy74usVJy7Gpbj1jphBPl52xjl1Xtr7lbGLdqFp4eD8fc24qZaoXaHJCIiOaCERTKdSkln7qZDzFqzn+V/H+Of/21fLw9urh1Gl4ZluaFqSbw8C9Z8l4lLd/Paj6bk/tt31aVbk/I2RyQiIjmVk+/vK/qW+uijj4iIiMDPz4/IyEiio6Mvemzr1q1xOBzn3Tp06JDluC1btnD77bcTEhJCYGAgTZo0ISYm5krCk38p4uvF3Y3KMfWBZix7vg3P3VKDqqWLkJLu5If1B+g3eSXNRvzGaz9u5q8D8RSE/PW7tbGZycpzt9RQsiIiUgjkeIRlxowZ9OnTh/HjxxMZGcmYMWOYOXMm27Zto3Tp0ucdf/z4cVJTUzP/fezYMerVq8enn37KfffdB8CuXbto2rQp999/Pz179iQ4OJi//vqLZs2aXfCc/6URlpyxLIuNsfHMWhPLnPUHOH763P9PzbCgzPkuocF+NkZ5YYu2HWbA56tId1r0v64Sr3S8xm0nFIuIuLs8vSQUGRlJkyZN+PDDDwFwOp2UL1+exx9/nBdeeOGyjx8zZgxDhgzh4MGDBAaagmc9evTA29ubKVOm5CSUTEpYrlxahpPF244we20s8zfHkZrhBMDDAddVLcldDctxc+1QAnzsL8C2NuYE93yyguS0DDrXD2d0t/qq9isiUoDlWcKSmppKQEAA33zzDZ07d87c37dvX06ePMn3339/2XPUqVOH5s2bM2HCBMAkPCEhITz33HMsXbqUtWvXUqlSJQYPHpzlOf4tJSWFlJRzq2ASEhIoX768EparFJ+Uxk8bDzJrzX5W7T2RuT/Qx5NbrjUtAZpVLoGnDUnCzsOJdB2/nBNJabSsXopP+zQuNHVmRETcVZ7NYTl69CgZGRmEhmZdjREaGsqhQ4cu+/jo6Gg2bdrEgAEDMvcdPnyYU6dOMXLkSG655RbmzZvHnXfeSZcuXVi8ePEFzzNixAhCQkIyb+XLaw5DbggJ8OaeyAp880gLFj/bmqfaVaNC8QBOp2bw7Zr99Pp0Bde/9Rtvzd3KjrjEfIvrwMlk+kyM5kRSGvXLF2X8vQ2VrIiIFDI5GmE5cOAAZcuW5Y8//qB58+aZ+5977jkWL17MihUrLvn4hx56iOXLl7Nhw4bzztmzZ0+mTp2auf/2228nMDCQadOmnXcejbDkH8uyWL33BLPWxvLj+gMknDnXEqBO2RC6NCxLp3rhlMyjMvgnk1LpOn45Ow6fokqpQL55uAXFAgtn+wEREXeTkxGWHE1MKFmyJJ6ensTFxWXZHxcXR1hY2CUfe/r0aaZPn86rr7563jm9vLyoVatWlv3XXHMNS5cuveC5fH198fVVn5j84HA4aBxRnMYRxRnSsRYLtx7m2zWxLNp2mI2x8WyMjeeNn7bQqnopujQsR9trSuPnnTvdkZNTM+j/2Up2HD5FmRA/vrg/UsmKiEghlaOExcfHh0aNGhEVFZU5v8TpdBIVFcXAgQMv+diZM2eSkpLCvffee945mzRpwrZt27Ls3759OxUrVsxJeJLH/Lw9ubVOGW6tU4Zjp1L4Yf0BZq+NZf3+eKK2HiZqqymP3/FsS4DGFYtd8QqetAwnj361mjUxJwnx9+aL/k0pW9Q/l1+RiIgUFDle+jFo0CD69u1L48aNadq0KWPGjOH06dP069cPgD59+lC2bFlGjBiR5XETJ06kc+fOlChR4rxzPvvss3Tv3p2WLVty4403MnfuXH744QcWLVp0Za9K8lyJIr7cd10l7ruuEjsPJzJrjWkJcCD+DNOiY5gWHUOF4gHc2aAsXRqWpWKJwGyf2+k0JfcXbjuCn7cHk+5rQrXQoDx8NSIi4uquqNLthx9+yDvvvMOhQ4eoX78+H3zwAZGRkYApFBcREcFnn32Wefy2bduoWbMm8+bN46abbrrgOSdNmsSIESPYv38/NWrUYPjw4dxxxx3ZikfLml2D02nx5+5jzFoTyy8bD3I69VxLgEYVi9GlYVk61gknJODSLQHe/HkLE37/G08PB5/0aUSbmiq5LyLijlSaX2yXlJrO/M1xfLsmlqU7juA8+1Pm4+lB22tK06VhOVpVL3Xeap8Jv+/izZ+3AjCqaz3ualQuv0MXEZF8ooRFXMrhhDN8v+4A367Zz9ZD55ZDFw/0oVPdMnRpWI665UL4dk0sz8xcD8CLt9XkwZZV7ApZRETygRIWcVmbDyQwe+1+vlt3gCOJ55amVy4ZyN7jSWQ4LR5qWZnBt11jY5QiIpIflLCIy0vPcLJ051FmrYll3uZDnEkzLQHualiOd7vWVX8gEZFCIM/qsIjkFi9PD1rXKE3rGqVJPJPG3E2HiE9Oo2+LCCUrIiJyHiUsYrsgP2+6NlZ7BRERuTg1ZBERERGXp4RFREREXJ4SFhGR3GZZcGAdZKRf9lARyR4lLCIiuW3Z+zChFcx93u5IRNyGEhYRkdx06gj8/q7ZXjUJDm+xNx4RN6GERUQkNy1+C1LPVnS2nLBgmK3hiLgLJSwiIrnl6E5YPdlsdxwDDk/YPhd2L7E1LBF3oIRFRCS3RA0HZzpUaw+N+5kbwPxXwOm0NzaRAk4Ji4hIbtgXDVvmgMMD2g0z+1o9Dz5F4MBa2Dzb1vBECjolLCIiV8uyYN4rZrt+LwitZbaLlIbrnjTbC4ZDesqFHy8il6WERUTkam39Efb9CV7+cOOLWe9r/hgUCYOTe2HlRHviE3EDSlhERK5GRtq5lUAtBkJweNb7fQLPJTG/vw3JJ/MzOhG3oYRFRORqrPkcju2EgJLQ4okLH1O/F5SqCcknYOl7+RufiJtQwiIicqVSEmHRSLPd+gXwC77wcZ5e0G642f5zHJzclz/xibgRJSwiIlfqj7Fw+ggUrwKN7rv0sdXbQ8XrISMFFr6ZL+GJuBMlLCIiVyLxkElYANoNBU/vSx/vcMBNr5rt9dPg0Ma8jU/EzShhERG5EgvfhLQkKNcUrrk9e48p1whqdwEsmD8kT8MTcTdKWEREcurwVlg7xWzf/JoZPcmutkPAwxt2/QY7o/ImPhE3pIRFRCSnFgwzjQ1rdoQKzXL22OKVoOkDZnv+UJXsF8kmJSwiIjmxZyls/8U0NvynBH9OtXwWfEMgbiNs/DpXwxNxV0pYRESy698l+BvdByWrXdl5AorDDU+b7ajXIO1MroQn4s6UsIiIZNdfs+HAGvAONHVXrkbkwxBcFhL2Q/T/5U58Im5MCYuISHakp0DU2eJv1z1pGhteDW9/aPOy2f59FCQdv7rzibg5JSwiItmxahKc2ANFQk3PoNxQtzuE1oGUePj93dw5p4ibUsIiInI5ySdh8dtm+8YXTUPD3ODhCTedHbWJnmASIhG5ICUsIiKXs2wMJB+HkjWg/r25e+6qbaHyjeBMMxNwReSClLCIiFxK/H7TsBDMaIinV+4/x03DAQds+gZi1+T++UXcgBIWEZFLWfgmpJ+BitdB9Vvy5jnK1DPzWcCU7LesvHkekQJMCYuIyMUc2gjrpprtm3JYgj+n2rwEnr6wZwnsmJ93zyNSQClhERG5mPlDAQtq32kaF+alohWg2cNnn3cIZKTn7fOJFDBKWERELmTXb7AryjQqbJtPnZWvHwT+xeDIFlg/NX+eU6SAUMIiIvJfTqcZ5QBoMgCKV86f5/UvavoMgZk7k3o6f55XpABQwiIi8l8bZ5r5K77B5xKI/NJkgLk8lHgQ/vw4f59bxIUpYRER+be0M/Db2Xoo1z8NgSXy9/m9fKHtULO99H04dSR/n1/ERSlhERH5t+j/g/h9pjFhs0fsiaF2FyhTH1IT4fe37YlBxMUoYRHXcGwXHFhrdxRS2CUdN40IAW58yTQotIOHB9x8dpRn1SQ4utOeOERciBIWsU/ScYj+BD5pC2MbwoTWsOUHu6OSwmzJKNOIMPRaqNfD3lgqtYRq7cGZfq5LtEghlgc1pkUuIT0Ftv8K66fDjnmmf8q//fAUlG8GRUrZEp4UYif2mAaEYErle3jaGg4A7YbBzvmwZQ7si4byTe2OSMQ2GmGRvGdZELMCfnwa3q0OX/eGbT+ZZKVMPWg/Ap7+y/xVm3QUfnhSpckl//32OmSkQuXWUKWt3dEYobWgfi+zPe8V/V5IoaYRFsk7x3fDhhlmNOXE7nP7g8Khbjcz5F76mnP77/w/c1lo20+wfhrUvyffQ5ZC6sBas5QZ4KZX87YEf07d+CJs/Ab2/Qlbf4JrOtodkYgtlLBI7ko+AX99Z5KUfX+e2+8dCLVuN0lKxA0XHm4PuxZuHAxRr8Ivz5vjipbPt9ClkLIsM3oBpgFhmXr2xvNfweHQ/DFY8i4sGArV24Ont91RieQ7JSxy9dJTYecC2DAdtv1ihtUBHB5meL1uD/NXoU/g5c/V4klzjv0r4fvHoPd3ZsWESF7ZMd80HPT0hTYv2x3NhV33JKz+DI7thDWfm+JyIoWMEha5MpYFB9aYkZRN30LSsXP3la4F9XpCna4QXCZn5/X0MpeGxl8PuxfDyk8g8qHcjV3kH86McyX4Ix8yFWZdkV8wtH4Bfn4GFo00I0G+QXZHJZKvlLBIzpzcd25eyrEd5/YHljbzUup2h7A6VzcHoEQVM4/g52dMt9wqbaFk1auPXeS/1k01jQb9isINg+yO5tIa3Qd/joPju+CPsWZui0ghooRFLu9MAmz+3iQqe5ac2+/lDzU7mNGUyq3N6EhuaXw/bP0R/l4Esx+C/r/m7vlFUpNg4Rtmu+WzpkuyK/P0hnZD4es+JmFp3B+CwuyOSiTf6BtALiwjHf5eaFbrbP0J0s+cuy/iBjN59prbzVB1XvDwgDs+go9bQOwqWDYGWj6TN88lhdOfH5sGg0UrQNMH7I4me665Hco1MXO8Fo2ATu/bHZFIvnFYVsFf2J+QkEBISAjx8fEEB+fRF2hhYFlwaAOsn2GWeJ4+fO6+ktVNklKnW/6u3Fk/3YyweHjDA79Bmbr599zivk4dgQ8amF49XT6Ful3tjij7Yv6ESe3NpPZH/4RSNeyOSOSK5eT7WyMsAgkHYMPX5pLP4c3n9geUMBNn63aH8Ab21Kao292U69/6o0lcHlxkutmKXI3f3zbJSpl6cO1ddkeTMxWaQc2O5ndi/lC4Z7rdEYnkCyUshVXKKfOBt34a/L0YODvQ5ukLNW4181KqtrW/3oPDYYa9Y/40ydTCN03ZdJErdWyXaSgIcPPrBXPZfLthZvn/9l9gz1KIuN7uiETynBKWwsSZYZYKr59hRi3STp+7r0Jzc8mnVmfwL2pXhBcWWNIkLTN6wR8fQI3boEKk3VFJQRU13DQUrHazaTBYEJWsZlYNrZpoit498JtrVecVyQNKWAqDuM1mJGXjTDPJ8B/FK5uibnW7QfFK9sWXHdd0NKM+66eZS0OPLMteITqRf9u30qx4c3hAuwI+Utf6BTPH68Aa+Gs2XNvF7ohE8pQSFneVGAebvjFf8Ic2ntvvV9Rcs6/Xw6w2KEh/ld0yEnYvMX2J5g+BDqPsjkgKEsuC+WdL8Ne/xzQWLMiKlDYVcBe9aUaNanYELx+7oxLJM0pY3ElaslmCvH467PoNrAyz38Pb9B+p18MMgxfUSav+RaHzR/DFHbDyU3NpqKqLdNUV17ftZ4hZbuoHtXaTomvNHzOXhU7sMfNymj1sd0QieUYJS0HndMLeZaaPz1/fm5UP/yjXxKyyufYuCChuX4y5qXJraPogRE+A7wfCo3+4fsEvsV9GmllRA9D8UQgpa288ucW3iKl4+8OTsPgtqN8T/ELsjkokTyhhKaiObDdJyoavIX7fuf1FK5ydl9LdfcvZtxsOO6NMifJfnocuE+yOSFzdmi9MK4mAEuYyijupfy8s/xiOboOl75kVRCJuSAlLQXL6mGk0uH6amWj3D98QqH2HmZRavlnBXKaZEz4BpkHipJtN7ZiaHaHW7XZHJa4qJdFUhQVo9YL7jUB4epml/tN6mF5DTQZASDm7oxLJdUpYXF3aGdg+18xL2TnfLMcEcHhCtZvMSEqNW8Hb394481v5JnD907BkFPz4lCmmVaS03VGJK/rjQzh9xKyKa3Sf3dHkjeq3QMXrzOXhhW9C54/tjkgk1ylhcUWWZQqlbZhuliueiT93X5n6ZiTl2rugSCnbQnQJrV6A7fMgbqO5ht9jasFa9SR5L/GQaRQI0Hao+66icTjgptfg0zamA3WzRyHsWrujEslVSlhcybFd5hLHhhlm1v8/gsuaWil1e0DpmraF53K8fODO8TChtVkBsm4qNOhld1TiShaNNAUSyzaGWnfYHU3eKtcIat9p/shZMBTu/dbuiERylRIWuyUdNx8wG2bAvhXn9vsUMR+wdbub7sjuPi/lSoVda1ZJRA2HuS+YyqX52ZxRXNeRbWayLcDNrxWO0be2Q2DLj7BzAexaCFVutDsikVyjhMUO6almPsr6abD9V8hINfsdHlD5RnPJp+ZtquSaXdc9afqq7I+G7x+F3t8rwRNYMMzUIqrRASq2sDua/FG8spl0u2KcKa5YabF+F8RtKGHJL5YFsavN5NlN30Ly8XP3hdaBet1NZ+SgMPtiLKg8PM2lofHXw+7fYeUnEPmQ3VGJnfYsM5cJHZ6Fb5lvy2dh3VdwaINpx1Gvu90RieQKJSx57cReUytlw3Q4tvPc/iJhULermZeiyXFXr0QVuOlV+PkZ85dllTamQZwUPv8uwd+oL5Sqbm88+S2whFlBFzUcfnvNXFr29rM7KpGrpoQlL5yJNw3W1k83ywz/4eUP13QyJfIrtzYjA5J7mgwwrQn+XmgaJPafZ2pUSOGy+TszmukdaFaSFUbNHjHtK+L3marQ1z1hd0QiV02f5rklI83071k/3QxFp585e4cDKt1g5qVc0wl8g2wN0605HHDHR/Bxc/OFtew9MzwuhUd6Kiw424X5uicgKNTeeOzi7Q83vmTmdC15Fxrc6z7tOaTQuqLZWB999BERERH4+fkRGRlJdHT0RY9t3bo1DofjvFuHDh0uePzDDz+Mw+FgzJgxVxJa/rIsOLAOfnkBRl8DU7vBX7NMslKqpqn78PQm6PuD6Q6rZCXvhZSF294224vegoMb7I1H8teqSaabd2BpaD7Q7mjsVa8HhF5rRnyXqLO5FHw5HmGZMWMGgwYNYvz48URGRjJmzBjat2/Ptm3bKF36/Eqjs2bNIjU1NfPfx44do169enTt2vW8Y2fPns2ff/5JeHh4TsPKX/GxsPFrM5pyZOu5/QElzcTZet1NgbfCsIzSFdXtDlt/hC0/mEtDDy4quB2qJfvOxJsGgAA3DjaNAQszD09Tsv/Lu8xloaYPQLEIu6MSuWI5HmEZPXo0DzzwAP369aNWrVqMHz+egIAAJk2adMHjixcvTlhYWOZt/vz5BAQEnJewxMbG8vjjj/PVV1/h7e19Za8mL6UkmsJkn98O79U2SyaPbAVPX6jdBe75Gv63FW4dCeENlKzYyeGAjmMgsBQc3gwL37A7IskPS8eY1Xclq0ODPnZH4xqqtDXz5TJS4bfX7Y5G5KrkKGFJTU1l9erVtGvX7twJPDxo164dy5cvz9Y5Jk6cSI8ePQgMPFdjxOl00rt3b5599llq16592XOkpKSQkJCQ5ZYnnBmmK/C3D8C71eG7R2D3YsAyfTtuHwvP7oCuk6F6e/B0wUSrsAosCZ3eN9vLPjCtDsR9xcfCn2f757QbrsnW/3A4zOo5MEucD6y1Nx4puKI/McUIbZSjhOXo0aNkZGQQGpp1IltoaCiHDh267OOjo6PZtGkTAwYMyLL/rbfewsvLiyeeyN5M9hEjRhASEpJ5K18+jyqbHt8NX3Yxl3/SkqBEVbjxZXhyA/T7GRr2cb/Or+6kZgeodw9gweyHIeWU3RFJXln4ppk7VqGFaQYq55SpZy6TAsx7xcy9E8mJDV+bkhFfdTUVpG2SryUQJ06cSJ06dWjatGnmvtWrV/P+++/z2Wef4cjmZZTBgwcTHx+fedu3b1/eBFyyKlS72SyXHRAFA1dBq2ehWMW8eT7JfbeOhOByZiLm/CF2RyN5Ie4vUygNCk8J/pxq87K5fL1niSnbL5JdO+abqwtg5kGVtK+uUY4SlpIlS+Lp6UlcXFyW/XFxcYSFXbpC6+nTp5k+fTr3339/lv1Llizh8OHDVKhQAS8vL7y8vNi7dy//+9//iIiIuOC5fH19CQ4OznLLM71mQodRUK6xPggLIr8Q6Hz2UsGqifqwdkfzhwAW1Opsfk/lfEUrnKv+PH+Iudwtcjn7VsLXfcCZDnW6wc1v2Po9mKOExcfHh0aNGhEVFZW5z+l0EhUVRfPmzS/52JkzZ5KSksK9996bZX/v3r3ZsGED69aty7yFh4fz7LPP8uuvv+YkPJELq9wKmp79sP5+ICSfsDceyT27Fpok1MPLNP6Ti7thEPgVNRPR1021OxpxdYe3wtSuZjpE1XamxpXNfaly/OyDBg3ik08+4fPPP2fLli088sgjnD59mn79+gHQp08fBg8efN7jJk6cSOfOnSlRokSW/SVKlODaa6/NcvP29iYsLIwaNWpc4csS+Y92w8wcpMSD8PNzdkcjucHpPHeZr/H9pj2DXJx/sXOFFBe+AalJ9sYjrit+v5m/mXwCyjaGbl+Al4/dUeU8YenevTvvvvsuQ4YMoX79+qxbt465c+dmTsSNiYnh4MGDWR6zbds2li5det7lIJF84xMAncebjtgbvzatE6Rg2/SNafDnGwytlIRmS9MHzOWhxIPnVlWJ/FvScZhyJyTEQskaZlqET+DlH5cPHJZV8KeMJyQkEBISQnx8fN7OZ5GCL+o1U6rcvzg8tgKKnF/sUAqAtDPwYROIjzGXgm74n90RFRwbZsKsAeATBE+uMyUARABST5taY7GrILgs3D8PQsrl6VPm5Pvb3gtSIvmt1fMQVscUGJvzhJZ4FlQrPzHJSlA4RD5idzQFy7V3maXOqYmw+G27oxFXkZ4KM3qbZMW/GPSenefJSk4pYZHCxcsH7vw/8PSB7b+cWw4rBUfScfj9HbPd5iVzuU+yz8MDbj5b9XbVRDi2y954xH5Op2mUuSsKvAPgnplQyvXmkCphkcIntDbc+KLZ/uUFOBljbzySM0tGmb5BpWuZLuiSc5VamhpTznSIetXuaMROlgW/vmgqIXt4QbcpUL6J3VFdkBIWKZxaPAHlI82w+HePmr8wxPWd2Gsa+YEpOe/haW88BVm74WYS+ubvTL0NKZyWjoYV48x253FQrd2lj7eREhYpnDw8zS+nd4Cp/vnPl6C4tt9eN438KrUytSHkyoXWgvr3mO35KtlfKK3+/NwI2y0joW43e+O5DCUsUniVqGJKuQMsGApHttsbj1zagXVmSTqY0RVVnr56rV8EL3+IWQ7bfrY7GslPW36EH58y29cPgmauP3ldCYsUbo3vhyptTOO87x6GjHS7I5ILsSwzCgCmRHh4fVvDcRshZaH5o2Z7/lD9/BcWe5bCN/3BckKD3gWmSrQSFincHA64/UPwDYHY1bD0PbsjkgvZuQB2/25Wd7V52e5o3Mt1T0FACTi2A9Z+YXc0ktcOboBpPSEjBWp2hI5jCsxopRIWkZCycNvZZbKLR8LB9fbGI1k5M86V4G/6oLql5za/YGj1gtleOAJSTtkbj+Sd47vhy7sgJQEqXgd3TQRPL7ujyjYlLCJgJptdc7tZ5jn7YVNJVVzD+mmmYZ9fiCra5pVG90HxynD6MPwx1u5oJC+cOmxK7p8+DKF1oOc08PazO6ocUcIiAmZItON7EFjKfDkufMPuiARMg77fzv5ftHwWAorbG4+78vKBtkPN9h9jIfGQvfFI7joTb5oZntgNRSvCvd+YPwAKGCUsIv8ILAmdPjDbf4yFvcvtjUdMfYjEAxBSAZo8YHc07q3WHaYzb9ppWDTS7mgkt6Sdgem94NBG8wdZ79kQFGZ3VFdECYvIv9W8Der3AiyzakjX8+1z+igsOTsJuu0rBW74usBxOM6V7F/zBRzZZm88cvWcGabR5Z4lptnlvd+acg4FlBIWkf+6ZQSElIcTe84tpZX8t/htU4k4rC5ce7fd0RQOFZublSNWBiwYbnc0cjUsC34aBFt+MKvrek41TS8LMCUsIv/lFwJ3fGS2V00yS2olfx3bZRrzgSnu56GPqnzTdig4PGHbT7D3D7ujkSu18A1Y/Zlpv3DXRNM/qoDTp4DIhVRuBZEPm+3vB0LyCXvjKWyiXjUrtqq2g8qt7Y6mcClVHRr1NdvzVLK/QFrxf+c6mncYDbVutzeeXKKEReRi2g6FElUh8SD8/Kzd0RQe+1eZhnw4TAl+yX+tXgDvQIj95/9CCoyN38Avz5ntG1+Gxv3sjScXKWERuRifALjz/8yQ6saZ8Nd3dkfk/izL/FUPZvJzaG174ymsgkLhuifM9oLhkJ5qbzySPTujTB0pgKYPQctn7I0nlylhEbmUco1NYzCAH5+GxDh743F3236BmD/Ayw9ufNHuaAq35gOhSKip3bF6st3RyOXsXw0zeoMzDa69y3RfLiAl97NLCYvI5bR6HsLqQPJx+OFJXdPPKxnppms2QLNHTcsEsY9vEWg92GwvfssUHxPXdGQ7fHW3qaFTpQ10Hu+WE9Xd7xWJ5DYvH7hzglkauP0XWPul3RG5p7VfwNHt4F8crn/K7mgETCffktUh6Rgse9/uaORC4mNNyf3k4xDeELpNMZ9ZbkgJi0h2hNaCG18y23MHw4m99sbjblJOmcZ7YEa0CmDZcLfk6QXtztZjWf6R+XIU15F03JTcT9gPJapBr5lmZMxNKWERya4Wj0P5ZqaY2fePgdNpd0TuY/mHpilbsUrQuL/d0ci/1bgVKrSA9DOw8E27o5F/pCbB1O5wZCsEhUPvWaa9iBtTwiKSXR6ecOc4s9xzzxKI/j+7I3IPiXGw7GwPp3ZD3XY4u8ByOEzxPoB1X0HcX/bGI5CRBjP7wv5o8CtqkpWiFeyOKs8pYRHJieKVz314LxhmJrvJ1Vk80kwWLNsIanW2Oxq5kHKNz/7fWDB/qN3RFG5Opxnh3TEPvPzhnq+h9DV2R5UvlLCI5FTj/lClrRkin/2QWd0iV+bIdlj9udm+6TW3W4bpVtoNBQ9v2Dkf/l5kdzSFk2WZ/mYbZpj2Cd0+hwqRdkeVb5SwiOSUwwF3fGgmhh5YA0tH2x1RwbVgmGm0V+M2iLjO7mjkUopXhib3m+35QzSHyw7L3jfzvcD0O6ve3t548pkSFpErERwOt71rthe/BQfW2RpOgbT3D9Ngz+EB7YbZHY1kR8tnwTcYDq6HTd/YHU3hsvbLc3WKbn4D6ve0Nx4bKGERuVJ1ukKtO0yTvtkPQ9oZuyMqOP5dgr9hHyhVw954JHsCS56rkRP1mn7m88vWn2HO2VYJ1z0JLQbaG49NlLCIXCmHAzq8B4Gl4cgW085dsmfz96axnncgtFYJ/gIl8hGzjDY+BlZ+Ync07m/vH/BNP3PptP695+riFEJKWESuRmAJuP3sktw/xpoPF7m09FSIOvuh2+Jx02hPCg6fAGhztoji7++Y4mWSNw5tgqk9zAT/6rdCp/cL9cR0JSwiV6vGreYvHyz47hFTtVUubvVkOP63GZkqpEPbBV69nlC6tukvpEnneePEHvjyLkiJhwrN4e5JpvJwIaaERSQ33DICQsqbD5l5L9sdjes6E28mKQO0fgF8g+yNR66Mhyfc9KrZXvF/alWR204dMf2BTh0yiWHPaWZkq5BTwiKSG/yCofPHZnv1ZNixwN54XNWy900jvRLVzGRbKbiqtoVKrSAjVfO3ctOZBPjqLjMKWbQC3Pst+BezOyqXoIRFJLdUamkmJALMGahr+/8VH2sa6IFZxuzpbWs4cpUcjnOjLBtmaGl/bkhPgRm9zLLxgJJw72wILmN3VC5DCYtIbmo31IweJB6En5+1OxrXsuhNM3mwQnOo2cHuaCQ3hNeHOt3M9vxXzHJ1uTLODJj1AOz+HXyKwL3fQMmqdkflUpSwiOQmb3+48/9M2exN38Bfs+2OyDXE/QXrppptleB3L21eBk8f80W7M8ruaAomy4KfnzHL/T19oMdXEN7A7qhcjhIWkdxWrhHcMMhs/zjIdCMu7OYPBctpCu2Vb2J3NJKbilWEyIfM9vwhZqRAcmbRSFg1CXBAlwlQubXdEbkkJSwieaHlcxBWF5KPww9PFO6h8r8XmYZ5Hl7QVp1+3dIN/wO/onD4L1g/3e5oCpboT0zHcoAO70LtO+2Nx4UpYRHJC14+5tKQpw9sn2v6gBRGTqf5qxtMl+sSVeyNR/KGfzFo+YzZ/u11SEu2N56CYtOsc3PdWg+GJgPsjcfFKWERySuhtcz1fYC5LxTOWhWbvjUrHnyCzKiTuK8mD0BIBUg8AH9+bHc0rm/XQpj1IGCZRKXV83ZH5PKUsIjkpeYDzaqY1FPw3aNmxKGwSE+BqLPLXq9/EoqUsjceyVveftD2bEPLJe/B6aP2xuPKYtfAjHvBmQa1OsOtb2siejYoYRHJSx6epqCcdyDsXQorxtsdUf6J/sQ0yAsqA80eszsayQ/X3g1l6kFqoukzJOc7uhO+utv8EVOplZlk6+Fpd1QFghIWkbxWvDK0f91sRw2HI9vsjSc/JJ8494V140sqK15YeHiYZesAKz+FY7vsjcfVJBw0JfeTjkGZ+mb5spev3VEVGEpYRPJDo35QtZ0pnDb7IchIszuivLVkFJw5CaWugfr32B2N5KfKraDqTeBMh99eszsa15F8Ar7sYkYdi1eBXt+ol1YOKWERyQ8OB9w+FvxC4MBaWOLGHW5P7DUN8cCUbtdwd+Fz03DAYQon7l9ldzT2S02CqT3g8GYoEga9Z2tO1xVQwiKSX4LD4bZRZvv3t03i4o4WvmEa4kXcANVusjsasUNobajfy2zPK+Ql+zPS4Jt+sO9P8wdL71mm2J7kmBIWkfxU526zKsCZDrMfhrQzdkeUuw6uN43wAG5WCf5C7cYXwcsPYv6Abb/YHY09LAvmPGFqMXn5Qc8ZJpmTK6KERSQ/ORzQYTQEloYjW2Hh63ZHlHssy/w1DVCnq3qhFHYhZaHZo2Z7wVDISLc3HjvMHwLrp5reYl0/g4rN7Y6oQFPCIpLfAkuY+SwAf3wIe5bZG09u2RUFuxeb6r7/FMyTwu36pyCgBBzdDmun2B1N/lr2Afzxgdm+fSzUuNXeeNyAEhYRO9S4BRrcC1jw3SOQkmh3RFfHmQHzzpbgb/ogFIuwNRxxEX4h5yq4LhoBKafsjSe/rJsG88+ONrYbDg162RuPm1DCImKX9iNMKfOTe2FeAR+RWD/dNL7zCzGN8ET+0agfFKsEp+Jg+Ud2R5P3tv8K358tlNh8IFz3pL3xuBElLCJ28Qs2VXABVn8GO+bbGs4VS0s2De/AJCsBxe2NR1yLlw+0O9ule9n7kBhnbzx5KeZP+LovWBlQr6cpoqeJ57lGCYuInSrdcG5i4vcDIem4vfFciT/HmYZ3IeWh6UN2RyOuqFZnKNsI0k7D4pF2R5M34jbD1G6QngzV2pt5Kx76is1NejdF7NZ2CJSsDqcOwc/P2B1Nzpw+BkvfM9ttXjEN8ET+y+E4V7J/9edwZLu98eS2kzGmiu2ZeCgfaVYEeXrbHZXbUcIiYjdvf7hzvFn6uOlb2DTL7oiy7/d3ICUBwuqYpcwiFxNxHdS4zVwuiRpudzS55/RR0x8o8aBpRdFzunpn5RElLCKuoGyjc5NVfxoEiYfsjSc7jv9tGtyB+etZw99yOe2Gm8R864+wd7nd0Vy9lETTefnYTnNJ9N5vNYcrD+kTRsRVtHwWytQzTdLmPOH65cyjXgVnGlRpC1VutDsaKQhKVYeGfcz2/AJesj89BWbca1psBJQw/YFCytodlVtTwiLiKrx84M7/A09f2PGraxfa2r/KNLbDcbbRnUg2tR4M3oGwfyVs/t7uaK6M02laa/y9yLyWXjOhZDW7o3J7SlhEXEnpa85ViZ07GE7ssTWcC7IsU3IczNLNsDr2xiMFS1AotHjcbEcNh/RUe+PJKcuCuc/DX7PAwxt6fGku6UqeU8Ii4mqaPwYVWkDqKfjuMfPXnCvZPhf2LjPN3Nq8ZHc0UhC1GGj6aR3/29QgKkh+fweiJwAOM1m+Shu7Iyo0lLCIuBoPT1NQzjsQ9i6FFePsjuicjHSYf7YIWLNHIKScvfFIweQbBK1fMNuLR8KZBHvjya6VE2HhG2b71rdM93XJN0pYRFxR8UrQ/uwH44LhcGSbvfH8Y+0UOLoN/IvD9U/bHY0UZA37QolqkHTMVMB1dX99Bz+dXcnX8jmIVJHE/KaERcRVNboPqt4EGSkw+yHISLM3npRTpoEdQKvnTN8gkSvl6XVuwvbyjyDhgL3xXMrfi2HWA4Blfi9vfNHuiAolJSwirsrhMOW9/YqapZNLRtkbz/KPTAO7YhHQ+H57YxH3UOM2qNDclLNf+Kbd0VzYgXUwvRdkpMI1naDDaPUHsokSFhFXFlwGOpxNVH5/xyQudjh1+NywfdshZgm2yNX6d8n+dV+Zfjyu5Ngu+PIuSE2EiBugy6dmjpnYQgmLiKu79i7TPM6Zbmo/pJ3J/xgWjTSN68IbQu0u+f/84r7KN4Fad4DlhAVD7Y7mnMRDMKUzJB2FsLrQY6p6ZdlMCYuIq3M4zDB0kVA4shV+ey1/n//ojnNLT29+TcPhkvvaDgUPL9gxz8wXsVvySTOycjIGilUyJff9gu2OqtBTwiJSEASWMPNZwMwl2bMs/557wTDTsK76rRBxff49rxQeJaqcmxc1f4i9tYfSkmFaT4jbZP5I6D0bipS2Lx7JpIRFpKCo3h4a9AYs+O5h03gtr+1dbhrVOTyg3bC8fz4pvFo9Bz5BcHCdqSJrh4x0+OZ+iPkDfIPNyErxSvbEIue5ooTlo48+IiIiAj8/PyIjI4mOjr7osa1bt8bhcJx369ChAwBpaWk8//zz1KlTh8DAQMLDw+nTpw8HDrjwEjcRu7R/E0IqmKHqX/O4yqxlmQZ1YBKl0jXz9vmkcAssCdc/ZbajhpvmgvnJsuDHJ2HbT6afV8/pajvhYnKcsMyYMYNBgwYxdOhQ1qxZQ7169Wjfvj2HDx++4PGzZs3i4MGDmbdNmzbh6elJ165dAUhKSmLNmjW88sorrFmzhlmzZrFt2zZuv/32q3tlIu7ILxjuHAc4YM3nsH1e3j3XljmmQZ13gGlYJ5LXmj0KQWVMQh79Sf4+d9RwWPulGU28exJEXJe/zy+X5bCsnPX3joyMpEmTJnz44YcAOJ1Oypcvz+OPP84LL7xw2cePGTOGIUOGcPDgQQIDAy94zMqVK2natCl79+6lQoUKlz1nQkICISEhxMfHExysiVFSCMx9Ef78CIqEwaPLIaB47p4/Iw0+amp6vbR8Tj2DJP+smQJzBpr6Q0+uA/9ief+cyz+CX88Wg7t9LDTsk/fPKUDOvr9zNMKSmprK6tWradeu3bkTeHjQrl07li9fnq1zTJw4kR49elw0WQGIj4/H4XBQtGjRC96fkpJCQkJClptIodL2FShZA04dgp+fyf3zr/7MJCuBpeC6J3L//CIXU/8eKF0LzpyEJaPz/vnWzziXrLQdomTFheUoYTl69CgZGRmEhoZm2R8aGsqhQ4cu+/jo6Gg2bdrEgAEDLnrMmTNneP755+nZs+dFs60RI0YQEhKSeStfvnxOXoZIweftbzrFOjxh07fmllvOJJi6K2Aa1PkG5d65RS7HwxNuetVsr/g/c3kor+yYD98/arabPQrXD8q755Krlq+rhCZOnEidOnVo2rTpBe9PS0ujW7duWJbFuHEX71A7ePBg4uPjM2/79u3Lq5BFXFfZhtDy7OjKT/8zha5yw7L3TbGsElVNgzqR/Fa1HVRqafpo/fZG3jzHvmiY0dsUZKzTDW5+QzWGXFyOEpaSJUvi6elJXFxclv1xcXGEhYVd8rGnT59m+vTp3H//hXuQ/JOs7N27l/nz51/yWpavry/BwcFZbiKFUstnoUw9SD4Bcx43Kx2uRsIBcz0fzDJmT++rDlEkxxyOc6MsG2bAwfW5e/7DW+GrrqaHUdV2cMdH4KEqH64uR/9DPj4+NGrUiKioqMx9TqeTqKgomjdvfsnHzpw5k5SUFO69997z7vsnWdmxYwcLFiygRIkSOQlLpPDy9IY7J5hlmDvmwZovru58C980H+LlI6Fmx9yJUeRKhDeAOl0ByxSTu9pk/B8n98GXXcwcmbKNodsX6o1VQOQ4pRw0aBCffPIJn3/+OVu2bOGRRx7h9OnT9OvXD4A+ffowePD5SyAnTpxI586dz0tG0tLSuPvuu1m1ahVfffUVGRkZHDp0iEOHDpGamnqFL0ukECld00zCBTN58MSeKztP3GbTgA5MQzoNj4vd2rwMnj7w9yLYFXXZwy/r9DGTrCTEmknrvWaCz8UXgIhr8crpA7p3786RI0cYMmQIhw4don79+sydOzdzIm5MTAwe/xla27ZtG0uXLmXevPNrRsTGxjJnzhwA6tevn+W+hQsX0rp165yGKFL4NHsUtv5sKnR+9yj0/THnQ9wLhpkGdNfcDhUi8yRMkRwpFgFNH4TlH8L8oVD5xivvlpxyCqZ2g6PbIbgs9J6V++UAJE/luA6LK1IdFhHg+G4Yd53pqnzzG9BiYPYfu/t3+LyTaUD36AooWTXv4hTJiaTj8EF9OBMPnceZZc85lZ4K03qYURr/YtD/VyhVI9dDlZzLszosIuLCileCW94021GvmomF2eF0wryzl5Qa9VOyIq4loDjccHY13G+vm+aEOeF0wnePmGTFOwDumalkpYBSwiLiThr2hao3meWgsx8yFWsv569ZpuGcTxFo9XyehyiSY00fhJDyZu7JivHZf5xlwa+DYdM3ZvSw2xQo3yTv4pQ8pYRFxJ04HKa0uF9Rk4T8/u6lj09PMT1UAK57CoqUyuMARa6Atx+0OTsKuGS0mTybHUtGnUtwOo+Dau0ufby4NCUsIu4muAx0GGW2f38HYtdc/NiVn5pKokXCoPmj+ROfyJWo09V0T05JMD/Xl7P6M/jtNbN9y0io2y1Pw5O8p4RFxB3VuRtq3wlWBsx++MLX/ZNPwOK3zXabl7S8U1ybh4dZbg8m0T7+98WP3fID/Pi02b5+EDR7JO/jkzynhEXEXXUYDUVC4eg2M1nxv5aMNsWzSl0D9a5g5YVIfqtyo6lM60yDqNcufMyepfDN/WaJfoPepqGhuAUlLCLuKqA43P6h2V7+kfkg/8fJGNNYDuCm4eCZ45JMIvZoNxxwmMni+1dnve/gBpjW00w6r9kROo5RAUQ3ooRFxJ1Vvxka9gEss7QzJdHs/+0N86EecQNUu9nWEEVyJOzac7VY/l2y//jf8OVdZo5LxevgrolKxN2MEhYRd9f+TShawYyq/PqiaSS3YYa576bh+gtUCp4bXwQvP9i7FLb/ColxMOVOOH0YQutAz2lmZZG4FSUsIu7ON8gs6cRhmiN+3Rew4Nq7oGwju6MTybmQcucm0s4fAl/dZXpoFa0I934DfiG2hid5QwmLSGEQcT00f8xsn9gNHt7n6lqIFETXPw3+xc2k8kMbIbAU9J4NQWF2RyZ5RAmLSGHR5hXToRag6QOmlL9IQeUXcq4ys08Q3PstlKhib0ySpzQjSaSw8PYzH+pbf4RG99kdjcjVa/oAeHpD+aamqJy4NXVrFhEREVuoW7OIiIi4FSUsIiIi4vKUsIiIiIjLU8IiIiIiLk8Ji4iIiLg8JSwiIiLi8pSwiIiIiMtTwiIiIiIuTwmLiIiIuDwlLCIiIuLylLCIiIiIy1PCIiIiIi5PCYuIiIi4PC+7A8gN/zScTkhIsDkSERERya5/vrf/+R6/FLdIWBITEwEoX768zZGIiIhITiUmJhISEnLJYxxWdtIaF+d0Ojlw4ABBQUE4HI5cPXdCQgLly5dn3759BAcH5+q55Ry9z/lD73P+0XudP/Q+54+8ep8tyyIxMZHw8HA8PC49S8UtRlg8PDwoV65cnj5HcHCwfhnygd7n/KH3Of/ovc4fep/zR168z5cbWfmHJt2KiIiIy1PCIiIiIi5PCctl+Pr6MnToUHx9fe0Oxa3pfc4fep/zj97r/KH3OX+4wvvsFpNuRURExL1phEVERERcnhIWERERcXlKWERERMTlKWERERERl6eE5SKGDRuGw+HIcqtZs6bdYbml2NhY7r33XkqUKIG/vz916tRh1apVdoflViIiIs77eXY4HDz22GN2h+ZWMjIyeOWVV6hUqRL+/v5UqVKF1157LVt9UiRnEhMTeeqpp6hYsSL+/v60aNGClStX2h1Wgff777/TqVMnwsPDcTgcfPfdd1nutyyLIUOGUKZMGfz9/WnXrh07duzIl9iUsFxC7dq1OXjwYOZt6dKldofkdk6cOMF1112Ht7c3v/zyC5s3b2bUqFEUK1bM7tDcysqVK7P8LM+fPx+Arl272hyZe3nrrbcYN24cH374IVu2bOGtt97i7bffZuzYsXaH5nYGDBjA/PnzmTJlChs3buTmm2+mXbt2xMbG2h1agXb69Gnq1avHRx99dMH73377bT744APGjx/PihUrCAwMpH379pw5cybvg7PkgoYOHWrVq1fP7jDc3vPPP29df/31dodR6Dz55JNWlSpVLKfTaXcobqVDhw5W//79s+zr0qWL1atXL5sick9JSUmWp6en9eOPP2bZ37BhQ+ull16yKSr3A1izZ8/O/LfT6bTCwsKsd955J3PfyZMnLV9fX2vatGl5Ho9GWC5hx44dhIeHU7lyZXr16kVMTIzdIbmdOXPm0LhxY7p27Urp0qVp0KABn3zyid1hubXU1FS+/PJL+vfvn+vNQgu7Fi1aEBUVxfbt2wFYv349S5cu5dZbb7U5MveSnp5ORkYGfn5+Wfb7+/trJDwP7d69m0OHDtGuXbvMfSEhIURGRrJ8+fI8f34lLBcRGRnJZ599xty5cxk3bhy7d+/mhhtuIDEx0e7Q3Mrff//NuHHjqFatGr/++iuPPPIITzzxBJ9//rndobmt7777jpMnT3LffffZHYrbeeGFF+jRowc1a9bE29ubBg0a8NRTT9GrVy+7Q3MrQUFBNG/enNdee40DBw6QkZHBl19+yfLlyzl48KDd4bmtQ4cOARAaGpplf2hoaOZ9ecktujXnhX//RVS3bl0iIyOpWLEiX3/9Nffff7+NkbkXp9NJ48aNefPNNwFo0KABmzZtYvz48fTt29fm6NzTxIkTufXWWwkPD7c7FLfz9ddf89VXXzF16lRq167NunXreOqppwgPD9fPcy6bMmUK/fv3p2zZsnh6etKwYUN69uzJ6tWr7Q5N8ohGWLKpaNGiVK9enZ07d9odilspU6YMtWrVyrLvmmuu0eW3PLJ3714WLFjAgAED7A7FLT377LOZoyx16tShd+/ePP3004wYMcLu0NxOlSpVWLx4MadOnWLfvn1ER0eTlpZG5cqV7Q7NbYWFhQEQFxeXZX9cXFzmfXlJCUs2nTp1il27dlGmTBm7Q3Er1113Hdu2bcuyb/v27VSsWNGmiNzb5MmTKV26NB06dLA7FLeUlJSEh0fWj1VPT0+cTqdNEbm/wMBAypQpw4kTJ/j111+544477A7JbVWqVImwsDCioqIy9yUkJLBixQqaN2+e58+vS0IX8cwzz9CpUycqVqzIgQMHGDp0KJ6envTs2dPu0NzK008/TYsWLXjzzTfp1q0b0dHRTJgwgQkTJtgdmttxOp1MnjyZvn374uWlX/280KlTJ9544w0qVKhA7dq1Wbt2LaNHj6Z///52h+Z2fv31VyzLokaNGuzcuZNnn32WmjVr0q9fP7tDK9BOnTqV5UrC7t27WbduHcWLF6dChQo89dRTvP7661SrVo1KlSrxyiuvEB4eTufOnfM+uDxfh1RAde/e3SpTpozl4+NjlS1b1urevbu1c+dOu8NySz/88IN17bXXWr6+vlbNmjWtCRMm2B2SW/r1118twNq2bZvdobithIQE68knn7QqVKhg+fn5WZUrV7ZeeuklKyUlxe7Q3M6MGTOsypUrWz4+PlZYWJj12GOPWSdPnrQ7rAJv4cKFFnDerW/fvpZlmaXNr7zyihUaGmr5+vpabdu2zbfPFIdlqQSjiIiIuDbNYRERERGXp4RFREREXJ4SFhEREXF5SlhERETE5SlhEREREZenhEVERERcnhIWERERcXlKWERERMTlKWERERERl6eERURERFyeEhYRERFxeUpYRERExOX9P99QNsH/c63IAAAAAElFTkSuQmCC\n"},"metadata":{}}],"execution_count":65}]}