{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.14","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":84896,"databundleVersionId":10305135,"sourceType":"competition"}],"dockerImageVersionId":30804,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\nwarnings.filterwarnings('ignore', message='use_inf_as_na option is deprecated')\n# warnings.filterwarnings('ignore', category=FutureWarning)","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:35:54.437894Z","iopub.execute_input":"2024-12-18T15:35:54.438317Z","iopub.status.idle":"2024-12-18T15:35:54.444274Z","shell.execute_reply.started":"2024-12-18T15:35:54.438283Z","shell.execute_reply":"2024-12-18T15:35:54.443139Z"}},"outputs":[],"execution_count":196},{"cell_type":"code","source":"df = pd.read_csv(\"/kaggle/input/playground-series-s4e12/train.csv\")\ntest = pd.read_csv(\"/kaggle/input/playground-series-s4e12/test.csv\")\nsample_submission = pd.read_csv(\"/kaggle/input/playground-series-s4e12/sample_submission.csv\")\n\ndf.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:35:54.446518Z","iopub.execute_input":"2024-12-18T15:35:54.446859Z","iopub.status.idle":"2024-12-18T15:36:00.902412Z","shell.execute_reply.started":"2024-12-18T15:35:54.446828Z","shell.execute_reply":"2024-12-18T15:36:00.901426Z"}},"outputs":[{"execution_count":197,"output_type":"execute_result","data":{"text/plain":"   id   Age  Gender  Annual Income Marital Status  Number of Dependents  \\\n0   0  19.0  Female        10049.0        Married                   1.0   \n1   1  39.0  Female        31678.0       Divorced                   3.0   \n2   2  23.0    Male        25602.0       Divorced                   3.0   \n3   3  21.0    Male       141855.0        Married                   2.0   \n4   4  21.0    Male        39651.0         Single                   1.0   \n\n  Education Level     Occupation  Health Score  Location  ... Previous Claims  \\\n0      Bachelor's  Self-Employed     22.598761     Urban  ...             2.0   \n1        Master's            NaN     15.569731     Rural  ...             1.0   \n2     High School  Self-Employed     47.177549  Suburban  ...             1.0   \n3      Bachelor's            NaN     10.938144     Rural  ...             1.0   \n4      Bachelor's  Self-Employed     20.376094     Rural  ...             0.0   \n\n   Vehicle Age  Credit Score  Insurance Duration           Policy Start Date  \\\n0         17.0         372.0                 5.0  2023-12-23 15:21:39.134960   \n1         12.0         694.0                 2.0  2023-06-12 15:21:39.111551   \n2         14.0           NaN                 3.0  2023-09-30 15:21:39.221386   \n3          0.0         367.0                 1.0  2024-06-12 15:21:39.226954   \n4          8.0         598.0                 4.0  2021-12-01 15:21:39.252145   \n\n  Customer Feedback Smoking Status Exercise Frequency Property Type  \\\n0              Poor             No             Weekly         House   \n1           Average            Yes            Monthly         House   \n2              Good            Yes             Weekly         House   \n3              Poor            Yes              Daily     Apartment   \n4              Poor            Yes             Weekly         House   \n\n  Premium Amount  \n0         2869.0  \n1         1483.0  \n2          567.0  \n3          765.0  \n4         2022.0  \n\n[5 rows x 21 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>id</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Annual Income</th>\n      <th>Marital Status</th>\n      <th>Number of Dependents</th>\n      <th>Education Level</th>\n      <th>Occupation</th>\n      <th>Health Score</th>\n      <th>Location</th>\n      <th>...</th>\n      <th>Previous Claims</th>\n      <th>Vehicle Age</th>\n      <th>Credit Score</th>\n      <th>Insurance Duration</th>\n      <th>Policy Start Date</th>\n      <th>Customer Feedback</th>\n      <th>Smoking Status</th>\n      <th>Exercise Frequency</th>\n      <th>Property Type</th>\n      <th>Premium Amount</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0</td>\n      <td>19.0</td>\n      <td>Female</td>\n      <td>10049.0</td>\n      <td>Married</td>\n      <td>1.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>22.598761</td>\n      <td>Urban</td>\n      <td>...</td>\n      <td>2.0</td>\n      <td>17.0</td>\n      <td>372.0</td>\n      <td>5.0</td>\n      <td>2023-12-23 15:21:39.134960</td>\n      <td>Poor</td>\n      <td>No</td>\n      <td>Weekly</td>\n      <td>House</td>\n      <td>2869.0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>39.0</td>\n      <td>Female</td>\n      <td>31678.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>Master's</td>\n      <td>NaN</td>\n      <td>15.569731</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>12.0</td>\n      <td>694.0</td>\n      <td>2.0</td>\n      <td>2023-06-12 15:21:39.111551</td>\n      <td>Average</td>\n      <td>Yes</td>\n      <td>Monthly</td>\n      <td>House</td>\n      <td>1483.0</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>2</td>\n      <td>23.0</td>\n      <td>Male</td>\n      <td>25602.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>High School</td>\n      <td>Self-Employed</td>\n      <td>47.177549</td>\n      <td>Suburban</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>14.0</td>\n      <td>NaN</td>\n      <td>3.0</td>\n      <td>2023-09-30 15:21:39.221386</td>\n      <td>Good</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n      <td>567.0</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>3</td>\n      <td>21.0</td>\n      <td>Male</td>\n      <td>141855.0</td>\n      <td>Married</td>\n      <td>2.0</td>\n      <td>Bachelor's</td>\n      <td>NaN</td>\n      <td>10.938144</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>1.0</td>\n      <td>0.0</td>\n      <td>367.0</td>\n      <td>1.0</td>\n      <td>2024-06-12 15:21:39.226954</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Daily</td>\n      <td>Apartment</td>\n      <td>765.0</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>4</td>\n      <td>21.0</td>\n      <td>Male</td>\n      <td>39651.0</td>\n      <td>Single</td>\n      <td>1.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>20.376094</td>\n      <td>Rural</td>\n      <td>...</td>\n      <td>0.0</td>\n      <td>8.0</td>\n      <td>598.0</td>\n      <td>4.0</td>\n      <td>2021-12-01 15:21:39.252145</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n      <td>2022.0</td>\n    </tr>\n  </tbody>\n</table>\n<p>5 rows × 21 columns</p>\n</div>"},"metadata":{}}],"execution_count":197},{"cell_type":"code","source":"test.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:00.903612Z","iopub.execute_input":"2024-12-18T15:36:00.903969Z","iopub.status.idle":"2024-12-18T15:36:00.925913Z","shell.execute_reply.started":"2024-12-18T15:36:00.903939Z","shell.execute_reply":"2024-12-18T15:36:00.924743Z"}},"outputs":[{"execution_count":198,"output_type":"execute_result","data":{"text/plain":"        id   Age  Gender  Annual Income Marital Status  Number of Dependents  \\\n0  1200000  28.0  Female         2310.0            NaN                   4.0   \n1  1200001  31.0  Female       126031.0        Married                   2.0   \n2  1200002  47.0  Female        17092.0       Divorced                   0.0   \n3  1200003  28.0  Female        30424.0       Divorced                   3.0   \n4  1200004  24.0    Male        10863.0       Divorced                   2.0   \n\n  Education Level     Occupation  Health Score  Location    Policy Type  \\\n0      Bachelor's  Self-Employed      7.657981     Rural          Basic   \n1        Master's  Self-Employed     13.381379  Suburban        Premium   \n2             PhD     Unemployed     24.354527     Urban  Comprehensive   \n3             PhD  Self-Employed      5.136225  Suburban  Comprehensive   \n4     High School     Unemployed     11.844155  Suburban        Premium   \n\n   Previous Claims  Vehicle Age  Credit Score  Insurance Duration  \\\n0              NaN         19.0           NaN                 1.0   \n1              NaN         14.0         372.0                 8.0   \n2              NaN         16.0         819.0                 9.0   \n3              1.0          3.0         770.0                 5.0   \n4              NaN         14.0         755.0                 7.0   \n\n            Policy Start Date Customer Feedback Smoking Status  \\\n0  2023-06-04 15:21:39.245086              Poor            Yes   \n1  2024-04-22 15:21:39.224915              Good            Yes   \n2  2023-04-05 15:21:39.134960           Average            Yes   \n3  2023-10-25 15:21:39.134960              Poor            Yes   \n4  2021-11-26 15:21:39.259788           Average             No   \n\n  Exercise Frequency Property Type  \n0             Weekly         House  \n1             Rarely     Apartment  \n2            Monthly         Condo  \n3              Daily         House  \n4             Weekly         House  ","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>id</th>\n      <th>Age</th>\n      <th>Gender</th>\n      <th>Annual Income</th>\n      <th>Marital Status</th>\n      <th>Number of Dependents</th>\n      <th>Education Level</th>\n      <th>Occupation</th>\n      <th>Health Score</th>\n      <th>Location</th>\n      <th>Policy Type</th>\n      <th>Previous Claims</th>\n      <th>Vehicle Age</th>\n      <th>Credit Score</th>\n      <th>Insurance Duration</th>\n      <th>Policy Start Date</th>\n      <th>Customer Feedback</th>\n      <th>Smoking Status</th>\n      <th>Exercise Frequency</th>\n      <th>Property Type</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1200000</td>\n      <td>28.0</td>\n      <td>Female</td>\n      <td>2310.0</td>\n      <td>NaN</td>\n      <td>4.0</td>\n      <td>Bachelor's</td>\n      <td>Self-Employed</td>\n      <td>7.657981</td>\n      <td>Rural</td>\n      <td>Basic</td>\n      <td>NaN</td>\n      <td>19.0</td>\n      <td>NaN</td>\n      <td>1.0</td>\n      <td>2023-06-04 15:21:39.245086</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Weekly</td>\n      <td>House</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1200001</td>\n      <td>31.0</td>\n      <td>Female</td>\n      <td>126031.0</td>\n      <td>Married</td>\n      <td>2.0</td>\n      <td>Master's</td>\n      <td>Self-Employed</td>\n      <td>13.381379</td>\n      <td>Suburban</td>\n      <td>Premium</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>372.0</td>\n      <td>8.0</td>\n      <td>2024-04-22 15:21:39.224915</td>\n      <td>Good</td>\n      <td>Yes</td>\n      <td>Rarely</td>\n      <td>Apartment</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>1200002</td>\n      <td>47.0</td>\n      <td>Female</td>\n      <td>17092.0</td>\n      <td>Divorced</td>\n      <td>0.0</td>\n      <td>PhD</td>\n      <td>Unemployed</td>\n      <td>24.354527</td>\n      <td>Urban</td>\n      <td>Comprehensive</td>\n      <td>NaN</td>\n      <td>16.0</td>\n      <td>819.0</td>\n      <td>9.0</td>\n      <td>2023-04-05 15:21:39.134960</td>\n      <td>Average</td>\n      <td>Yes</td>\n      <td>Monthly</td>\n      <td>Condo</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>1200003</td>\n      <td>28.0</td>\n      <td>Female</td>\n      <td>30424.0</td>\n      <td>Divorced</td>\n      <td>3.0</td>\n      <td>PhD</td>\n      <td>Self-Employed</td>\n      <td>5.136225</td>\n      <td>Suburban</td>\n      <td>Comprehensive</td>\n      <td>1.0</td>\n      <td>3.0</td>\n      <td>770.0</td>\n      <td>5.0</td>\n      <td>2023-10-25 15:21:39.134960</td>\n      <td>Poor</td>\n      <td>Yes</td>\n      <td>Daily</td>\n      <td>House</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>1200004</td>\n      <td>24.0</td>\n      <td>Male</td>\n      <td>10863.0</td>\n      <td>Divorced</td>\n      <td>2.0</td>\n      <td>High School</td>\n      <td>Unemployed</td>\n      <td>11.844155</td>\n      <td>Suburban</td>\n      <td>Premium</td>\n      <td>NaN</td>\n      <td>14.0</td>\n      <td>755.0</td>\n      <td>7.0</td>\n      <td>2021-11-26 15:21:39.259788</td>\n      <td>Average</td>\n      <td>No</td>\n      <td>Weekly</td>\n      <td>House</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}],"execution_count":198},{"cell_type":"code","source":"df.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:00.927666Z","iopub.execute_input":"2024-12-18T15:36:00.928643Z","iopub.status.idle":"2024-12-18T15:36:01.566779Z","shell.execute_reply.started":"2024-12-18T15:36:00.928592Z","shell.execute_reply":"2024-12-18T15:36:01.565714Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1200000 entries, 0 to 1199999\nData columns (total 21 columns):\n #   Column                Non-Null Count    Dtype  \n---  ------                --------------    -----  \n 0   id                    1200000 non-null  int64  \n 1   Age                   1181295 non-null  float64\n 2   Gender                1200000 non-null  object \n 3   Annual Income         1155051 non-null  float64\n 4   Marital Status        1181471 non-null  object \n 5   Number of Dependents  1090328 non-null  float64\n 6   Education Level       1200000 non-null  object \n 7   Occupation            841925 non-null   object \n 8   Health Score          1125924 non-null  float64\n 9   Location              1200000 non-null  object \n 10  Policy Type           1200000 non-null  object \n 11  Previous Claims       835971 non-null   float64\n 12  Vehicle Age           1199994 non-null  float64\n 13  Credit Score          1062118 non-null  float64\n 14  Insurance Duration    1199999 non-null  float64\n 15  Policy Start Date     1200000 non-null  object \n 16  Customer Feedback     1122176 non-null  object \n 17  Smoking Status        1200000 non-null  object \n 18  Exercise Frequency    1200000 non-null  object \n 19  Property Type         1200000 non-null  object \n 20  Premium Amount        1200000 non-null  float64\ndtypes: float64(9), int64(1), object(11)\nmemory usage: 192.3+ MB\n","output_type":"stream"}],"execution_count":199},{"cell_type":"code","source":"test.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:01.569047Z","iopub.execute_input":"2024-12-18T15:36:01.569394Z","iopub.status.idle":"2024-12-18T15:36:01.992673Z","shell.execute_reply.started":"2024-12-18T15:36:01.569329Z","shell.execute_reply":"2024-12-18T15:36:01.99163Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 800000 entries, 0 to 799999\nData columns (total 20 columns):\n #   Column                Non-Null Count   Dtype  \n---  ------                --------------   -----  \n 0   id                    800000 non-null  int64  \n 1   Age                   787511 non-null  float64\n 2   Gender                800000 non-null  object \n 3   Annual Income         770140 non-null  float64\n 4   Marital Status        787664 non-null  object \n 5   Number of Dependents  726870 non-null  float64\n 6   Education Level       800000 non-null  object \n 7   Occupation            560875 non-null  object \n 8   Health Score          750551 non-null  float64\n 9   Location              800000 non-null  object \n 10  Policy Type           800000 non-null  object \n 11  Previous Claims       557198 non-null  float64\n 12  Vehicle Age           799997 non-null  float64\n 13  Credit Score          708549 non-null  float64\n 14  Insurance Duration    799998 non-null  float64\n 15  Policy Start Date     800000 non-null  object \n 16  Customer Feedback     747724 non-null  object \n 17  Smoking Status        800000 non-null  object \n 18  Exercise Frequency    800000 non-null  object \n 19  Property Type         800000 non-null  object \ndtypes: float64(8), int64(1), object(11)\nmemory usage: 122.1+ MB\n","output_type":"stream"}],"execution_count":200},{"cell_type":"code","source":"#df = df.drop(['id','Previous Claims'], axis=1)\n#test = test.drop(['id','Previous Claims'], axis=1)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:01.993926Z","iopub.execute_input":"2024-12-18T15:36:01.99428Z","iopub.status.idle":"2024-12-18T15:36:01.998982Z","shell.execute_reply.started":"2024-12-18T15:36:01.994244Z","shell.execute_reply":"2024-12-18T15:36:01.997841Z"}},"outputs":[],"execution_count":201},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:02.000376Z","iopub.execute_input":"2024-12-18T15:36:02.000786Z","iopub.status.idle":"2024-12-18T15:36:02.630342Z","shell.execute_reply.started":"2024-12-18T15:36:02.000732Z","shell.execute_reply":"2024-12-18T15:36:02.629328Z"}},"outputs":[{"execution_count":202,"output_type":"execute_result","data":{"text/plain":"id                           0\nAge                      18705\nGender                       0\nAnnual Income            44949\nMarital Status           18529\nNumber of Dependents    109672\nEducation Level              0\nOccupation              358075\nHealth Score             74076\nLocation                     0\nPolicy Type                  0\nPrevious Claims         364029\nVehicle Age                  6\nCredit Score            137882\nInsurance Duration           1\nPolicy Start Date            0\nCustomer Feedback        77824\nSmoking Status               0\nExercise Frequency           0\nProperty Type                0\nPremium Amount               0\ndtype: int64"},"metadata":{}}],"execution_count":202},{"cell_type":"code","source":"test.isnull().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:02.631817Z","iopub.execute_input":"2024-12-18T15:36:02.632562Z","iopub.status.idle":"2024-12-18T15:36:03.048668Z","shell.execute_reply.started":"2024-12-18T15:36:02.632517Z","shell.execute_reply":"2024-12-18T15:36:03.047635Z"}},"outputs":[{"execution_count":203,"output_type":"execute_result","data":{"text/plain":"id                           0\nAge                      12489\nGender                       0\nAnnual Income            29860\nMarital Status           12336\nNumber of Dependents     73130\nEducation Level              0\nOccupation              239125\nHealth Score             49449\nLocation                     0\nPolicy Type                  0\nPrevious Claims         242802\nVehicle Age                  3\nCredit Score             91451\nInsurance Duration           2\nPolicy Start Date            0\nCustomer Feedback        52276\nSmoking Status               0\nExercise Frequency           0\nProperty Type                0\ndtype: int64"},"metadata":{}}],"execution_count":203},{"cell_type":"code","source":"print(df.columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:03.04993Z","iopub.execute_input":"2024-12-18T15:36:03.050269Z","iopub.status.idle":"2024-12-18T15:36:03.056134Z","shell.execute_reply.started":"2024-12-18T15:36:03.050237Z","shell.execute_reply":"2024-12-18T15:36:03.054909Z"}},"outputs":[{"name":"stdout","text":"Index(['id', 'Age', 'Gender', 'Annual Income', 'Marital Status',\n       'Number of Dependents', 'Education Level', 'Occupation', 'Health Score',\n       'Location', 'Policy Type', 'Previous Claims', 'Vehicle Age',\n       'Credit Score', 'Insurance Duration', 'Policy Start Date',\n       'Customer Feedback', 'Smoking Status', 'Exercise Frequency',\n       'Property Type', 'Premium Amount'],\n      dtype='object')\n","output_type":"stream"}],"execution_count":204},{"cell_type":"code","source":"print(test.columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:03.057476Z","iopub.execute_input":"2024-12-18T15:36:03.057888Z","iopub.status.idle":"2024-12-18T15:36:03.067994Z","shell.execute_reply.started":"2024-12-18T15:36:03.057852Z","shell.execute_reply":"2024-12-18T15:36:03.066961Z"}},"outputs":[{"name":"stdout","text":"Index(['id', 'Age', 'Gender', 'Annual Income', 'Marital Status',\n       'Number of Dependents', 'Education Level', 'Occupation', 'Health Score',\n       'Location', 'Policy Type', 'Previous Claims', 'Vehicle Age',\n       'Credit Score', 'Insurance Duration', 'Policy Start Date',\n       'Customer Feedback', 'Smoking Status', 'Exercise Frequency',\n       'Property Type'],\n      dtype='object')\n","output_type":"stream"}],"execution_count":205},{"cell_type":"code","source":"# Replace 'Policy Start Date' with your actual column name\ndf['Policy Start Date'] = pd.to_datetime(df['Policy Start Date'], errors='coerce')  # Convert to datetime object\n\n# Optionally format the dates to a standard string format (e.g., YYYY-MM-DD)\ndf['Policy Start Date'] = df['Policy Start Date'].dt.strftime('%Y-%m-%d')\n\n# Check for any invalid dates that were converted to NaT (Not a Time)\ninvalid_dates = df['Policy Start Date'].isnull().sum()\nprint(f\"Number of invalid dates: {invalid_dates}\")\n\n# Display a sample of the DataFrame to confirm the changes\n#print(df.head())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:03.069139Z","iopub.execute_input":"2024-12-18T15:36:03.06952Z","iopub.status.idle":"2024-12-18T15:36:04.377791Z","shell.execute_reply.started":"2024-12-18T15:36:03.069483Z","shell.execute_reply":"2024-12-18T15:36:04.376854Z"}},"outputs":[{"name":"stdout","text":"Number of invalid dates: 0\n","output_type":"stream"}],"execution_count":206},{"cell_type":"code","source":"# Replace 'Policy Start Date' with your actual column name\ntest['Policy Start Date'] = pd.to_datetime(test['Policy Start Date'], errors='coerce')  # Convert to datetime object\n\n# Optionally format the dates to a standard string format (e.g., YYYY-MM-DD)\ntest['Policy Start Date'] = test['Policy Start Date'].dt.strftime('%Y-%m-%d')\n\n# Check for any invalid dates that were converted to NaT (Not a Time)\ninvalid_dates = test['Policy Start Date'].isnull().sum()\nprint(f\"Number of invalid dates: {invalid_dates}\")\n\n# Display a sample of the DataFrame to confirm the changes\n#print(df.head())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:04.379338Z","iopub.execute_input":"2024-12-18T15:36:04.379652Z","iopub.status.idle":"2024-12-18T15:36:05.248403Z","shell.execute_reply.started":"2024-12-18T15:36:04.379622Z","shell.execute_reply":"2024-12-18T15:36:05.247414Z"}},"outputs":[{"name":"stdout","text":"Number of invalid dates: 0\n","output_type":"stream"}],"execution_count":207},{"cell_type":"markdown","source":"handling numerical missing values","metadata":{}},{"cell_type":"code","source":" df['Age'] = df['Age'].fillna(df['Age'].median())\ntest['Age'] = test['Age'].fillna(test['Age'].median())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.249438Z","iopub.execute_input":"2024-12-18T15:36:05.249765Z","iopub.status.idle":"2024-12-18T15:36:05.309749Z","shell.execute_reply.started":"2024-12-18T15:36:05.249728Z","shell.execute_reply":"2024-12-18T15:36:05.308882Z"}},"outputs":[],"execution_count":208},{"cell_type":"code","source":"df['Annual Income'] = df['Annual Income'].fillna(df['Annual Income'].median())\ntest['Annual Income'] = test['Annual Income'].fillna(test['Annual Income'].median())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.315101Z","iopub.execute_input":"2024-12-18T15:36:05.315661Z","iopub.status.idle":"2024-12-18T15:36:05.377518Z","shell.execute_reply.started":"2024-12-18T15:36:05.315629Z","shell.execute_reply":"2024-12-18T15:36:05.376361Z"}},"outputs":[],"execution_count":209},{"cell_type":"code","source":"df['Number of Dependents'] = df['Number of Dependents'].fillna(df['Number of Dependents'].mode()[0])\ntest['Number of Dependents'] = test['Number of Dependents'].fillna(test['Number of Dependents'].mode()[0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.378685Z","iopub.execute_input":"2024-12-18T15:36:05.379012Z","iopub.status.idle":"2024-12-18T15:36:05.423799Z","shell.execute_reply.started":"2024-12-18T15:36:05.378983Z","shell.execute_reply":"2024-12-18T15:36:05.422744Z"}},"outputs":[],"execution_count":210},{"cell_type":"code","source":"df['Previous Claims'] = df['Previous Claims'].fillna(df['Previous Claims'].mode()[0])\ntest['Previous Claims'] = test['Previous Claims'].fillna(test['Previous Claims'].mode()[0])","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.425247Z","iopub.execute_input":"2024-12-18T15:36:05.425535Z","iopub.status.idle":"2024-12-18T15:36:05.476977Z","shell.execute_reply.started":"2024-12-18T15:36:05.425508Z","shell.execute_reply":"2024-12-18T15:36:05.476121Z"}},"outputs":[],"execution_count":211},{"cell_type":"code","source":"df['Health Score'] = df['Health Score'].fillna(df['Health Score'].median())\ntest['Health Score'] = test['Health Score'].fillna(test['Health Score'].median())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.478074Z","iopub.execute_input":"2024-12-18T15:36:05.478339Z","iopub.status.idle":"2024-12-18T15:36:05.538675Z","shell.execute_reply.started":"2024-12-18T15:36:05.478314Z","shell.execute_reply":"2024-12-18T15:36:05.53786Z"}},"outputs":[],"execution_count":212},{"cell_type":"code","source":"df['Credit Score'] = df['Credit Score'].fillna(df['Credit Score'].median())\ntest['Credit Score'] = test['Credit Score'].fillna(test['Credit Score'].median())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.539828Z","iopub.execute_input":"2024-12-18T15:36:05.540127Z","iopub.status.idle":"2024-12-18T15:36:05.599147Z","shell.execute_reply.started":"2024-12-18T15:36:05.540097Z","shell.execute_reply":"2024-12-18T15:36:05.598085Z"}},"outputs":[],"execution_count":213},{"cell_type":"code","source":"df['Vehicle Age'] = df['Vehicle Age'].fillna(df['Vehicle Age'].median())\ntest['Vehicle Age'] = test['Vehicle Age'].fillna(test['Vehicle Age'].median())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.600801Z","iopub.execute_input":"2024-12-18T15:36:05.601251Z","iopub.status.idle":"2024-12-18T15:36:05.660273Z","shell.execute_reply.started":"2024-12-18T15:36:05.601207Z","shell.execute_reply":"2024-12-18T15:36:05.659466Z"}},"outputs":[],"execution_count":214},{"cell_type":"code","source":"df['Insurance Duration'] = df['Insurance Duration'].fillna(df['Insurance Duration'].median())\ntest['Insurance Duration'] = test['Insurance Duration'].fillna(test['Insurance Duration'].median())","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.661354Z","iopub.execute_input":"2024-12-18T15:36:05.661633Z","iopub.status.idle":"2024-12-18T15:36:05.721738Z","shell.execute_reply.started":"2024-12-18T15:36:05.661606Z","shell.execute_reply":"2024-12-18T15:36:05.720914Z"}},"outputs":[],"execution_count":215},{"cell_type":"markdown","source":"Now for categorical values","metadata":{}},{"cell_type":"code","source":"df['Marital Status'] = df['Marital Status'].fillna('Unknown')\ntest['Marital Status'] = test['Marital Status'].fillna('Unknown')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.722839Z","iopub.execute_input":"2024-12-18T15:36:05.723165Z","iopub.status.idle":"2024-12-18T15:36:05.855573Z","shell.execute_reply.started":"2024-12-18T15:36:05.723136Z","shell.execute_reply":"2024-12-18T15:36:05.854763Z"}},"outputs":[],"execution_count":216},{"cell_type":"code","source":"df['Occupation'] = df['Occupation'].fillna('unspecified')\ntest['Occupation'] = test['Occupation'].fillna('unspecified')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.856624Z","iopub.execute_input":"2024-12-18T15:36:05.856946Z","iopub.status.idle":"2024-12-18T15:36:05.996499Z","shell.execute_reply.started":"2024-12-18T15:36:05.856917Z","shell.execute_reply":"2024-12-18T15:36:05.995651Z"}},"outputs":[],"execution_count":217},{"cell_type":"code","source":"df['Customer Feedback'] = df['Customer Feedback'].fillna('No Feedback')\ntest['Customer Feedback'] = test['Customer Feedback'].fillna('No Feedback')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:05.997833Z","iopub.execute_input":"2024-12-18T15:36:05.998587Z","iopub.status.idle":"2024-12-18T15:36:06.132818Z","shell.execute_reply.started":"2024-12-18T15:36:05.99854Z","shell.execute_reply":"2024-12-18T15:36:06.131844Z"}},"outputs":[],"execution_count":218},{"cell_type":"code","source":"# Define the bins and labels for age groups\nbins = [0, 18, 30, 40, 50, 60, 70]  # Define bin edges\nlabels = ['<18', '18-29', '30-39', '40-49', '50-59', '60+']  # Labels corresponding to the bins\n\n# Apply binning to the Age column\ndf['Age Group'] = pd.cut(df['Age'], bins=bins, labels=labels, right=False)\n\n# Convert Age column to integer\ndf['Age'] = df['Age'].astype(int)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:06.134106Z","iopub.execute_input":"2024-12-18T15:36:06.13453Z","iopub.status.idle":"2024-12-18T15:36:06.178721Z","shell.execute_reply.started":"2024-12-18T15:36:06.134476Z","shell.execute_reply":"2024-12-18T15:36:06.177926Z"}},"outputs":[],"execution_count":219},{"cell_type":"code","source":"# Define the bins and labels for age groups\nbins = [0, 18, 30, 40, 50, 60, 70]  # Define bin edges\nlabels = ['<18', '18-29', '30-39', '40-49', '50-59', '60+']  # Labels corresponding to the bins\n\n# Apply binning to the Age column\ntest['Age Group'] = pd.cut(test['Age'], bins=bins, labels=labels, right=False)\n\n# Convert Age column to integer\ntest['Age'] = test['Age'].astype(int)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:06.179896Z","iopub.execute_input":"2024-12-18T15:36:06.180166Z","iopub.status.idle":"2024-12-18T15:36:06.211513Z","shell.execute_reply.started":"2024-12-18T15:36:06.180139Z","shell.execute_reply":"2024-12-18T15:36:06.210746Z"}},"outputs":[],"execution_count":220},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:06.212595Z","iopub.execute_input":"2024-12-18T15:36:06.212877Z","iopub.status.idle":"2024-12-18T15:36:06.833178Z","shell.execute_reply.started":"2024-12-18T15:36:06.212849Z","shell.execute_reply":"2024-12-18T15:36:06.832066Z"}},"outputs":[{"execution_count":221,"output_type":"execute_result","data":{"text/plain":"id                      0\nAge                     0\nGender                  0\nAnnual Income           0\nMarital Status          0\nNumber of Dependents    0\nEducation Level         0\nOccupation              0\nHealth Score            0\nLocation                0\nPolicy Type             0\nPrevious Claims         0\nVehicle Age             0\nCredit Score            0\nInsurance Duration      0\nPolicy Start Date       0\nCustomer Feedback       0\nSmoking Status          0\nExercise Frequency      0\nProperty Type           0\nPremium Amount          0\nAge Group               0\ndtype: int64"},"metadata":{}}],"execution_count":221},{"cell_type":"code","source":"test.isnull().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:06.834418Z","iopub.execute_input":"2024-12-18T15:36:06.834804Z","iopub.status.idle":"2024-12-18T15:36:07.247594Z","shell.execute_reply.started":"2024-12-18T15:36:06.83477Z","shell.execute_reply":"2024-12-18T15:36:07.246461Z"}},"outputs":[{"execution_count":222,"output_type":"execute_result","data":{"text/plain":"id                      0\nAge                     0\nGender                  0\nAnnual Income           0\nMarital Status          0\nNumber of Dependents    0\nEducation Level         0\nOccupation              0\nHealth Score            0\nLocation                0\nPolicy Type             0\nPrevious Claims         0\nVehicle Age             0\nCredit Score            0\nInsurance Duration      0\nPolicy Start Date       0\nCustomer Feedback       0\nSmoking Status          0\nExercise Frequency      0\nProperty Type           0\nAge Group               0\ndtype: int64"},"metadata":{}}],"execution_count":222},{"cell_type":"code","source":"df.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:07.248943Z","iopub.execute_input":"2024-12-18T15:36:07.249283Z","iopub.status.idle":"2024-12-18T15:36:07.873573Z","shell.execute_reply.started":"2024-12-18T15:36:07.249252Z","shell.execute_reply":"2024-12-18T15:36:07.872505Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 1200000 entries, 0 to 1199999\nData columns (total 22 columns):\n #   Column                Non-Null Count    Dtype   \n---  ------                --------------    -----   \n 0   id                    1200000 non-null  int64   \n 1   Age                   1200000 non-null  int64   \n 2   Gender                1200000 non-null  object  \n 3   Annual Income         1200000 non-null  float64 \n 4   Marital Status        1200000 non-null  object  \n 5   Number of Dependents  1200000 non-null  float64 \n 6   Education Level       1200000 non-null  object  \n 7   Occupation            1200000 non-null  object  \n 8   Health Score          1200000 non-null  float64 \n 9   Location              1200000 non-null  object  \n 10  Policy Type           1200000 non-null  object  \n 11  Previous Claims       1200000 non-null  float64 \n 12  Vehicle Age           1200000 non-null  float64 \n 13  Credit Score          1200000 non-null  float64 \n 14  Insurance Duration    1200000 non-null  float64 \n 15  Policy Start Date     1200000 non-null  object  \n 16  Customer Feedback     1200000 non-null  object  \n 17  Smoking Status        1200000 non-null  object  \n 18  Exercise Frequency    1200000 non-null  object  \n 19  Property Type         1200000 non-null  object  \n 20  Premium Amount        1200000 non-null  float64 \n 21  Age Group             1200000 non-null  category\ndtypes: category(1), float64(8), int64(2), object(11)\nmemory usage: 193.4+ MB\n","output_type":"stream"}],"execution_count":223},{"cell_type":"code","source":"sns.histplot(df['Age'], bins=10, kde=True)\nplt.title(\"Age Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:07.874715Z","iopub.execute_input":"2024-12-18T15:36:07.875033Z","iopub.status.idle":"2024-12-18T15:36:13.147021Z","shell.execute_reply.started":"2024-12-18T15:36:07.875001Z","shell.execute_reply":"2024-12-18T15:36:13.146015Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":224},{"cell_type":"code","source":"sns.countplot(x='Gender', data=df)\nplt.title(\"Gender Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:13.148797Z","iopub.execute_input":"2024-12-18T15:36:13.149284Z","iopub.status.idle":"2024-12-18T15:36:13.990163Z","shell.execute_reply.started":"2024-12-18T15:36:13.149238Z","shell.execute_reply":"2024-12-18T15:36:13.989064Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":225},{"cell_type":"code","source":"sns.histplot(df['Annual Income'], bins=10, kde=True)\nplt.title(\"Annual Income Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:13.991176Z","iopub.execute_input":"2024-12-18T15:36:13.991471Z","iopub.status.idle":"2024-12-18T15:36:19.794138Z","shell.execute_reply.started":"2024-12-18T15:36:13.991441Z","shell.execute_reply":"2024-12-18T15:36:19.793136Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":226},{"cell_type":"code","source":"sns.boxplot(x=df['Annual Income'])\nplt.title(\"Annual Income Box Plot\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:19.795477Z","iopub.execute_input":"2024-12-18T15:36:19.795842Z","iopub.status.idle":"2024-12-18T15:36:20.183714Z","shell.execute_reply.started":"2024-12-18T15:36:19.795811Z","shell.execute_reply":"2024-12-18T15:36:20.182747Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":227},{"cell_type":"code","source":"df['Marital Status'].value_counts().plot.pie(autopct='%1.1f%%', startangle=90)\nplt.title(\"Marital Status Distribution\")\n#plt.ylabel(\"\")  \nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:20.185772Z","iopub.execute_input":"2024-12-18T15:36:20.186196Z","iopub.status.idle":"2024-12-18T15:36:20.395945Z","shell.execute_reply.started":"2024-12-18T15:36:20.186148Z","shell.execute_reply":"2024-12-18T15:36:20.394784Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":228},{"cell_type":"code","source":"sns.countplot(x='Number of Dependents', data=df)\nplt.title(\"Number of Dependents Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:20.397435Z","iopub.execute_input":"2024-12-18T15:36:20.398003Z","iopub.status.idle":"2024-12-18T15:36:20.763881Z","shell.execute_reply.started":"2024-12-18T15:36:20.39794Z","shell.execute_reply":"2024-12-18T15:36:20.76296Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":229},{"cell_type":"code","source":"sns.countplot(x='Education Level', data=df, order=df['Education Level'].value_counts().index)\nplt.title(\"Education Level Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:20.765321Z","iopub.execute_input":"2024-12-18T15:36:20.765776Z","iopub.status.idle":"2024-12-18T15:36:21.515563Z","shell.execute_reply.started":"2024-12-18T15:36:20.765714Z","shell.execute_reply":"2024-12-18T15:36:21.514466Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":230},{"cell_type":"code","source":"df['Occupation'].value_counts().plot(kind='barh')\nplt.title(\"Occupation Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:21.517019Z","iopub.execute_input":"2024-12-18T15:36:21.517818Z","iopub.status.idle":"2024-12-18T15:36:21.842989Z","shell.execute_reply.started":"2024-12-18T15:36:21.517771Z","shell.execute_reply":"2024-12-18T15:36:21.841911Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":231},{"cell_type":"code","source":"sns.kdeplot(df['Health Score'], fill=True)\nplt.title(\"Health Score Density\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:21.844413Z","iopub.execute_input":"2024-12-18T15:36:21.844778Z","iopub.status.idle":"2024-12-18T15:36:27.166982Z","shell.execute_reply.started":"2024-12-18T15:36:21.84474Z","shell.execute_reply":"2024-12-18T15:36:27.165943Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":232},{"cell_type":"code","source":"sns.countplot(x='Location', data=df, order=df['Location'].value_counts().index)\nplt.title(\"Location Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:27.168533Z","iopub.execute_input":"2024-12-18T15:36:27.169023Z","iopub.status.idle":"2024-12-18T15:36:27.925892Z","shell.execute_reply.started":"2024-12-18T15:36:27.168988Z","shell.execute_reply":"2024-12-18T15:36:27.924819Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAlUAAAHHCAYAAACWQK1nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABLpUlEQVR4nO3de1hVdd738c8G5SC4MRVBRjykpZKKhYl7Oqrk1mgmi3q0vBOV7NHQW6XUbAzNqdvSyUNpOh0Um8k7tckOWhih4qTkASUPqZVjg41uJBO2UoLCev7oYj3uwERcukHfr+ta1+Vav+/6re9eEHxae+2FzTAMQwAAALgoPt5uAAAA4EpAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAlBnTJ06VTabzdttVMt3330nm82mtLS0S36stLQ02Ww2fffdd+a21q1b65577rnkx5ak9evXy2azaf369ZfleEBtRagCrlIVv4i3bdvm7VY8/PTTT5o6dWqt+wVts9nMpV69emrcuLFiYmI0ZswYffXVV5Yd59VXX70sQawmanNvQG1g42//AVentLQ0DR06VFu3blW3bt283Y7phx9+UGhoqKZMmaKpU6d6jJ05c0ZnzpxRQEDAZe/LZrPprrvu0uDBg2UYhoqKivTll19qxYoVKi4u1osvvqiUlBSz3jAMlZSUqH79+vL19a32cTp16qSmTZteUKgsKyvT6dOn5e/vb17Ja926tTp16qRVq1ZVe56a9lZeXq7S0lL5+fnJx4f/V8fVq563GwCA6qpXr57q1fPej63rr79e//Vf/+Wx7YUXXtAf/vAHPfHEE+rQoYPuvvtuSb+EsEsd/oqLixUUFCRfX98LCm5W8/Hx8UrQBWob/pcCwG/asWOH+vXrJ7vdruDgYPXu3VtffPFFpbrCwkKNGzdOrVu3lr+/v1q0aKHBgwfrhx9+kCSVlpYqNTVVMTExCgkJUVBQkG677TatW7fOnOO7775TaGioJOnZZ581326ruGJV1T1VZ86c0Z///Ge1bdtW/v7+at26tZ5++mmVlJR41FXcY/T555+re/fuCggI0LXXXqu33nrros5PkyZN9M4776hevXp6/vnnPV7Lr++pcrlcGjp0qFq0aCF/f381b95c9957r3kvVOvWrbVnzx5lZWWZr/3OO++U9P/frs3KytLjjz+uZs2aqUWLFh5jZ99TVeHTTz9V165dFRAQoKioKL333nse4+e6T+3Xc/5Wb+e6p2rFihWKiYlRYGCgmjZtqv/6r//Sf/7zH4+aIUOGKDg4WP/5z3/Uv39/BQcHKzQ0VE8++aTKysrOc/aB2oUrVQDOac+ePbrttttkt9s1YcIE1a9fX3/961915513KisrS7GxsZKkkydP6rbbbtPevXs1bNgw3XTTTfrhhx/04Ycf6vvvv1fTpk3ldrv1xhtv6KGHHtLw4cN14sQJvfnmm3I6ndqyZYu6du2q0NBQLViwQCNHjtR9992n+++/X5LUpUuXc/b46KOPasmSJXrggQf0xBNPaPPmzZo+fbr27t2rlStXetR+++23euCBB5SUlKTExEQtWrRIQ4YMUUxMjG644YYan6eWLVvqjjvu0Lp16+R2u2W326usS0hI0J49ezR69Gi1bt1aR48eVUZGhvLy8tS6dWvNmTNHo0ePVnBwsP70pz9JksLCwjzmePzxxxUaGqrU1FQVFxf/Zl/ffPONBgwYoBEjRigxMVGLFy/Wgw8+qPT0dN11110X9Bqr09vZKt5evvnmmzV9+nTl5+dr7ty52rhxo3bs2KFGjRqZtWVlZXI6nYqNjdVf/vIXffbZZ3rppZfUtm1bjRw58oL6BLzKAHBVWrx4sSHJ2Lp16zlr+vfvb/j5+RkHDhwwtx0+fNho2LChcfvtt5vbUlNTDUnGe++9V2mO8vJywzAM48yZM0ZJSYnH2PHjx42wsDBj2LBh5raCggJDkjFlypRKc02ZMsU4+8dWbm6uIcl49NFHPeqefPJJQ5Kxdu1ac1urVq0MScaGDRvMbUePHjX8/f2NJ5544pznoIIkIzk5+ZzjY8aMMSQZX375pWEYhnHw4EFDkrF48WLztUoyZs6c+ZvHueGGG4w77rij0vaKr9ett95qnDlzpsqxgwcPmtsqXu8//vEPc1tRUZHRvHlz48YbbzS3/fqc/tac5+pt3bp1hiRj3bp1hmEYRmlpqdGsWTOjU6dOxs8//2zWrVq1ypBkpKammtsSExMNSca0adM85rzxxhuNmJiYSscCajPe/gNQpbKyMn366afq37+/rr32WnN78+bN9fDDD+vzzz+X2+2WJP3jH/9QdHS07rvvvkrzVLy15OvrKz8/P0m/3Nj8448/6syZM+rWrZu2b99eox4//vhjSfK4QVySnnjiCUnS6tWrPbZHRUXptttuM9dDQ0PVvn17/etf/6rR8c8WHBwsSTpx4kSV44GBgfLz89P69et1/PjxGh9n+PDh1b5/KiIiwuNrYrfbNXjwYO3YsUMul6vGPZzPtm3bdPToUT3++OMe91rFx8erQ4cOlb4ukjRixAiP9dtuu82SrwtwORGqAFSpoKBAP/30k9q3b19prGPHjiovL9ehQ4ckSQcOHFCnTp3OO+eSJUvUpUsXBQQEqEmTJgoNDdXq1atVVFRUox7//e9/y8fHR+3atfPYHh4erkaNGunf//63x/aWLVtWmuOaa665qJBT4eTJk5Kkhg0bVjnu7++vF198UZ988onCwsJ0++23a8aMGRccbtq0aVPt2nbt2lW6X+r666+XpCrvv7JKxXmv6nunQ4cOlb4uAQEB5r10Faz6ugCXE6EKwGXx97//XUOGDFHbtm315ptvKj09XRkZGerVq5fKy8svau7qPhD0XFd4DAueLLN79275+vr+ZugZO3asvv76a02fPl0BAQF65pln1LFjR+3YsaPaxwkMDLzoXs92rnN3OW8S9+YnFwErEaoAVCk0NFQNGjTQ/v37K43t27dPPj4+ioyMlCS1bdtWu3fv/s353n33XV177bV677339Mgjj8jpdCouLk6nTp3yqLuQJ6a3atVK5eXl+uabbzy25+fnq7CwUK1atar2XBcjLy9PWVlZcjgc57xSVaFt27Z64okn9Omnn2r37t0qLS3VSy+9ZI5b+cT4b7/9tlJg/PrrryX98mk+6ZcrQtIvn94826+vJl1IbxXnvarvnf3791+2rwtwuRGqAFTJ19dXffr00QcffODxVlF+fr6WLl2qW2+91fyUW0JCgr788stKn7aT/v9VoIqrEWf/kt+8ebOys7M96hs0aCCp8i/5qlQ8E2rOnDke22fNmiXpl3t4LrUff/xRDz30kMrKysxPxVXlp59+qhQg27Ztq4YNG3o8/iEoKKhar706Dh8+7PE1cbvdeuutt9S1a1eFh4ebPUjShg0bzLri4mItWbKk0nzV7a1bt25q1qyZFi5c6PHaPvnkE+3du/eyfF0Ab+CRCsBVbtGiRUpPT6+0fcyYMXruueeUkZGhW2+9VY8//rjq1aunv/71ryopKdGMGTPM2vHjx+vdd9/Vgw8+qGHDhikmJkY//vijPvzwQy1cuFDR0dG655579N577+m+++5TfHy8Dh48qIULFyoqKsq8H0n65e2tqKgoLVu2TNdff70aN26sTp06VXnPVnR0tBITE/Xaa6+psLBQd9xxh7Zs2aIlS5aof//+6tmzp6Xn6uuvv9bf//53GYYht9ttPlH95MmTmjVrlvr27fub+/bu3Vv/5//8H0VFRalevXpauXKl8vPzNXDgQLMuJiZGCxYs0HPPPad27dqpWbNm6tWrV436vf7665WUlKStW7cqLCxMixYtUn5+vhYvXmzW9OnTRy1btlRSUpLGjx8vX19fLVq0SKGhocrLy/OYr7q91a9fXy+++KKGDh2qO+64Qw899JD5SIXWrVtr3LhxNXo9QK3n1c8eAvCaio/Mn2s5dOiQYRiGsX37dsPpdBrBwcFGgwYNjJ49exqbNm2qNN+xY8eMUaNGGb/73e8MPz8/o0WLFkZiYqLxww8/GIbxy6MV/ud//sdo1aqV4e/vb9x4443GqlWrjMTERKNVq1Yec23atMmIiYkx/Pz8PB6vUNXH/0+fPm08++yzRps2bYz69esbkZGRxqRJk4xTp0551LVq1cqIj4+v1Pcdd9xR5WMCfu3sc+Pj42M0atTIuPHGG40xY8YYe/bsqVT/60cq/PDDD0ZycrLRoUMHIygoyAgJCTFiY2ON5cuXe+zncrmM+Ph4o2HDhoYks7ffegTGuR6pEB8fb6xZs8bo0qWL4e/vb3To0MFYsWJFpf1zcnKM2NhYw8/Pz2jZsqUxa9asKuc8V2+/fqRChWXLlhk33nij4e/vbzRu3NgYNGiQ8f3333vUJCYmGkFBQZV6OtejHoDajL/9BwAAYAHuqQIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAjz88zIqLy/X4cOH1bBhQ0v/FAUAALh0DMPQiRMnFBERIR+fc1+PIlRdRocPHzb/VhoAAKhbDh06pBYtWpxznFB1GVX8odVDhw6ZfzMNAADUbm63W5GRkef9g+mEqsuo4i0/u91OqAIAoI4536073KgOAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYoNaEqhdeeEE2m01jx441t506dUrJyclq0qSJgoODlZCQoPz8fI/98vLyFB8frwYNGqhZs2YaP368zpw541Gzfv163XTTTfL391e7du2UlpZW6fjz589X69atFRAQoNjYWG3ZssVjvDq9AACAq1etCFVbt27VX//6V3Xp0sVj+7hx4/TRRx9pxYoVysrK0uHDh3X//feb42VlZYqPj1dpaak2bdqkJUuWKC0tTampqWbNwYMHFR8fr549eyo3N1djx47Vo48+qjVr1pg1y5YtU0pKiqZMmaLt27crOjpaTqdTR48erXYvAADgKmd42YkTJ4zrrrvOyMjIMO644w5jzJgxhmEYRmFhoVG/fn1jxYoVZu3evXsNSUZ2drZhGIbx8ccfGz4+PobL5TJrFixYYNjtdqOkpMQwDMOYMGGCccMNN3gcc8CAAYbT6TTXu3fvbiQnJ5vrZWVlRkREhDF9+vRq91IdRUVFhiSjqKio2vsAAADvqu7v73rejXRScnKy4uPjFRcXp+eee87cnpOTo9OnTysuLs7c1qFDB7Vs2VLZ2dnq0aOHsrOz1blzZ4WFhZk1TqdTI0eO1J49e3TjjTcqOzvbY46Kmoq3GUtLS5WTk6NJkyaZ4z4+PoqLi1N2dna1e6lKSUmJSkpKzHW3212DM+QpZvxbFz0Hriw5Mwd7uwXlTevs7RZQi7RM3eXtFgCv8Gqoeuedd7R9+3Zt3bq10pjL5ZKfn58aNWrksT0sLEwul8usOTtQVYxXjP1Wjdvt1s8//6zjx4+rrKysypp9+/ZVu5eqTJ8+Xc8+++w5xwEAwJXDa/dUHTp0SGPGjNHbb7+tgIAAb7VxSU2aNElFRUXmcujQIW+3BAAALhGvXanKycnR0aNHddNNN5nbysrKtGHDBs2bN09r1qxRaWmpCgsLPa4Q5efnKzw8XJIUHh5e6VN6FZ/IO7vm15/Sy8/Pl91uV2BgoHx9feXr61tlzdlznK+Xqvj7+8vf37+aZwQAYJVbXrnF2y2gFtk4euNlOY7XrlT17t1bu3btUm5urrl069ZNgwYNMv9dv359ZWZmmvvs379feXl5cjgckiSHw6Fdu3Z5fEovIyNDdrtdUVFRZs3Zc1TUVMzh5+enmJgYj5ry8nJlZmaaNTExMeftBQAAXN28dqWqYcOG6tSpk8e2oKAgNWnSxNyelJSklJQUNW7cWHa7XaNHj5bD4TBvDO/Tp4+ioqL0yCOPaMaMGXK5XJo8ebKSk5PNK0QjRozQvHnzNGHCBA0bNkxr167V8uXLtXr1avO4KSkpSkxMVLdu3dS9e3fNmTNHxcXFGjp0qCQpJCTkvL0AAICrm9c//fdbZs+eLR8fHyUkJKikpEROp1OvvvqqOe7r66tVq1Zp5MiRcjgcCgoKUmJioqZNm2bWtGnTRqtXr9a4ceM0d+5ctWjRQm+88YacTqdZM2DAABUUFCg1NVUul0tdu3ZVenq6x83r5+sFAABc3WyGYRjebuJq4Xa7FRISoqKiItnt9hrNwSMV8Gs8UgG1TW14pAL3VOFsF3tPVXV/f9eKJ6oDAADUdYQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMACXg1VCxYsUJcuXWS322W32+VwOPTJJ5+Y43feeadsNpvHMmLECI858vLyFB8frwYNGqhZs2YaP368zpw541Gzfv163XTTTfL391e7du2UlpZWqZf58+erdevWCggIUGxsrLZs2eIxfurUKSUnJ6tJkyYKDg5WQkKC8vPzrTsZAACgTvNqqGrRooVeeOEF5eTkaNu2berVq5fuvfde7dmzx6wZPny4jhw5Yi4zZswwx8rKyhQfH6/S0lJt2rRJS5YsUVpamlJTU82agwcPKj4+Xj179lRubq7Gjh2rRx99VGvWrDFrli1bppSUFE2ZMkXbt29XdHS0nE6njh49ataMGzdOH330kVasWKGsrCwdPnxY999//yU+QwAAoK6wGYZheLuJszVu3FgzZ85UUlKS7rzzTnXt2lVz5sypsvaTTz7RPffco8OHDyssLEyStHDhQk2cOFEFBQXy8/PTxIkTtXr1au3evdvcb+DAgSosLFR6erokKTY2VjfffLPmzZsnSSovL1dkZKRGjx6tp556SkVFRQoNDdXSpUv1wAMPSJL27dunjh07Kjs7Wz169KjWa3O73QoJCVFRUZHsdnuNzk/M+LdqtB+uXDkzB3u7BeVN6+ztFlCLtEzd5e0WdMsrt3i7BdQiG0dvvKj9q/v7u9bcU1VWVqZ33nlHxcXFcjgc5va3335bTZs2VadOnTRp0iT99NNP5lh2drY6d+5sBipJcjqdcrvd5tWu7OxsxcXFeRzL6XQqOztbklRaWqqcnByPGh8fH8XFxZk1OTk5On36tEdNhw4d1LJlS7OmKiUlJXK73R4LAAC4MtXzdgO7du2Sw+HQqVOnFBwcrJUrVyoqKkqS9PDDD6tVq1aKiIjQzp07NXHiRO3fv1/vvfeeJMnlcnkEKknmusvl+s0at9utn3/+WcePH1dZWVmVNfv27TPn8PPzU6NGjSrVVBynKtOnT9ezzz57gWcEAADURV4PVe3bt1dubq6Kior07rvvKjExUVlZWYqKitJjjz1m1nXu3FnNmzdX7969deDAAbVt29aLXVfPpEmTlJKSYq673W5FRkZ6sSMAAHCpeP3tPz8/P7Vr104xMTGaPn26oqOjNXfu3CprY2NjJUnffvutJCk8PLzSJ/Aq1sPDw3+zxm63KzAwUE2bNpWvr2+VNWfPUVpaqsLCwnPWVMXf39/8ZGPFAgAArkxeD1W/Vl5erpKSkirHcnNzJUnNmzeXJDkcDu3atcvjU3oZGRmy2+3mW4gOh0OZmZke82RkZJj3bfn5+SkmJsajpry8XJmZmWZNTEyM6tev71Gzf/9+5eXledz/BQAArl5efftv0qRJ6tevn1q2bKkTJ05o6dKlWr9+vdasWaMDBw5o6dKluvvuu9WkSRPt3LlT48aN0+23364uXbpIkvr06aOoqCg98sgjmjFjhlwulyZPnqzk5GT5+/tLkkaMGKF58+ZpwoQJGjZsmNauXavly5dr9erVZh8pKSlKTExUt27d1L17d82ZM0fFxcUaOnSoJCkkJERJSUlKSUlR48aNZbfbNXr0aDkcjmp/8g8AAFzZvBqqjh49qsGDB+vIkSMKCQlRly5dtGbNGt111106dOiQPvvsMzPgREZGKiEhQZMnTzb39/X11apVqzRy5Eg5HA4FBQUpMTFR06ZNM2vatGmj1atXa9y4cZo7d65atGihN954Q06n06wZMGCACgoKlJqaKpfLpa5duyo9Pd3j5vXZs2fLx8dHCQkJKikpkdPp1Kuvvnp5ThQAAKj1at1zqq5kPKcKlwLPqUJtw3OqUNtcdc+pAgAAqMsIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAa+GqgULFqhLly6y2+2y2+1yOBz65JNPzPFTp04pOTlZTZo0UXBwsBISEpSfn+8xR15enuLj49WgQQM1a9ZM48eP15kzZzxq1q9fr5tuukn+/v5q166d0tLSKvUyf/58tW7dWgEBAYqNjdWWLVs8xqvTCwAAuHp5NVS1aNFCL7zwgnJycrRt2zb16tVL9957r/bs2SNJGjdunD766COtWLFCWVlZOnz4sO6//35z/7KyMsXHx6u0tFSbNm3SkiVLlJaWptTUVLPm4MGDio+PV8+ePZWbm6uxY8fq0Ucf1Zo1a8yaZcuWKSUlRVOmTNH27dsVHR0tp9Opo0ePmjXn6wUAAFzdbIZhGN5u4myNGzfWzJkz9cADDyg0NFRLly7VAw88IEnat2+fOnbsqOzsbPXo0UOffPKJ7rnnHh0+fFhhYWGSpIULF2rixIkqKCiQn5+fJk6cqNWrV2v37t3mMQYOHKjCwkKlp6dLkmJjY3XzzTdr3rx5kqTy8nJFRkZq9OjReuqpp1RUVHTeXqrD7XYrJCRERUVFstvtNTo/MePfqtF+uHLlzBzs7RaUN62zt1tALdIydZe3W9Atr9zi7RZQi2wcvfGi9q/u7+9ac09VWVmZ3nnnHRUXF8vhcCgnJ0enT59WXFycWdOhQwe1bNlS2dnZkqTs7Gx17tzZDFSS5HQ65Xa7zatd2dnZHnNU1FTMUVpaqpycHI8aHx8fxcXFmTXV6QUAAFzd6nm7gV27dsnhcOjUqVMKDg7WypUrFRUVpdzcXPn5+alRo0Ye9WFhYXK5XJIkl8vlEagqxivGfqvG7Xbr559/1vHjx1VWVlZlzb59+8w5ztdLVUpKSlRSUmKuu93u85wNAABQV3n9SlX79u2Vm5urzZs3a+TIkUpMTNRXX33l7bYsMX36dIWEhJhLZGSkt1sCAACXiNdDlZ+fn9q1a6eYmBhNnz5d0dHRmjt3rsLDw1VaWqrCwkKP+vz8fIWHh0uSwsPDK30Cr2L9fDV2u12BgYFq2rSpfH19q6w5e47z9VKVSZMmqaioyFwOHTpUvZMCAADqHK+Hql8rLy9XSUmJYmJiVL9+fWVmZppj+/fvV15enhwOhyTJ4XBo165dHp/Sy8jIkN1uV1RUlFlz9hwVNRVz+Pn5KSYmxqOmvLxcmZmZZk11eqmKv7+/+biIigUAAFyZvHpP1aRJk9SvXz+1bNlSJ06c0NKlS7V+/XqtWbNGISEhSkpKUkpKiho3biy73a7Ro0fL4XCYn7br06ePoqKi9Mgjj2jGjBlyuVyaPHmykpOT5e/vL0kaMWKE5s2bpwkTJmjYsGFau3atli9frtWrV5t9pKSkKDExUd26dVP37t01Z84cFRcXa+jQoZJUrV4AAMDVzauh6ujRoxo8eLCOHDmikJAQdenSRWvWrNFdd90lSZo9e7Z8fHyUkJCgkpISOZ1Ovfrqq+b+vr6+WrVqlUaOHCmHw6GgoCAlJiZq2rRpZk2bNm20evVqjRs3TnPnzlWLFi30xhtvyOl0mjUDBgxQQUGBUlNT5XK51LVrV6Wnp3vcvH6+XgAAwNWt1j2n6krGc6pwKfCcKtQ2PKcKtc1V95wqAACAuoxQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYwKuhavr06br55pvVsGFDNWvWTP3799f+/fs9au68807ZbDaPZcSIER41eXl5io+PV4MGDdSsWTONHz9eZ86c8ahZv369brrpJvn7+6tdu3ZKS0ur1M/8+fPVunVrBQQEKDY2Vlu2bPEYP3XqlJKTk9WkSRMFBwcrISFB+fn51pwMAABQp3k1VGVlZSk5OVlffPGFMjIydPr0afXp00fFxcUedcOHD9eRI0fMZcaMGeZYWVmZ4uPjVVpaqk2bNmnJkiVKS0tTamqqWXPw4EHFx8erZ8+eys3N1dixY/Xoo49qzZo1Zs2yZcuUkpKiKVOmaPv27YqOjpbT6dTRo0fNmnHjxumjjz7SihUrlJWVpcOHD+v++++/hGcIAADUFTbDMAxvN1GhoKBAzZo1U1ZWlm6//XZJv1yp6tq1q+bMmVPlPp988onuueceHT58WGFhYZKkhQsXauLEiSooKJCfn58mTpyo1atXa/fu3eZ+AwcOVGFhodLT0yVJsbGxuvnmmzVv3jxJUnl5uSIjIzV69Gg99dRTKioqUmhoqJYuXaoHHnhAkrRv3z517NhR2dnZ6tGjx3lfn9vtVkhIiIqKimS322t0jmLGv1Wj/XDlypk52NstKG9aZ2+3gFqkZeoub7egW165xdstoBbZOHrjRe1f3d/fteqeqqKiIklS48aNPba//fbbatq0qTp16qRJkybpp59+Mseys7PVuXNnM1BJktPplNvt1p49e8yauLg4jzmdTqeys7MlSaWlpcrJyfGo8fHxUVxcnFmTk5Oj06dPe9R06NBBLVu2NGsAAMDVq563G6hQXl6usWPH6pZbblGnTp3M7Q8//LBatWqliIgI7dy5UxMnTtT+/fv13nvvSZJcLpdHoJJkrrtcrt+scbvd+vnnn3X8+HGVlZVVWbNv3z5zDj8/PzVq1KhSTcVxfq2kpEQlJSXmutvtru7pAAAAdUytCVXJycnavXu3Pv/8c4/tjz32mPnvzp07q3nz5urdu7cOHDigtm3bXu42L8j06dP17LPPersNAABwGdSKt/9GjRqlVatWad26dWrRosVv1sbGxkqSvv32W0lSeHh4pU/gVayHh4f/Zo3dbldgYKCaNm0qX1/fKmvOnqO0tFSFhYXnrPm1SZMmqaioyFwOHTr0m68NAADUXV4NVYZhaNSoUVq5cqXWrl2rNm3anHef3NxcSVLz5s0lSQ6HQ7t27fL4lF5GRobsdruioqLMmszMTI95MjIy5HA4JEl+fn6KiYnxqCkvL1dmZqZZExMTo/r163vU7N+/X3l5eWbNr/n7+8tut3ssAADgyuTVt/+Sk5O1dOlSffDBB2rYsKF5b1JISIgCAwN14MABLV26VHfffbeaNGminTt3aty4cbr99tvVpUsXSVKfPn0UFRWlRx55RDNmzJDL5dLkyZOVnJwsf39/SdKIESM0b948TZgwQcOGDdPatWu1fPlyrV692uwlJSVFiYmJ6tatm7p37645c+aouLhYQ4cONXtKSkpSSkqKGjduLLvdrtGjR8vhcFTrk38AAODK5tVQtWDBAkm/PDbhbIsXL9aQIUPk5+enzz77zAw4kZGRSkhI0OTJk81aX19frVq1SiNHjpTD4VBQUJASExM1bdo0s6ZNmzZavXq1xo0bp7lz56pFixZ644035HQ6zZoBAwaooKBAqampcrlc6tq1q9LT0z1uXp89e7Z8fHyUkJCgkpISOZ1Ovfrqq5fo7AAAgLqkVj2n6krHc6pwKfCcKtQ2PKcKtc1V+ZwqAACAuopQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABWoUqnr16qXCwsJK291ut3r16nWxPQEAANQ5NQpV69evV2lpaaXtp06d0j//+c+LbgoAAKCuqXchxTt37jT//dVXX8nlcpnrZWVlSk9P1+9+9zvrugMAAKgjLihUde3aVTabTTabrcq3+QIDA/XKK69Y1hwAAEBdcUGh6uDBgzIMQ9dee622bNmi0NBQc8zPz0/NmjWTr6+v5U0CAADUdhcUqlq1aiVJKi8vvyTNAAAA1FUXFKrO9s0332jdunU6evRopZCVmpp60Y0BAADUJTUKVa+//rpGjhyppk2bKjw8XDabzRyz2WyEKgAAcNWpUah67rnn9Pzzz2vixIlW9wMAAFAn1eg5VcePH9eDDz5odS8AAAB1Vo1C1YMPPqhPP/3U6l4AAADqrBq9/deuXTs988wz+uKLL9S5c2fVr1/fY/y///u/LWkOAACgrqhRqHrttdcUHBysrKwsZWVleYzZbDZCFQAAuOrUKFQdPHjQ6j4AAADqtBrdUwUAAABPNbpSNWzYsN8cX7RoUY2aAQAAqKtqFKqOHz/usX769Gnt3r1bhYWFVf6hZQAAgCtdjULVypUrK20rLy/XyJEj1bZt24tuCgAAoK6x7J4qHx8fpaSkaPbs2VZNCQAAUGdYeqP6gQMHdObMGSunBAAAqBNq9PZfSkqKx7phGDpy5IhWr16txMRESxoDAACoS2oUqnbs2OGx7uPjo9DQUL300kvn/WQgAADAlahGb/+tW7fOY8nMzNQ777yjxx57TPXqVT+nTZ8+XTfffLMaNmyoZs2aqX///tq/f79HzalTp5ScnKwmTZooODhYCQkJys/P96jJy8tTfHy8GjRooGbNmmn8+PGV3oZcv369brrpJvn7+6tdu3ZKS0ur1M/8+fPVunVrBQQEKDY2Vlu2bLngXgAAwNXpou6pKigo0Oeff67PP/9cBQUFF7x/VlaWkpOT9cUXXygjI0OnT59Wnz59VFxcbNaMGzdOH330kVasWKGsrCwdPnxY999/vzleVlam+Ph4lZaWatOmTVqyZInS0tKUmppq1hw8eFDx8fHq2bOncnNzNXbsWD366KNas2aNWbNs2TKlpKRoypQp2r59u6Kjo+V0OnX06NFq9wIAAK5eNsMwjAvdqbi4WKNHj9Zbb72l8vJySZKvr68GDx6sV155RQ0aNKhRMwUFBWrWrJmysrJ0++23q6ioSKGhoVq6dKkeeOABSdK+ffvUsWNHZWdnq0ePHvrkk090zz336PDhwwoLC5MkLVy4UBMnTlRBQYH8/Pw0ceJErV69Wrt37zaPNXDgQBUWFio9PV2SFBsbq5tvvlnz5s2T9MsjIiIjIzV69Gg99dRT1erlfNxut0JCQlRUVCS73V6jcxQz/q0a7YcrV87Mwd5uQXnTOnu7BdQiLVN3ebsF3fLKLd5uAbXIxtEbL2r/6v7+rtGVqpSUFGVlZemjjz5SYWGhCgsL9cEHHygrK0tPPPFEjZsuKiqSJDVu3FiSlJOTo9OnTysuLs6s6dChg1q2bKns7GxJUnZ2tjp37mwGKklyOp1yu93as2ePWXP2HBU1FXOUlpYqJyfHo8bHx0dxcXFmTXV6+bWSkhK53W6PBQAAXJlqFKr+8Y9/6M0331S/fv1kt9tlt9t199136/XXX9e7775bo0bKy8s1duxY3XLLLerUqZMkyeVyyc/PT40aNfKoDQsLk8vlMmvODlQV4xVjv1Xjdrv1888/64cfflBZWVmVNWfPcb5efm369OkKCQkxl8jIyGqeDQAAUNfUKFT99NNPlQKIJDVr1kw//fRTjRpJTk7W7t279c4779Ro/9po0qRJKioqMpdDhw55uyUAAHCJ1ChUORwOTZkyRadOnTK3/fzzz3r22WflcDgueL5Ro0Zp1apVWrdunVq0aGFuDw8PV2lpqQoLCz3q8/PzFR4ebtb8+hN4Fevnq7Hb7QoMDFTTpk3l6+tbZc3Zc5yvl1/z9/c3r+RVLAAA4MpUo1A1Z84cbdy4US1atFDv3r3Vu3dvRUZGauPGjZo7d2615zEMQ6NGjdLKlSu1du1atWnTxmM8JiZG9evXV2Zmprlt//79ysvLM8Obw+HQrl27PD6ll5GRIbvdrqioKLPm7Dkqairm8PPzU0xMjEdNeXm5MjMzzZrq9AIAAK5eNXr4Z+fOnfXNN9/o7bff1r59+yRJDz30kAYNGqTAwMBqz5OcnKylS5fqgw8+UMOGDc17k0JCQhQYGKiQkBAlJSUpJSVFjRs3lt1u1+jRo+VwOMxP2/Xp00dRUVF65JFHNGPGDLlcLk2ePFnJycny9/eXJI0YMULz5s3ThAkTNGzYMK1du1bLly/X6tWrzV5SUlKUmJiobt26qXv37pozZ46Ki4s1dOhQs6fz9QIAAK5eNQpV06dPV1hYmIYPH+6xfdGiRSooKNDEiROrNc+CBQskSXfeeafH9sWLF2vIkCGSpNmzZ8vHx0cJCQkqKSmR0+nUq6++atb6+vpq1apVGjlypBwOh4KCgpSYmKhp06aZNW3atNHq1as1btw4zZ07Vy1atNAbb7whp9Np1gwYMEAFBQVKTU2Vy+VS165dlZ6e7nHv2Pl6AQAAV68aPaeqdevWWrp0qX7/+997bN+8ebMGDhyogwcPWtbglYTnVOFS4DlVqG14ThVqm1r9nCqXy6XmzZtX2h4aGqojR47UZEoAAIA6rUahquKm9F/buHGjIiIiLropAACAuqZG91QNHz5cY8eO1enTp9WrVy9JUmZmpiZMmHBRT1QHAACoq2oUqsaPH69jx47p8ccfV2lpqSQpICBAEydO1KRJkyxtEAAAoC6oUaiy2Wx68cUX9cwzz2jv3r0KDAzUddddZz7CAAAA4GpTo1BVITg4WDfffLNVvQAAANRZNbpRHQAAAJ4IVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFjAq6Fqw4YN+sMf/qCIiAjZbDa9//77HuNDhgyRzWbzWPr27etR8+OPP2rQoEGy2+1q1KiRkpKSdPLkSY+anTt36rbbblNAQIAiIyM1Y8aMSr2sWLFCHTp0UEBAgDp37qyPP/7YY9wwDKWmpqp58+YKDAxUXFycvvnmG2tOBAAAqPO8GqqKi4sVHR2t+fPnn7Omb9++OnLkiLn87//+r8f4oEGDtGfPHmVkZGjVqlXasGGDHnvsMXPc7XarT58+atWqlXJycjRz5kxNnTpVr732mlmzadMmPfTQQ0pKStKOHTvUv39/9e/fX7t37zZrZsyYoZdfflkLFy7U5s2bFRQUJKfTqVOnTll4RgAAQF1Vz5sH79evn/r16/ebNf7+/goPD69ybO/evUpPT9fWrVvVrVs3SdIrr7yiu+++W3/5y18UERGht99+W6WlpVq0aJH8/Px0ww03KDc3V7NmzTLD19y5c9W3b1+NHz9ekvTnP/9ZGRkZmjdvnhYuXCjDMDRnzhxNnjxZ9957ryTprbfeUlhYmN5//30NHDjQqlMCAADqqFp/T9X69evVrFkztW/fXiNHjtSxY8fMsezsbDVq1MgMVJIUFxcnHx8fbd682ay5/fbb5efnZ9Y4nU7t379fx48fN2vi4uI8jut0OpWdnS1JOnjwoFwul0dNSEiIYmNjzZqqlJSUyO12eywAAODKVKtDVd++ffXWW28pMzNTL774orKystSvXz+VlZVJklwul5o1a+axT7169dS4cWO5XC6zJiwszKOmYv18NWePn71fVTVVmT59ukJCQswlMjLygl4/AACoO7z69t/5nP22WufOndWlSxe1bdtW69evV+/evb3YWfVMmjRJKSkp5rrb7SZYAQBwharVV6p+7dprr1XTpk317bffSpLCw8N19OhRj5ozZ87oxx9/NO/DCg8PV35+vkdNxfr5as4eP3u/qmqq4u/vL7vd7rEAAIArU50KVd9//72OHTum5s2bS5IcDocKCwuVk5Nj1qxdu1bl5eWKjY01azZs2KDTp0+bNRkZGWrfvr2uueYasyYzM9PjWBkZGXI4HJKkNm3aKDw83KPG7XZr8+bNZg0AALi6eTVUnTx5Urm5ucrNzZX0yw3hubm5ysvL08mTJzV+/Hh98cUX+u6775SZmal7771X7dq1k9PplCR17NhRffv21fDhw7VlyxZt3LhRo0aN0sCBAxURESFJevjhh+Xn56ekpCTt2bNHy5Yt09y5cz3elhszZozS09P10ksvad++fZo6daq2bdumUaNGSZJsNpvGjh2r5557Th9++KF27dqlwYMHKyIiQv3797+s5wwAANROXr2natu2berZs6e5XhF0EhMTtWDBAu3cuVNLlixRYWGhIiIi1KdPH/35z3+Wv7+/uc/bb7+tUaNGqXfv3vLx8VFCQoJefvllczwkJESffvqpkpOTFRMTo6ZNmyo1NdXjWVa///3vtXTpUk2ePFlPP/20rrvuOr3//vvq1KmTWTNhwgQVFxfrscceU2FhoW699Valp6crICDgUp4iAABQR9gMwzC83cTVwu12KyQkREVFRTW+vypm/FsWd4W6LmfmYG+3oLxpnb3dAmqRlqm7vN2CbnnlFm+3gFpk4+iNF7V/dX9/16l7qgAAAGorQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAGvhqoNGzboD3/4gyIiImSz2fT+++97jBuGodTUVDVv3lyBgYGKi4vTN99841Hz448/atCgQbLb7WrUqJGSkpJ08uRJj5qdO3fqtttuU0BAgCIjIzVjxoxKvaxYsUIdOnRQQECAOnfurI8//viCewEAAFcvr4aq4uJiRUdHa/78+VWOz5gxQy+//LIWLlyozZs3KygoSE6nU6dOnTJrBg0apD179igjI0OrVq3Shg0b9Nhjj5njbrdbffr0UatWrZSTk6OZM2dq6tSpeu2118yaTZs26aGHHlJSUpJ27Nih/v37q3///tq9e/cF9QIAAK5eNsMwDG83IUk2m00rV65U//79Jf1yZSgiIkJPPPGEnnzySUlSUVGRwsLClJaWpoEDB2rv3r2KiorS1q1b1a1bN0lSenq67r77bn3//feKiIjQggUL9Kc//Ukul0t+fn6SpKeeekrvv/++9u3bJ0kaMGCAiouLtWrVKrOfHj16qGvXrlq4cGG1eqkOt9utkJAQFRUVyW631+g8xYx/q0b74cqVM3Owt1tQ3rTO3m4BtUjL1F3ebkG3vHKLt1tALbJx9MaL2r+6v79r7T1VBw8elMvlUlxcnLktJCREsbGxys7OliRlZ2erUaNGZqCSpLi4OPn4+Gjz5s1mze23324GKklyOp3av3+/jh8/btacfZyKmorjVKeXqpSUlMjtdnssAADgylRrQ5XL5ZIkhYWFeWwPCwszx1wul5o1a+YxXq9ePTVu3Nijpqo5zj7GuWrOHj9fL1WZPn26QkJCzCUyMvI8rxoAANRVtTZUXQkmTZqkoqIiczl06JC3WwIAAJdIrQ1V4eHhkqT8/HyP7fn5+eZYeHi4jh496jF+5swZ/fjjjx41Vc1x9jHOVXP2+Pl6qYq/v7/sdrvHAgAArky1NlS1adNG4eHhyszMNLe53W5t3rxZDodDkuRwOFRYWKicnByzZu3atSovL1dsbKxZs2HDBp0+fdqsycjIUPv27XXNNdeYNWcfp6Km4jjV6QUAAFzdvBqqTp48qdzcXOXm5kr65Ybw3Nxc5eXlyWazaezYsXruuef04YcfateuXRo8eLAiIiLMTwh27NhRffv21fDhw7VlyxZt3LhRo0aN0sCBAxURESFJevjhh+Xn56ekpCTt2bNHy5Yt09y5c5WSkmL2MWbMGKWnp+ull17Svn37NHXqVG3btk2jRo2SpGr1AgAArm71vHnwbdu2qWfPnuZ6RdBJTExUWlqaJkyYoOLiYj322GMqLCzUrbfeqvT0dAUEBJj7vP322xo1apR69+4tHx8fJSQk6OWXXzbHQ0JC9Omnnyo5OVkxMTFq2rSpUlNTPZ5l9fvf/15Lly7V5MmT9fTTT+u6667T+++/r06dOpk11ekFAABcvWrNc6quBjynCpcCz6lCbcNzqlDbXPXPqQIAAKhLCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAVqdaiaOnWqbDabx9KhQwdz/NSpU0pOTlaTJk0UHByshIQE5efne8yRl5en+Ph4NWjQQM2aNdP48eN15swZj5r169frpptukr+/v9q1a6e0tLRKvcyfP1+tW7dWQECAYmNjtWXLlkvymgEAQN1Uq0OVJN1www06cuSIuXz++efm2Lhx4/TRRx9pxYoVysrK0uHDh3X//feb42VlZYqPj1dpaak2bdqkJUuWKC0tTampqWbNwYMHFR8fr549eyo3N1djx47Vo48+qjVr1pg1y5YtU0pKiqZMmaLt27crOjpaTqdTR48evTwnAQAA1Hq1PlTVq1dP4eHh5tK0aVNJUlFRkd58803NmjVLvXr1UkxMjBYvXqxNmzbpiy++kCR9+umn+uqrr/T3v/9dXbt2Vb9+/fTnP/9Z8+fPV2lpqSRp4cKFatOmjV566SV17NhRo0aN0gMPPKDZs2ebPcyaNUvDhw/X0KFDFRUVpYULF6pBgwZatGjR5T8hAACgVqr1oeqbb75RRESErr32Wg0aNEh5eXmSpJycHJ0+fVpxcXFmbYcOHdSyZUtlZ2dLkrKzs9W5c2eFhYWZNU6nU263W3v27DFrzp6joqZijtLSUuXk5HjU+Pj4KC4uzqw5l5KSErndbo8FAABcmWp1qIqNjVVaWprS09O1YMECHTx4ULfddptOnDghl8slPz8/NWrUyGOfsLAwuVwuSZLL5fIIVBXjFWO/VeN2u/Xzzz/rhx9+UFlZWZU1FXOcy/Tp0xUSEmIukZGRF3wOAABA3VDP2w38ln79+pn/7tKli2JjY9WqVSstX75cgYGBXuyseiZNmqSUlBRz3e12E6wAALhC1eorVb/WqFEjXX/99fr2228VHh6u0tJSFRYWetTk5+crPDxckhQeHl7p04AV6+ersdvtCgwMVNOmTeXr61tlTcUc5+Lv7y+73e6xAACAK1OdClUnT57UgQMH1Lx5c8XExKh+/frKzMw0x/fv36+8vDw5HA5JksPh0K5duzw+pZeRkSG73a6oqCiz5uw5Kmoq5vDz81NMTIxHTXl5uTIzM80aAACAWh2qnnzySWVlZem7777Tpk2bdN9998nX11cPPfSQQkJClJSUpJSUFK1bt045OTkaOnSoHA6HevToIUnq06ePoqKi9Mgjj+jLL7/UmjVrNHnyZCUnJ8vf31+SNGLECP3rX//ShAkTtG/fPr366qtavny5xo0bZ/aRkpKi119/XUuWLNHevXs1cuRIFRcXa+jQoV45LwAAoPap1fdUff/993rooYd07NgxhYaG6tZbb9UXX3yh0NBQSdLs2bPl4+OjhIQElZSUyOl06tVXXzX39/X11apVqzRy5Eg5HA4FBQUpMTFR06ZNM2vatGmj1atXa9y4cZo7d65atGihN954Q06n06wZMGCACgoKlJqaKpfLpa5duyo9Pb3SzesAAODqZTMMw/B2E1cLt9utkJAQFRUV1fj+qpjxb1ncFeq6nJmDvd2C8qZ19nYLqEVapu7ydgu65ZVbvN0CapGNozde1P7V/f1dq9/+AwAAqCsIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAULVBZo/f75at26tgIAAxcbGasuWLd5uCQAA1AKEqguwbNkypaSkaMqUKdq+fbuio6PldDp19OhRb7cGAAC8jFB1AWbNmqXhw4dr6NChioqK0sKFC9WgQQMtWrTI260BAAAvI1RVU2lpqXJychQXF2du8/HxUVxcnLKzs73YGQAAqA3qebuBuuKHH35QWVmZwsLCPLaHhYVp3759Ve5TUlKikpISc72oqEiS5Ha7a9xHWcnPNd4XV6aL+X6yyolTZd5uAbVIbfiePPPzGW+3gFrkYr8nK/Y3DOM36whVl9D06dP17LPPVtoeGRnphW5wpQp5ZYS3WwA8TQ/xdgeAh5CJ1nxPnjhxQiEh556LUFVNTZs2la+vr/Lz8z225+fnKzw8vMp9Jk2apJSUFHO9vLxcP/74o5o0aSKbzXZJ+73Sud1uRUZG6tChQ7Lb7d5uB+B7ErUO35PWMQxDJ06cUERExG/WEaqqyc/PTzExMcrMzFT//v0l/RKSMjMzNWrUqCr38ff3l7+/v8e2Ro0aXeJOry52u50fFqhV+J5EbcP3pDV+6wpVBULVBUhJSVFiYqK6deum7t27a86cOSouLtbQoUO93RoAAPAyQtUFGDBggAoKCpSamiqXy6WuXbsqPT290s3rAADg6kOoukCjRo0659t9uHz8/f01ZcqUSm+vAt7C9yRqG74nLz+bcb7PBwIAAOC8ePgnAACABQhVAAAAFiBUAQAAWIBQBa9Zv369bDabCgsLL8n8rVu31pw5cy7J3ICVhgwZYj7/DqgpfuZ5H6EKNVZQUKCRI0eqZcuW8vf3V3h4uJxOpzZu3Ojt1oALNmTIENlsNtlsNtWvX19t2rTRhAkTdOrUKW+3hqvInXfeqbFjx1banpaWxsOj6wAeqYAaS0hIUGlpqZYsWaJrr71W+fn5yszM1LFjx7zaV2lpqfz8/LzaA+qmvn37avHixTp9+rRycnKUmJgom82mF198sUbznT59WvXr17e4S8ATP/NqD65UoUYKCwv1z3/+Uy+++KJ69uypVq1aqXv37po0aZL++Mc/6rvvvpPNZlNubq7HPjabTevXr/eYa+PGjerSpYsCAgLUo0cP7d692xybOnWqunbt6lE/Z84ctW7d2lyveOvk+eefV0REhNq3b2+OnThxQg899JCCgoL0u9/9TvPnz/eYa9asWercubOCgoIUGRmpxx9/XCdPnjTHK/7vcM2aNerYsaOCg4PVt29fHTlypOYnD7VWxRXXyMhI9e/fX3FxccrIyJBU9VsrXbt21dSpU811m82mBQsW6I9//KOCgoL0/PPPq6ysTElJSWrTpo0CAwPVvn17zZ079zK+Klxp+JlXexGqUCPBwcEKDg7W+++/r5KSkouaa/z48XrppZe0detWhYaG6g9/+INOnz59QXNkZmZq//79ysjI0KpVq8ztM2fOVHR0tHbs2KGnnnpKY8aMMX9JSpKPj49efvll7dmzR0uWLNHatWs1YcIEj7l/+ukn/eUvf9Hf/vY3bdiwQXl5eXryyScv6jWj9tu9e7c2bdp0wVcApk6dqvvuu0+7du3SsGHDVF5erhYtWmjFihX66quvlJqaqqefflrLly+/RJ3jasDPvFrKAGro3XffNa655hojICDA+P3vf29MmjTJ+PLLLw3DMIyDBw8akowdO3aY9cePHzckGevWrTMMwzDWrVtnSDLeeecds+bYsWNGYGCgsWzZMsMwDGPKlClGdHS0x3Fnz55ttGrVylxPTEw0wsLCjJKSEo+6Vq1aGX379vXYNmDAAKNfv37nfE0rVqwwmjRpYq4vXrzYkGR8++235rb58+cbYWFh5z4xqJMSExMNX19fIygoyPD39zckGT4+Psa7775rGMYv30+zZ8/22Cc6OtqYMmWKuS7JGDt27HmPlZycbCQkJHgc+95777XiZaCOu+OOO4wxY8ZU2r548WIjJCTEMAx+5tVmXKlCjSUkJOjw4cP68MMP1bdvX61fv1433XST0tLSLmgeh8Nh/rtx48Zq37699u7de0FzdO7cucorCmfPXbF+9tyfffaZevfurd/97ndq2LChHnnkER07dkw//fSTWdOgQQO1bdvWXG/evLmOHj16Qf2hbujZs6dyc3O1efNmJSYmaujQoUpISLigObp161Zp2/z58xUTE6PQ0FAFBwfrtddeU15enlVt4yrEz7zaiVCFixIQEKC77rpLzzzzjDZt2qQhQ4ZoypQp8vH55VvLOOuvIF3oW3rSL5eqjV/9JaWq5gkKCrrgub/77jvdc8896tKli/7xj38oJyfHvP+gtLTUrPv1jcY2m61ST7gyBAUFqV27doqOjtaiRYu0efNmvfnmm5Jq/r34zjvv6Mknn1RSUpI+/fRT5ebmaujQoR7fY0AFu92uoqKiStsLCwsVEhJirvMzr3YiVMFSUVFRKi4uVmhoqCR53Nx49k3rZ/viiy/Mfx8/flxff/21OnbsKEkKDQ2Vy+Xy+A/6XPOcb+6K9Yq5c3JyVF5erpdeekk9evTQ9ddfr8OHD1d7blzZfHx89PTTT2vy5Mn6+eefFRoa6vH97Ha7dfDgwfPOs3HjRv3+97/X448/rhtvvFHt2rXTgQMHLmXrqMPat2+v7du3V9q+fft2XX/99efdn5953kWoQo0cO3ZMvXr10t///nft3LlTBw8e1IoVKzRjxgzde++9CgwMVI8ePfTCCy9o7969ysrK0uTJk6uca9q0acrMzNTu3bs1ZMgQNW3a1HwQ4p133qmCggLNmDFDBw4c0Pz58/XJJ59Uu8+NGzdqxowZ+vrrrzV//nytWLFCY8aMkSS1a9dOp0+f1iuvvKJ//etf+tvf/qaFCxde9LnBlePBBx+Ur6+v5s+fr169eulvf/ub/vnPf2rXrl1KTEyUr6/veee47rrrtG3bNq1Zs0Zff/21nnnmGW3duvUydI+6aOTIkfr666/13//939q5c6f279+vWbNm6X//93/1xBNPnHd/fuZ5F6EKNRIcHKzY2FjNnj1bt99+uzp16qRnnnlGw4cP17x58yRJixYt0pkzZxQTE6OxY8fqueeeq3KuF154QWPGjFFMTIxcLpc++ugj816Bjh076tVXX9X8+fMVHR2tLVu2XNCnUJ544glt27ZNN954o5577jnNmjVLTqdTkhQdHa1Zs2bpxRdfVKdOnfT2229r+vTpF3lmcCWpV6+eRo0apRkzZuipp57SHXfcoXvuuUfx8fHq37+/x30n5/J//+//1f33368BAwYoNjZWx44d0+OPP34ZukdddO2112rDhg3at2+f4uLiFBsbq+XLl2vFihXq27fveffnZ5532QzeKAUAALhoXKkCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgDgMrLZbHr//fe93QaAS4BQBeCKNWTIEPNPHl1uU6dOVdeuXSttP3LkiPr163f5GwJwydXzdgMAcDUJDw/3dgsALhGuVAG4KmVlZal79+7y9/dX8+bN9dRTT+nMmTPmeHl5uWbMmKF27drJ399fLVu21PPPP2+OT5w4Uddff70aNGiga6+9Vs8884xOnz4tSUpLS9Ozzz6rL7/8UjabTTabTWlpaZIqv/23a9cu9erVS4GBgWrSpIkee+wxnTx50hyvuNr2l7/8Rc2bN1eTJk2UnJxsHgtA7cGVKgBXnf/85z+6++67NWTIEL311lvat2+fhg8froCAAE2dOlWSNGnSJL3++uuaPXu2br31Vh05ckT79u0z52jYsKHS0tIUERGhXbt2afjw4WrYsKEmTJigAQMGaPfu3UpPT9dnn30mSQoJCanUR3FxsZxOpxwOh7Zu3aqjR4/q0Ucf1ahRo8wQJknr1q1T8+bNtW7dOn377bcaMGCAunbtquHDh1/S8wTgAhkAcIVKTEw07r333krbn376aaN9+/ZGeXm5uW3+/PlGcHCwUVZWZrjdbsPf3994/fXXq32smTNnGjExMeb6lClTjOjo6Ep1koyVK1cahmEYr732mnHNNdcYJ0+eNMdXr15t+Pj4GC6Xy3wNrVq1Ms6cOWPWPPjgg8aAAQOq3RuAy4MrVQCuOnv37pXD4ZDNZjO33XLLLTp58qS+//57uVwulZSUqHfv3uecY9myZXr55Zd14MABnTx5UmfOnJHdbr/gPqKjoxUUFOTRR3l5ufbv36+wsDBJ0g033CBfX1+zpnnz5tq1a9cFHQvApcc9VQDwK4GBgb85np2drUGDBunuu+/WqlWrtGPHDv3pT39SaWnpJemnfv36Hus2m03l5eWX5FgAao5QBeCq07FjR2VnZ8swDHPbxo0b1bBhQ7Vo0ULXXXedAgMDlZmZWeX+mzZtUqtWrfSnP/1J3bp103XXXad///vfHjV+fn4qKys7bx9ffvmliouLPfrw8fFR+/btL+IVAvAGQhWAK1pRUZFyc3M9lscee0yHDh3S6NGjtW/fPn3wwQeaMmWKUlJS5OPjo4CAAE2cOFETJkzQW2+9pQMHDuiLL77Qm2++KUm67rrrlJeXp3feeUcHDhzQyy+/rJUrV3oct3Xr1jp48KByc3P1ww8/qKSkpFJvgwYNUkBAgBITE7V7926tW7dOo0eP1iOPPGK+9Qeg7uCeKgBXtPXr1+vGG2/02JaUlKSPP/5Y48ePV3R0tBo3bqykpCRNnjzZrHnmmWdUr149paam6vDhw2revLlGjBghSfrjH/+ocePGadSoUSopKVF8fLyeeeYZ85ODkpSQkKD33ntPPXv2VGFhoRYvXqwhQ4Z49NGgQQOtWbNGY8aM0c0336wGDRooISFBs2bNumTnA8ClYzPOvv4NAACAGuHtPwAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAL/D2SZNEDjZee6AAAAAElFTkSuQmCC"},"metadata":{}}],"execution_count":233},{"cell_type":"code","source":"df['Policy Type'].value_counts().plot.pie(autopct='%1.1f%%')\nplt.title(\"Policy Type Distribution\")\nplt.ylabel(\"\")  # Optional: to hide ylabel\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:27.927065Z","iopub.execute_input":"2024-12-18T15:36:27.927362Z","iopub.status.idle":"2024-12-18T15:36:28.162069Z","shell.execute_reply.started":"2024-12-18T15:36:27.927333Z","shell.execute_reply":"2024-12-18T15:36:28.160638Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":234},{"cell_type":"code","source":"sns.histplot(df['Vehicle Age'], bins=10)\nplt.title(\"Vehicle Age Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:28.163607Z","iopub.execute_input":"2024-12-18T15:36:28.164128Z","iopub.status.idle":"2024-12-18T15:36:29.247426Z","shell.execute_reply.started":"2024-12-18T15:36:28.164063Z","shell.execute_reply":"2024-12-18T15:36:29.246386Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":235},{"cell_type":"code","source":"sns.countplot(x='Insurance Duration', data=df)\nplt.title(\"Insurance Duration Count\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:29.253407Z","iopub.execute_input":"2024-12-18T15:36:29.253734Z","iopub.status.idle":"2024-12-18T15:36:29.541461Z","shell.execute_reply.started":"2024-12-18T15:36:29.253689Z","shell.execute_reply":"2024-12-18T15:36:29.540419Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":236},{"cell_type":"code","source":"\n# Time Series Plot\n# df['Policy Start Date'].dt.year.value_counts().sort_index().plot(kind='line')\n# plt.title(\"Policy Start Dates Over Time\")\n# plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:29.542573Z","iopub.execute_input":"2024-12-18T15:36:29.542888Z","iopub.status.idle":"2024-12-18T15:36:29.547416Z","shell.execute_reply.started":"2024-12-18T15:36:29.542858Z","shell.execute_reply":"2024-12-18T15:36:29.546271Z"}},"outputs":[],"execution_count":237},{"cell_type":"code","source":"sns.countplot(x='Smoking Status', data=df)\nplt.title(\"Smoking Status Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:29.548659Z","iopub.execute_input":"2024-12-18T15:36:29.548992Z","iopub.status.idle":"2024-12-18T15:36:30.321653Z","shell.execute_reply.started":"2024-12-18T15:36:29.548962Z","shell.execute_reply":"2024-12-18T15:36:30.319959Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAlUAAAHHCAYAAACWQK1nAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABF9ElEQVR4nO3dfXzO9f////uxzU6YY3OyEzJGCnOaYS2FWI5q9X4rFHnXnIQ0ihVSGkkp3hVyVnpner8pVIRl8nZaLCeTQnhL86HYrNgOlI3t9fuj315fh43NvDic3K6Xy3G5OJ7Px+v5ehyHafder9fxOmyGYRgCAADAJfFwdwMAAADXA0IVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhWAInr27Cl/f/8S69q1a6d27dpd/oZwUcLDw9WzZ8/Lvp/9+/fLZrMpKSnJHCvtz45VbDabRo8efcX2B1wIoQq4Cmzfvl1dunRRrVq15Ovrq5tuukn33HOP3n33XXe3dtXKy8vTpEmTdNttt8lutyswMFANGzZUv379tHv3brNuw4YNGj16tLKzs8u8r2nTprkEhyupXbt2stlsstls8vDwkN1uV7169fT4449rxYoVlu3nyy+/vGrDydXcG3A2G9/9B7jXhg0bdPfdd6tmzZqKi4tTaGioDh48qG+//Vb79u3TTz/9dMV76tmzpz799FOdOHHignV5eXmSJG9v7yvRlosHH3xQy5YtU/fu3RUdHa3Tp09r9+7dWrp0qV599VXzSM0///lPDR06VOnp6QoPDy/Tvho1aqSqVatqzZo1lvVfWu3atdO+ffs0btw4SdLJkyf1008/6fPPP9fPP/+sRx55RP/5z39Urlw5c5vc3Fx5eHi4jJVk4MCBmjp1qi7mV4JhGMrNzVW5cuXk6ekpqfQ/OxfjQr2dOnVKXl5e8vLysmx/QFnxUwi42WuvvaaAgABt3rxZgYGBLnNHjhxxT1Ol5I4wJUmbN2/W0qVL9dprr+nFF190mZsyZcolHZW6GgUEBOgf//iHy9gbb7yhZ555RtOmTVN4eLjefPNNc87Hx+ey9nPmzBkVFBTI29tbvr6+l3VfJXH3/oGzcfoPcLN9+/apYcOGRQKVJAUHB7s8t9lsGjhwoBYsWKCIiAj5+fkpOjpa27dvlyS99957qlu3rnx9fdWuXTvt37+/yJoLFixQZGSk/Pz8VLVqVf3jH//Qr7/+WmKf27ZtU1BQkNq1a2cehTj3mqo1a9bIZrNp/vz5eu2111SjRg35+vqqQ4cOxR5xmzp1qurUqSM/Pz+1atVKX3/9damu09q3b58kqXXr1kXmPD09VaVKFUnS6NGjNXToUElS7dq1zdNohe/LrFmz1L59ewUHB8vHx0cRERGaPn26y3rh4eHauXOn1q5da25f2N/o0aNls9mK9JCUlOSyH0nasmWLHA6HqlatKj8/P9WuXVu9e/e+4Ou8EE9PT02ePFkRERGaMmWKcnJyXHo++5qq06dP65VXXtEtt9wiX19fValSRXfeead5+rBnz56aOnWqJJmvsfB1FV439c9//lMTJ07UzTffLB8fH/3444/FXlNV6Oeff5bD4VCFChVUvXp1jRkzxuVIU+HPyrlH/85d80K9FY6de2rwu+++03333Se73S5/f3916NBB3377rUtN4d/R+vXrlZCQoKCgIFWoUEEPPfSQsrKySv4LAIrBkSrAzWrVqqXU1FTt2LFDjRo1KrH+66+/1uLFixUfHy9JGjdunB544AENGzZM06ZN09NPP61jx45p/Pjx6t27t1atWmVum5SUpF69eqlly5YaN26cMjMzNWnSJK1fv17fffddscFO+uvIkMPhUIsWLfTFF1/Iz8/vgj2+8cYb8vDw0PPPP6+cnByNHz9ePXr00MaNG82a6dOna+DAgbrrrrs0ZMgQ7d+/X506dVKlSpVUo0aNEt8zSZozZ45at2593lM/Dz/8sP73v//p448/1jvvvKOqVatKkoKCgsweGjZsqL/97W/y8vLSkiVL9PTTT6ugoMB8fydOnKhBgwbJ399fL730kiQpJCTkgv2d68iRI+rYsaOCgoL0wgsvKDAwUPv379fnn39+Ueucy9PTU927d9fLL7+sb775RrGxscXWjR49WuPGjdOTTz6pVq1ayel0asuWLdq6davuuece9e/fX4cOHdKKFSv073//u9g1Zs2apVOnTqlfv37y8fFR5cqVVVBQUGxtfn6+7r33Xt1+++0aP368UlJSNGrUKJ05c0Zjxoy5qNdYmt7OtnPnTt11112y2+0aNmyYypUrp/fee0/t2rXT2rVrFRUV5VI/aNAgVapUSaNGjdL+/fs1ceJEDRw4UPPmzbuoPgFJkgHArb766ivD09PT8PT0NKKjo41hw4YZy5cvN/Ly8orUSjJ8fHyM9PR0c+y9994zJBmhoaGG0+k0x0eMGGFIMmvz8vKM4OBgo1GjRsaff/5p1i1dutSQZCQmJppjcXFxRoUKFQzDMIxvvvnGsNvtRmxsrHHq1CmXftq2bWu0bdvWfL569WpDktGgQQMjNzfXHJ80aZIhydi+fbthGIaRm5trVKlSxWjZsqVx+vRpsy4pKcmQ5LJmcQoKCoy2bdsakoyQkBCje/fuxtSpU43/+7//K1I7YcIEl/fhbH/88UeRMYfDYdSpU8dlrGHDhsX2NGrUKKO4/4zOmjXLZZ8LFy40JBmbN2++4OsqTtu2bY2GDRued75w7UmTJpljtWrVMuLi4sznTZs2NWJjYy+4n/j4+GJfS3p6uiHJsNvtxpEjR4qdmzVrljkWFxdnSDIGDRpkjhUUFBixsbGGt7e3kZWVZRjG//tZWb16dYlrnq83w/jr38SoUaPM5506dTK8vb2Nffv2mWOHDh0yKlasaLRp08YcK/w7iomJMQoKCszxIUOGGJ6enkZ2dnax+wMuhNN/gJvdc889Sk1N1d/+9jd9//33Gj9+vBwOh2666SYtXry4SH2HDh1cLrgu/D/vzp07q2LFikXGf/75Z0l/nX46cuSInn76aZfrUGJjY1W/fn0lJycX2dfq1avlcDjUoUMHff7556W+VqdXr14u11vdddddRXr5/fff1bdvX5ejTD169FClSpVKXN9ms2n58uUaO3asKlWqpI8//ljx8fGqVauWHn300VJfU3X2EbecnBz99ttvatu2rX7++WeX02mXqvAI4NKlS3X69GnL1pVk3r7g+PHjF9z/zp07tXfv3jLvp3PnzuYRvtIYOHCg+efC09Z5eXn673//W+YeSpKfn6+vvvpKnTp1Up06dczxatWq6bHHHtM333wjp9Ppsk2/fv1cTifeddddys/P1//93/9dtj5x/SJUAVeBli1b6vPPP9exY8e0adMmjRgxQsePH1eXLl30448/utTWrFnT5XlAQIAkKSwsrNjxY8eOSZL5S6JevXpF9l+/fv0iv0ROnTql2NhY3XbbbZo/f/5FXZR+bo+FQencXurWretS5+XlVepP6Pn4+Oill17Srl27dOjQIX388ce6/fbbNX/+fJdf6Beyfv16xcTEqEKFCgoMDFRQUJB54buVoapt27bq3LmzXnnlFVWtWlV///vfNWvWLOXm5l7y2oXXt50dqM81ZswYZWdn69Zbb1Xjxo01dOhQ/fDDDxe1n9q1a5e61sPDwyXUSNKtt94qScVe52eVrKws/fHHH8X+jDdo0EAFBQU6ePCgy3hJP6vAxSBUAVcRb29vtWzZUq+//rqmT5+u06dPa8GCBS41hR9dP9f5xo0y3jXFx8dHsbGx2rhxo1JSUi5qW6t7KUm1atXUrVs3rVu3Trfccovmz5+vM2fOXHCbffv2qUOHDvrtt9/09ttvKzk5WStWrNCQIUMk6bzXC52tuIvUpb+OmJxb9+mnnyo1NVUDBw7Ur7/+qt69eysyMvKSbz2wY8cOSUUD6tnatGmjffv26cMPP1SjRo30wQcfqHnz5vrggw9KvZ+SrqO7WKV97y63K/2ziusboQq4SrVo0UKSdPjwYUvWK7y4e8+ePUXm9uzZY84XstlsmjNnjjp06KCuXbtaeo+mwn2d+4nAM2fOXNKRjHLlyqlJkyY6ffq0fvvtN0nn/+W9ZMkS5ebmavHixerfv7/uv/9+xcTEFBsezrdG4VGNc083nu/U0e23367XXntNW7Zs0Zw5c7Rz50598sknpX15ReTn52vu3LkqX7687rzzzgvWVq5cWb169dLHH3+sgwcPqkmTJi6fmjvfayyLgoIC81Rvof/973+SZB6JvJj3rrS9BQUFqXz58sX+jO/evVseHh5FjugCViJUAW62evXqYv+v+Msvv5RU/Om6smjRooWCg4M1Y8YMl9NOy5Yt065du4r95Ji3t7c+//xztWzZUg8++KA2bdpkWS9VqlTRzJkzXY4ozZkzp1SnXfbu3asDBw4UGc/OzlZqaqoqVapkXv9ToUIFc+5shUcozn7vc3JyNGvWrCLrVqhQodjrtG6++WZJ0rp168yxkydPavbs2S51x44dK/J33KxZM0kq8ynA/Px8PfPMM9q1a5eeeeYZ2e3289b+/vvvLs/9/f1Vt25dl32f730qqylTpph/NgxDU6ZMUbly5dShQwdJfwVrT09Pl/dO+uvu9ecqbW+enp7q2LGjvvjiC5dwnpmZqblz5+rOO++84PsEXCpuqQC42aBBg/THH3/ooYceUv369ZWXl6cNGzZo3rx5Cg8PV69evSzZT7ly5fTmm2+qV69eatu2rbp3727eUiE8PNw87XUuPz8/LV26VO3bt9d9992ntWvXlurWDxfi7e2t0aNHa9CgQWrfvr0eeeQR7d+/X0lJSbr55ptLPDLx/fff67HHHtN9992nu+66S5UrV9avv/6q2bNn69ChQ5o4caIZmiIjIyVJL730krp166Zy5crpwQcfVMeOHeXt7a0HH3xQ/fv314kTJzRz5kwFBwcXOToYGRmp6dOna+zYsapbt66Cg4PVvn17dezYUTVr1lSfPn00dOhQeXp66sMPP1RQUJBL6Js9e7amTZumhx56SDfffLOOHz+umTNnym636/777y/x/crJydF//vMfSdIff/xh3lF937596tatm1599dULbh8REaF27dopMjJSlStX1pYtW/Tpp5+6XHtW+D4988wzcjgc8vT0VLdu3UrsrTi+vr5KSUlRXFycoqKitGzZMiUnJ+vFF180w25AQIC6du2qd999VzabTTfffLOWLl1a7A1vL6a3sWPHasWKFbrzzjv19NNPy8vLS++9955yc3M1fvz4Mr0eoNTc+MlDAIZhLFu2zOjdu7dRv359w9/f3/D29jbq1q1rDBo0yMjMzHSplWTEx8e7jBV+BH3ChAku44UfWV+wYIHL+Lx584zbbrvN8PHxMSpXrmz06NHD+OWXX1xqzr6lQqHffvvNiIiIMEJDQ429e/cahnH+Wyqcu8/iPiZvGIYxefJko1atWoaPj4/RqlUrY/369UZkZKRx7733XvA9y8zMNN544w2jbdu2RrVq1QwvLy+jUqVKRvv27Y1PP/20SP2rr75q3HTTTYaHh4fLrQ4WL15sNGnSxPD19TXCw8ONN9980/jwww+L3IIhIyPDiI2NNSpWrFjklg9paWlGVFSU4e3tbdSsWdN4++23i9xSYevWrUb37t2NmjVrGj4+PkZwcLDxwAMPGFu2bLng6yx8jyWZD39/f+OWW24x/vGPfxhfffVVsduce0uFsWPHGq1atTICAwMNPz8/o379+sZrr73mctuOM2fOGIMGDTKCgoIMm81m3sLgfD9fZ8+de0uFChUqGPv27TM6duxolC9f3ggJCTFGjRpl5Ofnu2yflZVldO7c2ShfvrxRqVIlo3///saOHTuKrHm+3gyj6C0VDOOv99vhcBj+/v5G+fLljbvvvtvYsGGDS03h39G5t7k4360egNLgu/8AXDUKCgoUFBSkhx9+WDNnznR3OwBwUbimCoBbnDp1qsh1Rh999JGOHj1a4tfUAMDViCNVANxizZo1GjJkiLp27aoqVapo69at+te//qUGDRooLS3NbV/WDABlxYXqANwiPDxcYWFhmjx5so4eParKlSvriSee0BtvvEGgAnBN4kgVAACABbimCgAAwAKEKgAAAAtwTdUVVFBQoEOHDqlixYqWfiUEAAC4fAzD0PHjx1W9enV5eJz/eBSh6go6dOgQ3zsFAMA16uDBg6pRo8Z55wlVV1DFihUl/fWXwvdPAQBwbXA6nQoLCzN/j58PoeoKKjzlZ7fbCVUAAFxjSrp0hwvVAQAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAAC7g9VP3666/6xz/+oSpVqsjPz0+NGzfWli1bzHnDMJSYmKhq1arJz89PMTEx2rt3r8saR48eVY8ePWS32xUYGKg+ffroxIkTLjU//PCD7rrrLvn6+iosLEzjx48v0suCBQtUv359+fr6qnHjxvryyy9d5kvTCwAAuDG5NVQdO3ZMrVu3Vrly5bRs2TL9+OOPeuutt1SpUiWzZvz48Zo8ebJmzJihjRs3qkKFCnI4HDp16pRZ06NHD+3cuVMrVqzQ0qVLtW7dOvXr18+cdzqd6tixo2rVqqW0tDRNmDBBo0eP1vvvv2/WbNiwQd27d1efPn303XffqVOnTurUqZN27NhxUb0AAIAblOFGw4cPN+68887zzhcUFBihoaHGhAkTzLHs7GzDx8fH+Pjjjw3DMIwff/zRkGRs3rzZrFm2bJlhs9mMX3/91TAMw5g2bZpRqVIlIzc312Xf9erVM58/8sgjRmxsrMv+o6KijP79+5e6l5Lk5OQYkoycnJxS1QMAAPcr7e9vL3cGusWLF8vhcKhr165au3atbrrpJj399NPq27evJCk9PV0ZGRmKiYkxtwkICFBUVJRSU1PVrVs3paamKjAwUC1atDBrYmJi5OHhoY0bN+qhhx5Samqq2rRpI29vb7PG4XDozTff1LFjx1SpUiWlpqYqISHBpT+Hw6FFixaVupdz5ebmKjc313zudDov7Q0DcMM7MKaxu1sArjo1E7e7uwVJbj799/PPP2v69Om65ZZbtHz5cg0YMEDPPPOMZs+eLUnKyMiQJIWEhLhsFxISYs5lZGQoODjYZd7Ly0uVK1d2qSlujbP3cb6as+dL6uVc48aNU0BAgPkICwsr6S0BAADXKLceqSooKFCLFi30+uuvS5Juu+027dixQzNmzFBcXJw7W7PEiBEjXI5+OZ3OKxKsIod+dNn3AVxr0iY84e4WAFzn3Hqkqlq1aoqIiHAZa9CggQ4cOCBJCg0NlSRlZma61GRmZppzoaGhOnLkiMv8mTNndPToUZea4tY4ex/nqzl7vqRezuXj4yO73e7yAAAA1ye3hqrWrVtrz549LmP/+9//VKtWLUlS7dq1FRoaqpUrV5rzTqdTGzduVHR0tCQpOjpa2dnZSktLM2tWrVqlgoICRUVFmTXr1q3T6dOnzZoVK1aoXr165icNo6OjXfZTWFO4n9L0AgAAblxuDVVDhgzRt99+q9dff10//fST5s6dq/fff1/x8fGSJJvNpsGDB2vs2LFavHixtm/frieeeELVq1dXp06dJP11ZOvee+9V3759tWnTJq1fv14DBw5Ut27dVL16dUnSY489Jm9vb/Xp00c7d+7UvHnzNGnSJJdTc88++6xSUlL01ltvaffu3Ro9erS2bNmigQMHlroXAABw43LrNVUtW7bUwoULNWLECI0ZM0a1a9fWxIkT1aNHD7Nm2LBhOnnypPr166fs7GzdeeedSklJka+vr1kzZ84cDRw4UB06dJCHh4c6d+6syZMnm/MBAQH66quvFB8fr8jISFWtWlWJiYku97K64447NHfuXI0cOVIvvviibrnlFi1atEiNGjW6qF4AAMCNyWYYhuHuJm4UTqdTAQEBysnJuazXV3GhOlDU9XKhOrdUAIq63LdUKO3vb7d/TQ0AAMD1gFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAF3BqqRo8eLZvN5vKoX7++OX/q1CnFx8erSpUq8vf3V+fOnZWZmemyxoEDBxQbG6vy5csrODhYQ4cO1ZkzZ1xq1qxZo+bNm8vHx0d169ZVUlJSkV6mTp2q8PBw+fr6KioqSps2bXKZL00vAADgxuX2I1UNGzbU4cOHzcc333xjzg0ZMkRLlizRggULtHbtWh06dEgPP/ywOZ+fn6/Y2Fjl5eVpw4YNmj17tpKSkpSYmGjWpKenKzY2Vnfffbe2bdumwYMH68knn9Ty5cvNmnnz5ikhIUGjRo3S1q1b1bRpUzkcDh05cqTUvQAAgBubzTAMw107Hz16tBYtWqRt27YVmcvJyVFQUJDmzp2rLl26SJJ2796tBg0aKDU1VbfffruWLVumBx54QIcOHVJISIgkacaMGRo+fLiysrLk7e2t4cOHKzk5WTt27DDX7tatm7Kzs5WSkiJJioqKUsuWLTVlyhRJUkFBgcLCwjRo0CC98MILpeqlNJxOpwICApSTkyO73V7m960kkUM/umxrA9eqtAlPuLsFSxwY09jdLQBXnZqJ2y/r+qX9/e32I1V79+5V9erVVadOHfXo0UMHDhyQJKWlpen06dOKiYkxa+vXr6+aNWsqNTVVkpSamqrGjRubgUqSHA6HnE6ndu7cadacvUZhTeEaeXl5SktLc6nx8PBQTEyMWVOaXoqTm5srp9Pp8gAAANcnt4aqqKgoJSUlKSUlRdOnT1d6erruuusuHT9+XBkZGfL29lZgYKDLNiEhIcrIyJAkZWRkuASqwvnCuQvVOJ1O/fnnn/rtt9+Un59fbM3Za5TUS3HGjRungIAA8xEWFla6NwYAAFxzvNy58/vuu8/8c5MmTRQVFaVatWpp/vz58vPzc2Nn1hgxYoQSEhLM506nk2AFAMB1yu2n/84WGBioW2+9VT/99JNCQ0OVl5en7Oxsl5rMzEyFhoZKkkJDQ4t8Aq/weUk1drtdfn5+qlq1qjw9PYutOXuNknopjo+Pj+x2u8sDAABcn66qUHXixAnt27dP1apVU2RkpMqVK6eVK1ea83v27NGBAwcUHR0tSYqOjtb27dtdPqW3YsUK2e12RUREmDVnr1FYU7iGt7e3IiMjXWoKCgq0cuVKs6Y0vQAAgBubW0//Pf/883rwwQdVq1YtHTp0SKNGjZKnp6e6d++ugIAA9enTRwkJCapcubLsdrsGDRqk6Oho89N2HTt2VEREhB5//HGNHz9eGRkZGjlypOLj4+Xj4yNJeuqppzRlyhQNGzZMvXv31qpVqzR//nwlJyebfSQkJCguLk4tWrRQq1atNHHiRJ08eVK9evWSpFL1AgAAbmxuDVW//PKLunfvrt9//11BQUG688479e233yooKEiS9M4778jDw0OdO3dWbm6uHA6Hpk2bZm7v6emppUuXasCAAYqOjlaFChUUFxenMWPGmDW1a9dWcnKyhgwZokmTJqlGjRr64IMP5HA4zJpHH31UWVlZSkxMVEZGhpo1a6aUlBSXi9dL6gUAANzY3HqfqhsN96kC3If7VAHXL+5TBQAAcB0hVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYIGrJlS98cYbstlsGjx4sDl26tQpxcfHq0qVKvL391fnzp2VmZnpst2BAwcUGxur8uXLKzg4WEOHDtWZM2dcatasWaPmzZvLx8dHdevWVVJSUpH9T506VeHh4fL19VVUVJQ2bdrkMl+aXgAAwI3rqghVmzdv1nvvvacmTZq4jA8ZMkRLlizRggULtHbtWh06dEgPP/ywOZ+fn6/Y2Fjl5eVpw4YNmj17tpKSkpSYmGjWpKenKzY2Vnfffbe2bdumwYMH68knn9Ty5cvNmnnz5ikhIUGjRo3S1q1b1bRpUzkcDh05cqTUvQAAgBubzTAMw50NnDhxQs2bN9e0adM0duxYNWvWTBMnTlROTo6CgoI0d+5cdenSRZK0e/duNWjQQKmpqbr99tu1bNkyPfDAAzp06JBCQkIkSTNmzNDw4cOVlZUlb29vDR8+XMnJydqxY4e5z27duik7O1spKSmSpKioKLVs2VJTpkyRJBUUFCgsLEyDBg3SCy+8UKpeSsPpdCogIEA5OTmy2+2WvYfnihz60WVbG7hWpU14wt0tWOLAmMbubgG46tRM3H5Z1y/t72+3H6mKj49XbGysYmJiXMbT0tJ0+vRpl/H69eurZs2aSk1NlSSlpqaqcePGZqCSJIfDIafTqZ07d5o1567tcDjMNfLy8pSWluZS4+HhoZiYGLOmNL0AAIAbm5c7d/7JJ59o69at2rx5c5G5jIwMeXt7KzAw0GU8JCREGRkZZs3ZgapwvnDuQjVOp1N//vmnjh07pvz8/GJrdu/eXepeipObm6vc3FzzudPpPG8tAAC4trntSNXBgwf17LPPas6cOfL19XVXG5fVuHHjFBAQYD7CwsLc3RIAALhM3Baq0tLSdOTIETVv3lxeXl7y8vLS2rVrNXnyZHl5eSkkJER5eXnKzs522S4zM1OhoaGSpNDQ0CKfwCt8XlKN3W6Xn5+fqlatKk9Pz2Jrzl6jpF6KM2LECOXk5JiPgwcPlu7NAQAA1xy3haoOHTpo+/bt2rZtm/lo0aKFevToYf65XLlyWrlypbnNnj17dODAAUVHR0uSoqOjtX37dpdP6a1YsUJ2u10RERFmzdlrFNYUruHt7a3IyEiXmoKCAq1cudKsiYyMLLGX4vj4+Mhut7s8AADA9clt11RVrFhRjRo1chmrUKGCqlSpYo736dNHCQkJqly5sux2uwYNGqTo6Gjz03YdO3ZURESEHn/8cY0fP14ZGRkaOXKk4uPj5ePjI0l66qmnNGXKFA0bNky9e/fWqlWrNH/+fCUnJ5v7TUhIUFxcnFq0aKFWrVpp4sSJOnnypHr16iVJCggIKLEXAABwY3Prheoleeedd+Th4aHOnTsrNzdXDodD06ZNM+c9PT21dOlSDRgwQNHR0apQoYLi4uI0ZswYs6Z27dpKTk7WkCFDNGnSJNWoUUMffPCBHA6HWfPoo48qKytLiYmJysjIULNmzZSSkuJy8XpJvQAAgBub2+9TdSPhPlWA+3CfKuD6xX2qAAAAriOEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAuUKVS1b99e2dnZRcadTqfat29/qT0BAABcc8oUqtasWaO8vLwi46dOndLXX399yU0BAABca7wupviHH34w//zjjz8qIyPDfJ6fn6+UlBTddNNN1nUHAABwjbioUNWsWTPZbDbZbLZiT/P5+fnp3Xfftaw5AACAa8VFhar09HQZhqE6depo06ZNCgoKMue8vb0VHBwsT09Py5sEAAC42l1UqKpVq5YkqaCg4LI0AwAAcK26qFB1tr1792r16tU6cuRIkZCVmJh4yY0BAABcS8oUqmbOnKkBAwaoatWqCg0Nlc1mM+dsNhuhCgAA3HDKFKrGjh2r1157TcOHD7e6HwAAgGtSme5TdezYMXXt2tXqXgAAAK5ZZQpVXbt21VdffWV1LwAAANesMp3+q1u3rl5++WV9++23aty4scqVK+cy/8wzz1jSHAAAwLWiTKHq/fffl7+/v9auXau1a9e6zNlsNkIVAAC44ZQpVKWnp1vdBwAAwDWtTNdUAQAAwFWZjlT17t37gvMffvhhmZoBAAC4VpUpVB07dszl+enTp7Vjxw5lZ2cX+0XLAAAA17syhaqFCxcWGSsoKNCAAQN08803X3JTAAAA1xrLrqny8PBQQkKC3nnnHauWBAAAuGZYeqH6vn37dObMGSuXBAAAuCaU6fRfQkKCy3PDMHT48GElJycrLi7OksYAAACuJWUKVd99953Lcw8PDwUFBemtt94q8ZOBAAAA16Mynf5bvXq1y2PlypX65JNP1K9fP3l5lT6nTZ8+XU2aNJHdbpfdbld0dLSWLVtmzp86dUrx8fGqUqWK/P391blzZ2VmZrqsceDAAcXGxqp8+fIKDg7W0KFDi5yCXLNmjZo3by4fHx/VrVtXSUlJRXqZOnWqwsPD5evrq6ioKG3atMllvjS9AACAG9clXVOVlZWlb775Rt98842ysrIuevsaNWrojTfeUFpamrZs2aL27dvr73//u3bu3ClJGjJkiJYsWaIFCxZo7dq1OnTokB5++GFz+/z8fMXGxiovL08bNmzQ7NmzlZSUpMTERLMmPT1dsbGxuvvuu7Vt2zYNHjxYTz75pJYvX27WzJs3TwkJCRo1apS2bt2qpk2byuFw6MiRI2ZNSb0AAIAbm80wDONiNzp58qQGDRqkjz76SAUFBZIkT09PPfHEE3r33XdVvnz5MjdUuXJlTZgwQV26dFFQUJDmzp2rLl26SJJ2796tBg0aKDU1VbfffruWLVumBx54QIcOHVJISIgkacaMGRo+fLiysrLk7e2t4cOHKzk5WTt27DD30a1bN2VnZyslJUWSFBUVpZYtW2rKlCmS/ro9RFhYmAYNGqQXXnhBOTk5JfZSGk6nUwEBAcrJyZHdbi/ze1SSyKEfXba1gWtV2oQn3N2CJQ6MaezuFoCrTs3E7Zd1/dL+/i7TkaqEhAStXbtWS5YsUXZ2trKzs/XFF19o7dq1eu6558rUcH5+vj755BOdPHlS0dHRSktL0+nTpxUTE2PW1K9fXzVr1lRqaqokKTU1VY0bNzYDlSQ5HA45nU7zaFdqaqrLGoU1hWvk5eUpLS3NpcbDw0MxMTFmTWl6KU5ubq6cTqfLAwAAXJ/KFKo+++wz/etf/9J9991nXg91//33a+bMmfr0008vaq3t27fL399fPj4+euqpp7Rw4UJFREQoIyND3t7eCgwMdKkPCQlRRkaGJCkjI8MlUBXOF85dqMbpdOrPP//Ub7/9pvz8/GJrzl6jpF6KM27cOAUEBJiPsLCw0r0pAADgmlOmUPXHH38UCSGSFBwcrD/++OOi1qpXr562bdumjRs3asCAAYqLi9OPP/5YlrauOiNGjFBOTo75OHjwoLtbAgAAl0mZQlV0dLRGjRqlU6dOmWN//vmnXnnlFUVHR1/UWt7e3qpbt64iIyM1btw4NW3aVJMmTVJoaKjy8vKUnZ3tUp+ZmanQ0FBJUmhoaJFP4BU+L6nGbrfLz89PVatWlaenZ7E1Z69RUi/F8fHxMY/kFT4AAMD1qUyhauLEiVq/fr1q1KihDh06qEOHDgoLC9P69es1adKkS2qooKBAubm5ioyMVLly5bRy5Upzbs+ePTpw4IAZ3KKjo7V9+3aXT+mtWLFCdrtdERERZs3ZaxTWFK7h7e2tyMhIl5qCggKtXLnSrClNLwAA4MZWppt/Nm7cWHv37tWcOXO0e/duSVL37t3Vo0cP+fn5lXqdESNG6L777lPNmjV1/PhxzZ07V2vWrNHy5csVEBCgPn36KCEhQZUrV5bdbtegQYMUHR1tftquY8eOioiI0OOPP67x48crIyNDI0eOVHx8vHx8fCRJTz31lKZMmaJhw4apd+/eWrVqlebPn6/k5GSzj4SEBMXFxalFixZq1aqVJk6cqJMnT6pXr16SVKpeAADAja1MoWrcuHEKCQlR3759XcY//PBDZWVlafjw4aVa58iRI3riiSd0+PBhBQQEqEmTJlq+fLnuueceSdI777wjDw8Pde7cWbm5uXI4HJo2bZq5vaenp5YuXaoBAwYoOjpaFSpUUFxcnMaMGWPW1K5dW8nJyRoyZIgmTZqkGjVq6IMPPpDD4TBrHn30UWVlZSkxMVEZGRlq1qyZUlJSXK4bK6kXAABwYyvTfarCw8M1d+5c3XHHHS7jGzduVLdu3ZSenm5Zg9cT7lMFuA/3qQKuX9f0faoyMjJUrVq1IuNBQUE6fPhwWZYEAAC4ppUpVBVelH6u9evXq3r16pfcFAAAwLWmTNdU9e3bV4MHD9bp06fVvn17SdLKlSs1bNiwMt9RHQAA4FpWplA1dOhQ/f7773r66aeVl5cnSfL19dXw4cM1YsQISxsEAAC4FpQpVNlsNr355pt6+eWXtWvXLvn5+emWW24xb2MAAABwoylTqCrk7++vli1bWtULAADANatMF6oDAADAFaEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsIBbQ9W4cePUsmVLVaxYUcHBwerUqZP27NnjUnPq1CnFx8erSpUq8vf3V+fOnZWZmelSc+DAAcXGxqp8+fIKDg7W0KFDdebMGZeaNWvWqHnz5vLx8VHdunWVlJRUpJ+pU6cqPDxcvr6+ioqK0qZNmy66FwAAcGNya6hau3at4uPj9e2332rFihU6ffq0OnbsqJMnT5o1Q4YM0ZIlS7RgwQKtXbtWhw4d0sMPP2zO5+fnKzY2Vnl5edqwYYNmz56tpKQkJSYmmjXp6emKjY3V3XffrW3btmnw4MF68skntXz5crNm3rx5SkhI0KhRo7R161Y1bdpUDodDR44cKXUvAADgxmUzDMNwdxOFsrKyFBwcrLVr16pNmzbKyclRUFCQ5s6dqy5dukiSdu/erQYNGig1NVW33367li1bpgceeECHDh1SSEiIJGnGjBkaPny4srKy5O3treHDhys5OVk7duww99WtWzdlZ2crJSVFkhQVFaWWLVtqypQpkqSCggKFhYVp0KBBeuGFF0rVS0mcTqcCAgKUk5Mju91u6Xt3tsihH122tYFrVdqEJ9zdgiUOjGns7haAq07NxO2Xdf3S/v6+qq6pysnJkSRVrlxZkpSWlqbTp08rJibGrKlfv75q1qyp1NRUSVJqaqoaN25sBipJcjgccjqd2rlzp1lz9hqFNYVr5OXlKS0tzaXGw8NDMTExZk1pegEAADcuL3c3UKigoECDBw9W69at1ahRI0lSRkaGvL29FRgY6FIbEhKijIwMs+bsQFU4Xzh3oRqn06k///xTx44dU35+frE1u3fvLnUv58rNzVVubq753Ol0lvQ2AACAa9RVc6QqPj5eO3bs0CeffOLuViwzbtw4BQQEmI+wsDB3twQAAC6TqyJUDRw4UEuXLtXq1atVo0YNczw0NFR5eXnKzs52qc/MzFRoaKhZc+4n8Aqfl1Rjt9vl5+enqlWrytPTs9ias9coqZdzjRgxQjk5Oebj4MGDpXg3AADAtcitocowDA0cOFALFy7UqlWrVLt2bZf5yMhIlStXTitXrjTH9uzZowMHDig6OlqSFB0dre3bt7t8Sm/FihWy2+2KiIgwa85eo7CmcA1vb29FRka61BQUFGjlypVmTWl6OZePj4/sdrvLAwAAXJ/cek1VfHy85s6dqy+++EIVK1Y0r00KCAiQn5+fAgIC1KdPHyUkJKhy5cqy2+0aNGiQoqOjzU/bdezYUREREXr88cc1fvx4ZWRkaOTIkYqPj5ePj48k6amnntKUKVM0bNgw9e7dW6tWrdL8+fOVnJxs9pKQkKC4uDi1aNFCrVq10sSJE3Xy5En16tXL7KmkXgAAwI3LraFq+vTpkqR27dq5jM+aNUs9e/aUJL3zzjvy8PBQ586dlZubK4fDoWnTppm1np6eWrp0qQYMGKDo6GhVqFBBcXFxGjNmjFlTu3ZtJScna8iQIZo0aZJq1KihDz74QA6Hw6x59NFHlZWVpcTERGVkZKhZs2ZKSUlxuXi9pF4AAMCN66q6T9X1jvtUAe7DfaqA6xf3qQIAALiOEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAALEKoAAAAsQKgCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMACbg1V69at04MPPqjq1avLZrNp0aJFLvOGYSgxMVHVqlWTn5+fYmJitHfvXpeao0ePqkePHrLb7QoMDFSfPn104sQJl5offvhBd911l3x9fRUWFqbx48cX6WXBggWqX7++fH191bhxY3355ZcX3QsAALhxuTVUnTx5Uk2bNtXUqVOLnR8/frwmT56sGTNmaOPGjapQoYIcDodOnTpl1vTo0UM7d+7UihUrtHTpUq1bt079+vUz551Opzp27KhatWopLS1NEyZM0OjRo/X++++bNRs2bFD37t3Vp08ffffdd+rUqZM6deqkHTt2XFQvAADgxmUzDMNwdxOSZLPZtHDhQnXq1EnSX0eGqlevrueee07PP/+8JCknJ0chISFKSkpSt27dtGvXLkVERGjz5s1q0aKFJCklJUX333+/fvnlF1WvXl3Tp0/XSy+9pIyMDHl7e0uSXnjhBS1atEi7d++WJD366KM6efKkli5davZz++23q1mzZpoxY0apeikNp9OpgIAA5eTkyG63W/K+FSdy6EeXbW3gWpU24Ql3t2CJA2Mau7sF4KpTM3H7ZV2/tL+/r9prqtLT05WRkaGYmBhzLCAgQFFRUUpNTZUkpaamKjAw0AxUkhQTEyMPDw9t3LjRrGnTpo0ZqCTJ4XBoz549OnbsmFlz9n4Kawr3U5peipObmyun0+nyAAAA16erNlRlZGRIkkJCQlzGQ0JCzLmMjAwFBwe7zHt5ealy5couNcWtcfY+zldz9nxJvRRn3LhxCggIMB9hYWElvGoAAHCtumpD1fVgxIgRysnJMR8HDx50d0sAAOAyuWpDVWhoqCQpMzPTZTwzM9OcCw0N1ZEjR1zmz5w5o6NHj7rUFLfG2fs4X83Z8yX1UhwfHx/Z7XaXBwAAuD5dtaGqdu3aCg0N1cqVK80xp9OpjRs3Kjo6WpIUHR2t7OxspaWlmTWrVq1SQUGBoqKizJp169bp9OnTZs2KFStUr149VapUyaw5ez+FNYX7KU0vAADgxubWUHXixAlt27ZN27Ztk/TXBeHbtm3TgQMHZLPZNHjwYI0dO1aLFy/W9u3b9cQTT6h69ermJwQbNGige++9V3379tWmTZu0fv16DRw4UN26dVP16tUlSY899pi8vb3Vp08f7dy5U/PmzdOkSZOUkJBg9vHss88qJSVFb731lnbv3q3Ro0dry5YtGjhwoCSVqhcAAHBj83Lnzrds2aK7777bfF4YdOLi4pSUlKRhw4bp5MmT6tevn7Kzs3XnnXcqJSVFvr6+5jZz5szRwIED1aFDB3l4eKhz586aPHmyOR8QEKCvvvpK8fHxioyMVNWqVZWYmOhyL6s77rhDc+fO1ciRI/Xiiy/qlltu0aJFi9SoUSOzpjS9AACAG9dVc5+qGwH3qQLch/tUAdcv7lMFAABwHSFUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAUIVAACABQhVAAAAFiBUAQAAWIBQBQAAYAFCFQAAgAUIVQAAABYgVAEAAFiAUAUAAGABQhUAAIAFCFUAAAAWIFQBAABYgFAFAABgAULVRZo6darCw8Pl6+urqKgobdq0yd0tAQCAqwCh6iLMmzdPCQkJGjVqlLZu3aqmTZvK4XDoyJEj7m4NAAC4GaHqIrz99tvq27evevXqpYiICM2YMUPly5fXhx9+6O7WAACAmxGqSikvL09paWmKiYkxxzw8PBQTE6PU1FQ3dgYAAK4GXu5u4Frx22+/KT8/XyEhIS7jISEh2r17d7Hb5ObmKjc313yek5MjSXI6nZevUUn5uX9e1vWBa9Hl/nd3pRw/le/uFoCrzuX+9124vmEYF6wjVF1G48aN0yuvvFJkPCwszA3dADe2gHefcncLAC6XcQFXZDfHjx9XQMD590WoKqWqVavK09NTmZmZLuOZmZkKDQ0tdpsRI0YoISHBfF5QUKCjR4+qSpUqstlsl7VfuJ/T6VRYWJgOHjwou93u7nYAWIh/3zcWwzB0/PhxVa9e/YJ1hKpS8vb2VmRkpFauXKlOnTpJ+iskrVy5UgMHDix2Gx8fH/n4+LiMBQYGXuZOcbWx2+38Rxe4TvHv+8ZxoSNUhQhVFyEhIUFxcXFq0aKFWrVqpYkTJ+rkyZPq1auXu1sDAABuRqi6CI8++qiysrKUmJiojIwMNWvWTCkpKUUuXgcAADceQtVFGjhw4HlP9wFn8/Hx0ahRo4qcAgZw7ePfN4pjM0r6fCAAAABKxM0/AQAALECoAgAAsAChCgAAwAKEKgAAAAsQqoBL0LNnT9lsNr3xxhsu44sWLeKu+cA1yDAMxcTEyOFwFJmbNm2aAgMD9csvv7ihM1wLCFXAJfL19dWbb76pY8eOubsVAJfIZrNp1qxZ2rhxo9577z1zPD09XcOGDdO7776rGjVquLFDXM0IVcAliomJUWhoqMaNG3fems8++0wNGzaUj4+PwsPD9dZbb13BDgFcjLCwME2aNEnPP/+80tPTZRiG+vTpo44dO+q2227TfffdJ39/f4WEhOjxxx/Xb7/9Zm776aefqnHjxvLz81OVKlUUExOjkydPuvHV4EoiVAGXyNPTU6+//rrefffdYk8LpKWl6ZFHHlG3bt20fft2jR49Wi+//LKSkpKufLMASiUuLk4dOnRQ7969NWXKFO3YsUPvvfee2rdvr9tuu01btmxRSkqKMjMz9cgjj0iSDh8+rO7du6t3797atWuX1qxZo4cffljcDvLGwc0/gUvQs2dPZWdna9GiRYqOjlZERIT+9a9/adGiRXrooYdkGIZ69OihrKwsffXVV+Z2w4YNU3Jysnbu3OnG7gFcyJEjR9SwYUMdPXpUn332mXbs2KGvv/5ay5cvN2t++eUXhYWFac+ePTpx4oQiIyO1f/9+1apVy42dw104UgVY5M0339Ts2bO1a9cul/Fdu3apdevWLmOtW7fW3r17lZ+ffyVbBHARgoOD1b9/fzVo0ECdOnXS999/r9WrV8vf39981K9fX5K0b98+NW3aVB06dFDjxo3VtWtXzZw5k2stbzCEKsAibdq0kcPh0IgRI9zdCgCLeHl5ycvrr6/JPXHihB588EFt27bN5bF37161adNGnp6eWrFihZYtW6aIiAi9++67qlevntLT0938KnCl8IXKgIXeeOMNNWvWTPXq1TPHGjRooPXr17vUrV+/Xrfeeqs8PT2vdIsAyqh58+b67LPPFB4ebgatc9lsNrVu3VqtW7dWYmKiatWqpYULFyohIeEKdwt34EgVYKHGjRurR48emjx5sjn23HPPaeXKlXr11Vf1v//9T7Nnz9aUKVP0/PPPu7FTABcrPj5eR48eVffu3bV582bt27dPy5cvV69evZSfn6+NGzfq9ddf15YtW3TgwAF9/vnnysrKUoMGDdzdOq4QQhVgsTFjxqigoMB83rx5c82fP1+ffPKJGjVqpMTERI0ZM0Y9e/Z0X5MALlr16tW1fv165efnq2PHjmrcuLEGDx6swMBAeXh4yG63a926dbr//vt16623auTIkXrrrbd03333ubt1XCF8+g8AAMACHKkCAACwAKEKAADAAoQqAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgDcUGw2mxYtWnTe+fDwcE2cOPGK9QPg+kGoAnDFZWVlacCAAapZs6Z8fHwUGhoqh8NR5DsS3WHz5s3q16/fZd/P999/r7/97W8KDg6Wr6+vwsPD9eijj+rIkSOSpDVr1shmsyk7O/ui1t2/f79sNpu2bdtmfdMALogvVAZwxXXu3Fl5eXmaPXu26tSpo8zMTK1cuVK///67u1tTUFDQZd9HVlaWOnTooAceeEDLly9XYGCg9u/fr8WLF+vkyZOXff8ALhMDAK6gY8eOGZKMNWvWXLBOkjFjxgwjNjbW8PPzM+rXr29s2LDB2Lt3r9G2bVujfPnyRnR0tPHTTz+5bDdt2jSjTp06Rrly5Yxbb73V+Oijj4qsu3DhQvN5YmKiERoaanz//feGYRhGrVq1jHfeecelfubMmUanTp0MPz8/o27dusYXX3zhsuYXX3xh1K1b1/Dx8THatWtnJCUlGZKMY8eOFfvaFi5caHh5eRmnT58udj49Pd2Q5PKIi4szDMMwli1bZrRu3doICAgwKleubMTGxrq8B+du17ZtW8MwDKNt27bGs88+67Kfv//97+a6hmEYU6dONV9HcHCw0blz52L7A1A8Tv8BuKL8/f3l7++vRYsWKTc394K1r776qp544glt27ZN9evX12OPPab+/ftrxIgR2rJliwzD0MCBA836hQsX6tlnn9Vzzz2nHTt2qH///urVq5dWr15dZG3DMDRo0CB99NFH+vrrr9WkSZPz9vHKK6/okUce0Q8//KD7779fPXr00NGjRyVJ6enp6tKlizp16qTvv/9e/fv310svvXTB1xUaGqozZ85o4cKFMor5+tWwsDB99tlnkqQ9e/bo8OHDmjRpkiTp5MmTSkhI0JYtW7Ry5Up5eHjooYceMr/Ee9OmTZKk//73vzp8+LA+//zzC/ZSaMuWLXrmmWc0ZswY7dmzRykpKWrTpk2ptgXw/3NzqANwA/r000+NSpUqGb6+vsYdd9xhjBgxwjxSVEiSMXLkSPN5amqqIcn417/+ZY59/PHHhq+vr/n8jjvuMPr27euyTteuXY3777/fZd0FCxYYjz32mNGgQQPjl19+cakv7kjV2X2cOHHCkGQsW7bMMAzDGD58uNGoUSOXNV566aULHqkyDMN48cUXDS8vL6Ny5crGvffea4wfP97IyMgw51evXl3iGoZhGFlZWYYkY/v27YZh/L+jXN99951LXUlHqj777DPDbrcbTqfzgvsDcH4cqQJwxXXu3FmHDh3S4sWLde+992rNmjVq3ry5kpKSXOrOPnoUEhIiSWrcuLHL2KlTp+R0OiVJu3btUuvWrV3WaN26tXbt2uUyNmTIEG3cuFHr1q3TTTfdVGK/Z/dRoUIF2e1284LyPXv2qGXLli71rVq1KnHN1157TRkZGZoxY4YaNmyoGTNmqH79+tq+ffsFt9u7d6+6d++uOnXqyG63Kzw8XJJ04MCBEvd5Iffcc49q1aqlOnXq6PHHH9ecOXP0xx9/XNKawI2GUAXALXx9fXXPPffo5Zdf1oYNG9SzZ0+NGjXKpaZcuXLmn20223nHCk99ldY999yjX3/9VcuXLy9V/dn7LNzvxe6zOFWqVFHXrl31z3/+U7t27VL16tX1z3/+84LbPPjggzp69KhmzpypjRs3auPGjZKkvLy8C27n4eFR5FTj6dOnzT9XrFhRW7du1ccff6xq1aopMTFRTZs2vehPHwI3MkIVgKtCRETEJX/yrUGDBkVuy7B+/XpFRES4jP3tb3/T3Llz9eSTT+qTTz65pH3Wq1dPW7ZscRnbvHnzRa/j7e2tm2++2XwPvL29JUn5+flmze+//649e/Zo5MiR6tChgxo0aKBjx44VWefc7aS/PtV4+PBh83l+fr527NjhUuPl5aWYmBiNHz9eP/zwg/bv369Vq1Zd9GsBblTcUgHAFfX777+ra9eu6t27t5o0aaKKFStqy5YtGj9+vP7+979f0tpDhw7VI488ottuu00xMTFasmSJPv/8c/33v/8tUvvQQw/p3//+tx5//HF5eXmpS5cuZdpn//799fbbb2v48OHq06ePtm3bZp7GLDySdq6lS5fqk08+Ubdu3XTrrbfKMAwtWbJEX375pWbNmiVJqlWrlmw2m5YuXar7779ffn5+qlSpkqpUqaL3339f1apV04EDB/TCCy+4rB0cHCw/Pz+lpKSoRo0a8vX1VUBAgNq3b6+EhAQlJyfr5ptv1ttvv+1yFGrp0qX6+eef1aZNG1WqVElffvmlCgoKVK9evTK9L8ANyd0XdQG4sZw6dcp44YUXjObNmxsBAQFG+fLljXr16hkjR440/vjjD7NO59z6oLgLsIu7mPtib6kwb948w9fX1/jss88Mwyj+QvWz6w3DMAICAoxZs2aZz8+9pcL06dMNScaff/5Z7Huwb98+o2/fvsatt95q+Pn5GYGBgUbLli1d1jQMwxgzZowRGhpq2Gw284LyFStWGA0aNDB8fHyMJk2aGGvWrCnS48yZM42wsDDDw8PDvKVCXl6eMWDAAKNy5cpGcHCwMW7cOJcL1b/++mujbdu2RqVKlQw/Pz+jSZMmxrx584rtH0DxbIZRzOd5AQBl9tprr2nGjBk6ePCgu1sBcAVx+g8ALtG0adPUsmVLValSRevXr9eECRNc7p8F4MZAqAKAS7R3716NHTtWR48eVc2aNfXcc89pxIgR7m4LwBXG6T8AAAALcEsFAAAACxCqAAAALECoAgAAsAChCgAAwAKEKgAAAAsQqgAAACxAqAIAALAAoQoAAMAChCoAAAAL/H/OkxGeZKyWPwAAAABJRU5ErkJggg=="},"metadata":{}}],"execution_count":238},{"cell_type":"code","source":"sns.countplot(x='Exercise Frequency', data=df)\nplt.title(\"Exercise Frequency Distribution\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:30.323607Z","iopub.execute_input":"2024-12-18T15:36:30.324343Z","iopub.status.idle":"2024-12-18T15:36:31.198577Z","shell.execute_reply.started":"2024-12-18T15:36:30.324271Z","shell.execute_reply":"2024-12-18T15:36:31.197506Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":239},{"cell_type":"code","source":"df['Property Type'].value_counts().plot.pie(autopct='%1.1f%%')\nplt.title(\"Property Type Distribution\")\nplt.ylabel(\"\")  # Optional: to hide ylabel\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:31.200037Z","iopub.execute_input":"2024-12-18T15:36:31.200471Z","iopub.status.idle":"2024-12-18T15:36:31.429981Z","shell.execute_reply.started":"2024-12-18T15:36:31.200414Z","shell.execute_reply":"2024-12-18T15:36:31.428599Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":240},{"cell_type":"markdown","source":"Inter-Features Analysis!!!","metadata":{}},{"cell_type":"code","source":"#Age vs. Annual Income(Understand how income varies across age groups)\n\ndf['Age Group'] = pd.cut(df['Age'], bins=[0, 25, 40, 60, 100], labels=['0-25', '26-40', '41-60', '60+'])\nsns.boxplot(x='Age Group', y='Annual Income', data=df)\nplt.title(\"Annual Income by Age Group\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:31.431485Z","iopub.execute_input":"2024-12-18T15:36:31.432021Z","iopub.status.idle":"2024-12-18T15:36:31.981493Z","shell.execute_reply.started":"2024-12-18T15:36:31.431958Z","shell.execute_reply":"2024-12-18T15:36:31.979927Z"}},"outputs":[{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/seaborn/categorical.py:641: FutureWarning: The default of observed=False is deprecated and will be changed to True in a future version of pandas. Pass observed=False to retain current behavior or observed=True to adopt the future default and silence this warning.\n  grouped_vals = vals.groupby(grouper)\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"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"},"metadata":{}}],"execution_count":241},{"cell_type":"code","source":"# Gender vs. Premium Amount(Analyze if there's a difference in premium amounts paid by different genders.)\nsns.violinplot(x='Gender', y='Premium Amount', data=df)\nplt.title(\"Premium Amount Distribution by Gender\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:31.982838Z","iopub.execute_input":"2024-12-18T15:36:31.983124Z","iopub.status.idle":"2024-12-18T15:36:35.199114Z","shell.execute_reply.started":"2024-12-18T15:36:31.983096Z","shell.execute_reply":"2024-12-18T15:36:35.198064Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":242},{"cell_type":"code","source":"# Education Level vs. Occupation(Understand the relationship between education and job roles)\n# edu_occ.plot(kind='bar', colormap='viridis', figsize=(12, 6))\n# plt.title(\"Education Level vs. Occupation\")\n# plt.ylabel(\"Percentage\")\n# plt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:35.200599Z","iopub.execute_input":"2024-12-18T15:36:35.201047Z","iopub.status.idle":"2024-12-18T15:36:35.205581Z","shell.execute_reply.started":"2024-12-18T15:36:35.201001Z","shell.execute_reply":"2024-12-18T15:36:35.20446Z"}},"outputs":[],"execution_count":243},{"cell_type":"code","source":"#Smoking Status vs. Health Score\nsns.boxplot(x='Smoking Status', y='Health Score', data=df)\nplt.title(\"Health Score by Smoking Status\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:35.20693Z","iopub.execute_input":"2024-12-18T15:36:35.207322Z","iopub.status.idle":"2024-12-18T15:36:35.946353Z","shell.execute_reply.started":"2024-12-18T15:36:35.207277Z","shell.execute_reply":"2024-12-18T15:36:35.945317Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 Axes>","image/png":"iVBORw0KGgoAAAANSUhEUgAAAjIAAAHHCAYAAACle7JuAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuNSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/xnp5ZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABBm0lEQVR4nO3deVyVZf7/8fdhkYMgKCggIyqaigumoilaWooZNeZCmi3jklNO45JSUzGVqZXYMtmmWea4lKbjmJqV+1ZjaqlpOhqjhmkuuIOigML1+6Mf5+sRUDDwnBtfz8fjfsS57uvc9+ccvDtvrvu6z20zxhgBAABYkIerCwAAALhWBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBkAAGBZBBmghEaNGiWbzVaivsePHy/jqqzBZrNpyJAhri7juivuv4P+/furdu3a16cooJwgyMBSpk2bJpvNpk2bNhW6/vbbb1eTJk2uc1XS2LFjtWDBgjLZ9qJFi9ShQweFhISoYsWKqlOnjnr37q0lS5aUyf7Kg3379mnAgAGqW7eu7Ha7wsLC1L59e7344ouuLs1t5eXlacaMGWrdurWCgoJUqVIl1a9fX3379tWGDRsc/Xbu3KlRo0Zp375917yvWbNm6a233vr9RQMiyACloqyCzBtvvKF7771XNptNSUlJGj9+vBISErR7927Nnj271PdXHuzZs0fNmzfX0qVL9cADD+i9997T4MGDFRwcrFdffdXV5V3R5MmTlZKS4pJ9Dxs2TP369VP16tU1atQovfrqq4qPj9eGDRucQvPOnTs1evRoggzchperCwBQuIsXL+qll15S586dtWzZsgLrjx49et1qycvLU05Ojux2+3Xb57UaP368zp49q61bt6pWrVpO667ne3YtvL29XbLftLQ0TZw4UY8++qg+/PBDp3VvvfWWjh075pK6gOJgRAY3hE8++UQxMTHy9fVVUFCQ+vTpowMHDjj1+eabb9SrVy/VrFlTPj4+ioiI0IgRI3T+/PkrbttmsykzM1PTp0+XzWaTzWZT//79nfqcPn1a/fv3V+XKlRUYGKgBAwbo3LlzV9zu8ePHlZGRoXbt2hW6PiQkxOlxVlaWRo0apfr168tut6t69erq2bOn9u7d6+iTmZmpJ598UhEREfLx8VGDBg30xhtvyBhT4DUNGTJEM2fOVOPGjeXj4+P4q/zgwYN65JFHFBoaKh8fHzVu3Fj//Oc/r/haLjdz5kw1aNBAdrtdMTEx+vrrrx3rVq9eLZvNpvnz5xd43qxZs2Sz2bR+/foit713717VqFGjQIiRCr5ntWvX1h//+EetWbNGLVu2lK+vr6Kjo7VmzRpJ0meffabo6GhHnT/88EOBba5atUq33Xab/Pz8VLlyZXXr1k27du266nvwyy+/6KabblKTJk2UlpYmqeAcmX379slms+mNN97Qhx9+qLp168rHx0etWrXS999/X2Cbc+fOVaNGjWS329WkSRPNnz+/WPNuUlNTZYwp9N+azWZzvG/Tpk1Tr169JEl33HGH4997/vu1cOFC3XPPPQoPD5ePj4/q1q2rl156Sbm5uY7t3X777fryyy/1yy+/OJ6fX1/+qePLR3vWrFnjtB9J2r17txISEhQWFia73a4aNWqoT58+Sk9Pv+JrRfnDiAwsKT09vdCJkxcuXCjQ9sorr+iFF15Q79699ec//1nHjh3Tu+++q/bt2+uHH35Q5cqVJf32IXDu3Dk9/vjjCg4O1nfffad3331Xv/76q+bOnVtkLR9//LH+/Oc/65ZbbtFjjz0mSapbt65Tn969eysyMlLJycnasmWLPvroI4WEhFzxVEdISIh8fX21aNEiDR06VEFBQUX2zc3N1R//+EetXLlSffr00RNPPKEzZ85o+fLl2rFjh+rWrStjjO69916tXr1aAwcOVLNmzbR06VL97W9/08GDBzV+/Hinba5atUr/+te/NGTIEFWtWlW1a9dWWlqa2rRp4wg61apV0+LFizVw4EBlZGRo+PDhRdaYb+3atZozZ46GDRsmHx8fTZw4UXfddZe+++47NWnSRLfffrsiIiI0c+ZM9ejRw+m5M2fOVN26dRUbG1vk9mvVqqUVK1Zo1apV6tix41Xr2bNnjx588EENGjRIDz/8sN544w117dpVkyZN0t///nf99a9/lSQlJyerd+/eSklJkYfHb38DrlixQvHx8apTp45GjRql8+fP691331W7du20ZcuWIgPE3r171bFjRwUFBWn58uWqWrXqFWucNWuWzpw5o0GDBslms+m1115Tz5499fPPPztGcb788kvdf//9io6OVnJysk6dOqWBAwfqD3/4w1Xfg/zQN3fuXPXq1UsVK1YstF/79u01bNgwvfPOO/r73/+uhg0bSpLjv9OmTZO/v78SExPl7++vVatWaeTIkcrIyNDrr78uSXruueeUnp6uX3/91fFvzt/f/6o1XionJ0ddunRRdna2hg4dqrCwMB08eFBffPGFTp8+rcDAwBJtDxZnAAuZOnWqkXTFpXHjxo7++/btM56enuaVV15x2s727duNl5eXU/u5c+cK7C85OdnYbDbzyy+/ONpefPFFc/mh4+fnZ/r161fg+fl9H3nkEaf2Hj16mODg4Ku+3pEjRxpJxs/Pz8THx5tXXnnFbN68uUC/f/7zn0aSefPNNwusy8vLM8YYs2DBAiPJvPzyy07r77vvPmOz2cyePXscbZKMh4eH+e9//+vUd+DAgaZ69erm+PHjTu19+vQxgYGBhb6Hl8r/HW3atMnR9ssvvxi73W569OjhaEtKSjI+Pj7m9OnTjrajR48aLy8v8+KLL15xHzt27DC+vr5GkmnWrJl54oknzIIFC0xmZmaBvrVq1TKSzLfffutoW7p0qZFkfH19nX7vH3zwgZFkVq9e7Whr1qyZCQkJMSdOnHC0bdu2zXh4eJi+ffs62vL/HRw7dszs2rXLhIeHm1atWpmTJ0861dOvXz9Tq1Ytx+PU1FQjyQQHBzv1XbhwoZFkFi1a5GiLjo42NWrUMGfOnHG0rVmzxkhy2mZR+vbtaySZKlWqmB49epg33njD7Nq1q0C/uXPnFngf8hX2+x80aJCpWLGiycrKcrTdc889hdaUf3ynpqY6ta9evdppnz/88IORZObOnXvV14Xyj1NLsKQJEyZo+fLlBZamTZs69fvss8+Ul5en3r176/jx444lLCxM9erV0+rVqx19fX19HT9nZmbq+PHjatu2rYwxhZ5SKIm//OUvTo9vu+02nThxQhkZGVd83ujRozVr1izH5NXnnntOMTExatGihdPpi3nz5qlq1aoaOnRogW3kXyr+1VdfydPTU8OGDXNa/+STT8oYo8WLFzu1d+jQQY0aNXI8NsZo3rx56tq1q4wxTu9nly5dlJ6eri1btlz1vYiNjVVMTIzjcc2aNdWtWzctXbrUcQqib9++ys7O1r///W9Hvzlz5ujixYt6+OGHr7j9xo0ba+vWrXr44Ye1b98+vf322+revbtCQ0M1efLkAv0bNWrkNMLTunVrSVLHjh1Vs2bNAu0///yzJOnw4cPaunWr+vfv7zRa1rRpU3Xu3FlfffVVgX3t2LFDHTp0UO3atbVixQpVqVLliq8l3/333+/U97bbbnOq5dChQ9q+fbv69u3rNLrRoUMHRUdHF2sfU6dO1XvvvafIyEjNnz9fTz31lBo2bKhOnTrp4MGDxdrGpcfQmTNndPz4cd122206d+6cfvrpp2JtozjyR1yWLl161VO0KP8IMrCkW265RXFxcQWWyz8Ydu/eLWOM6tWrp2rVqjktu3btcpr8uX//fseHkr+/v6pVq6YOHTpI0u8+737pB6IkR52nTp266nMfeOABffPNNzp16pSWLVumBx98UD/88IO6du2qrKwsSb+dqmjQoIG8vIo+W/zLL78oPDxclSpVcmrPPy3wyy+/OLVHRkY6PT527JhOnz6tDz/8sMB7OWDAAEnFm0xbr169Am3169fXuXPnHJNKo6Ki1KpVK82cOdPRZ+bMmWrTpo1uuummq+6jfv36+vjjj3X8+HH9+OOPGjt2rLy8vPTYY49pxYoVTn0v/93kf0hGREQU2p7/O8t/vxo0aFBg/w0bNtTx48eVmZnp1N61a1dVqlRJS5cuVUBAwFVfR1E1Xv7vJ7+Wwt6b4rxfkuTh4aHBgwdr8+bNOn78uBYuXKj4+HitWrVKffr0KdY2/vvf/6pHjx4KDAxUQECAqlWr5giepTl3JTIyUomJifroo49UtWpVdenSRRMmTGB+zA2KOTIo1/Ly8mSz2bR48WJ5enoWWJ//12tubq46d+6skydP6plnnlFUVJT8/Px08OBB9e/fX3l5eb+rjsL2LanAJNsrCQgIUOfOndW5c2d5e3tr+vTp2rhxoyNslbZL/7qW5HgPHn74YfXr16/Q51w+IvZ79O3bV0888YR+/fVXZWdna8OGDXrvvfdKtA1PT09FR0crOjpasbGxuuOOOzRz5kzFxcU59SnquYUpye/scgkJCZo+fbpmzpypQYMGFft5ZVHLlQQHB+vee+/Vvffeq9tvv11r167VL7/8UugE6nynT59Whw4dFBAQoDFjxji+w2fLli165plninUMFfVFk5dOFs73j3/8Q/3799fChQu1bNkyDRs2TMnJydqwYYNq1KhR/BcLyyPIoFzLn+QaGRmp+vXrF9lv+/bt+t///qfp06erb9++jvbly5cXaz/F/abf0tKyZUtNnz5dhw8flvTb69y4caMuXLhQ5CW8+ZNgz5w54zQqkz/kf6UPKUmqVq2aKlWqpNzcXKcgUFK7d+8u0Pa///1PFStWVLVq1Rxtffr0UWJioj799FOdP39e3t7euv/++695vy1btpQkx3v2e+W/X4V978tPP/2kqlWrys/Pz6n99ddfl5eXl/7617+qUqVKevDBB0u1lj179hRYV1hbSbRs2VJr167V4cOHVatWrSL/ra9Zs0YnTpzQZ599pvbt2zvaU1NTC/Qtahv5I02nT592ar98tDBffkh9/vnn9e2336pdu3aaNGmSXn755eK8NJQTnFpCudazZ095enpq9OjRBf56NcboxIkTkv7vL95L+xhj9PbbbxdrP35+fgX+5/t7nTt3rsjLjPPns+Sf1khISNDx48cLHbHIf0133323cnNzC/QZP368bDab4uPjr1iPp6enEhISNG/ePO3YsaPA+uJ+18j69eud5tIcOHBACxcu1J133uk08lC1alXFx8frk08+0cyZM3XXXXdd9eoe6bfL6Au7ei1/zkphp4KuRfXq1dWsWTNNnz7d6Xe/Y8cOLVu2THfffXeB59hsNn344Ye677771K9fP33++eelUkt4eLiaNGmiGTNm6OzZs472tWvXavv27Vd9/pEjR7Rz584C7Tk5OVq5cqU8PDwcp6jyw9nl/94LO4ZycnI0ceLEAtv18/Mr9DRQ/tV+l16On5ubW+C7bTIyMnTx4kWntujoaHl4eCg7O7vI14nyiREZlGt169bVyy+/rKSkJO3bt0/du3dXpUqVlJqaqvnz5+uxxx7TU089paioKNWtW1dPPfWUDh48qICAAM2bN69Yc1gkKSYmRitWrNCbb76p8PBwRUZGOiaHXqtz586pbdu2atOmje666y5FRETo9OnTWrBggb755ht1795dzZs3l/TbaZgZM2YoMTFR3333nW677TZlZmZqxYoV+utf/6pu3bqpa9euuuOOO/Tcc89p3759uvnmm7Vs2TItXLhQw4cPL3DJeGHGjRun1atXq3Xr1nr00UfVqFEjnTx5Ulu2bNGKFSt08uTJq26jSZMm6tKli9Pl19JvE5sv17dvX913332SpJdeeqlY79urr76qzZs3q2fPno5TXVu2bNGMGTMUFBRUrEvEi+v1119XfHy8YmNjNXDgQMfl14GBgRo1alShz/Hw8NAnn3yi7t27q3fv3vrqq6+KdZn41YwdO1bdunVTu3btNGDAAJ06dUrvvfeemjRp4hRuCvPrr7/qlltuUceOHdWpUyeFhYXp6NGj+vTTT7Vt2zYNHz7cESKbNWsmT09Pvfrqq0pPT5ePj486duyotm3bqkqVKurXr5+GDRsmm82mjz/+uNDTXzExMZozZ44SExPVqlUr+fv7q2vXrmrcuLHatGmjpKQknTx5UkFBQZo9e3aB0LJq1SoNGTJEvXr1Uv369XXx4kV9/PHHjrCNG8z1v1AKuHb5l2d+//33ha7v0KGD0+XX+ebNm2duvfVW4+fnZ/z8/ExUVJQZPHiwSUlJcfTZuXOniYuLM/7+/qZq1arm0UcfNdu2bTOSzNSpUx39Crv8+qeffjLt27d3XPabfyn2pZfdFvY6Lr/M9FIXLlwwkydPNt27dze1atUyPj4+pmLFiqZ58+bm9ddfN9nZ2U79z507Z5577jkTGRlpvL29TVhYmLnvvvvM3r17HX3OnDljRowYYcLDw423t7epV6+eef311x2XaOeTZAYPHlxoXWlpaWbw4MEmIiLCsZ9OnTqZDz/8sMjXcvl2P/nkE1OvXj3j4+NjmjdvXuilvMYYk52dbapUqWICAwPN+fPnr7p9Y4xZt26dGTx4sGnSpIkJDAw03t7epmbNmqZ///5O74Uxv11+fc899xRZ56XyL4V+/fXXndpXrFhh2rVrZ3x9fU1AQIDp2rWr2blzp1Ofwv4dnDt3znTo0MH4+/ubDRs2GGOKvvz68n3m13j5peizZ882UVFRxsfHxzRp0sR8/vnnJiEhwURFRRX9hhljMjIyzNtvv226dOliatSoYby9vU2lSpVMbGysmTx5coF/H5MnTzZ16tQxnp6eTpdFr1u3zrRp08b4+vqa8PBw8/TTTzsuZ7/0d3z27Fnz4IMPmsqVKxe4PHzv3r0mLi7O+Pj4mNDQUPP3v//dLF++3GkbP//8s3nkkUdM3bp1jd1uN0FBQeaOO+4wK1asuOLrRPlkM6aMZosBwO908eJFhYeHq2vXrpoyZYqry7GkZs2aqVq1asWe7wVYDXNkALitBQsW6NixY04TsFG4CxcuFDgFs2bNGm3btk233367a4oCrgNGZAC4nY0bN+rHH3/USy+9pKpVqxbri/ZudPv27VNcXJwefvhhhYeH66efftKkSZMUGBioHTt2KDg42NUlAmWCyb4A3M7777+vTz75RM2aNdO0adNcXY4lVKlSRTExMfroo4907Ngx+fn56Z577tG4ceMIMSjXGJEBAACWxRwZAABgWQQZAABgWeV+jkxeXp4OHTqkSpUqXfevkQcAANfGGKMzZ84oPDxcHh5Fj7uU+yBz6NChAnexBQAA1nDgwIEr3gi03AeZ/JvjHThwQAEBAS6uBgAAFEdGRoYiIiKcbnJbmHIfZPJPJwUEBBBkAACwmKtNC2GyLwAAsCyCDAAAsCyCDAAAsCyCDAAAsCyXB5mDBw/q4YcfVnBwsHx9fRUdHa1NmzY51htjNHLkSFWvXl2+vr6Ki4vT7t27XVgxAABwFy4NMqdOnVK7du3k7e2txYsXa+fOnfrHP/6hKlWqOPq89tpreueddzRp0iRt3LhRfn5+6tKli7KyslxYOQAAcAcuvWnks88+q3Xr1umbb74pdL0xRuHh4XryySf11FNPSZLS09MVGhqqadOmqU+fPlfdR0ZGhgIDA5Wens7l1wAAWERxP79dOiLz+eefq2XLlurVq5dCQkLUvHlzTZ482bE+NTVVR44cUVxcnKMtMDBQrVu31vr16wvdZnZ2tjIyMpwWAABQPrk0yPz88896//33Va9ePS1dulSPP/64hg0bpunTp0uSjhw5IkkKDQ11el5oaKhj3eWSk5MVGBjoWLg9AQAA5ZdLg0xeXp5atGihsWPHqnnz5nrsscf06KOPatKkSde8zaSkJKWnpzuWAwcOlGLFAADAnbg0yFSvXl2NGjVyamvYsKH2798vSQoLC5MkpaWlOfVJS0tzrLucj4+P43YE3JYAAIDyzaVBpl27dkpJSXFq+9///qdatWpJkiIjIxUWFqaVK1c61mdkZGjjxo2KjY29rrUCAAD349KbRo4YMUJt27bV2LFj1bt3b3333Xf68MMP9eGHH0r67UZRw4cP18svv6x69eopMjJSL7zwgsLDw9W9e3dXll6uGGPKxeXsxhhlZ2dL+m1k7mo3GrMCu91eLl4HAJQVlwaZVq1aaf78+UpKStKYMWMUGRmpt956Sw899JCjz9NPP63MzEw99thjOn36tG699VYtWbJEdrvdhZWXL1lZWYqPj3d1GSjE4sWL5evr6+oyAMBtufR7ZK4Hvkfm6s6fP0+QcVMEGQA3quJ+frt0RAbuwW63a/Hixa4u43fLyspSjx49JEnz588vF6N25eE1wLU4dey+OHVcOggykM1mK3d/9dvt9nL3moBrwalj98WIa+lw+U0jAQAArhUjMgBQjnHq2H2Vh9fgDggyAFCOceoY5R2nlgAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGURZAAAgGW5NMiMGjVKNpvNaYmKinKsz8rK0uDBgxUcHCx/f38lJCQoLS3NhRUDAAB34vIRmcaNG+vw4cOO5T//+Y9j3YgRI7Ro0SLNnTtXa9eu1aFDh9SzZ08XVgsAANyJl8sL8PJSWFhYgfb09HRNmTJFs2bNUseOHSVJU6dOVcOGDbVhwwa1adPmepcKAADcjMtHZHbv3q3w8HDVqVNHDz30kPbv3y9J2rx5sy5cuKC4uDhH36ioKNWsWVPr168vcnvZ2dnKyMhwWgAAQPnk0iDTunVrTZs2TUuWLNH777+v1NRU3XbbbTpz5oyOHDmiChUqqHLlyk7PCQ0N1ZEjR4rcZnJysgIDAx1LREREGb8KAADgKi49tRQfH+/4uWnTpmrdurVq1aqlf/3rX/L19b2mbSYlJSkxMdHxOCMjgzADAEA55fJTS5eqXLmy6tevrz179igsLEw5OTk6ffq0U5+0tLRC59Tk8/HxUUBAgNMCAADKJ7cKMmfPntXevXtVvXp1xcTEyNvbWytXrnSsT0lJ0f79+xUbG+vCKgEAgLtw6amlp556Sl27dlWtWrV06NAhvfjii/L09NQDDzygwMBADRw4UImJiQoKClJAQICGDh2q2NhYrlgCAACSXBxkfv31Vz3wwAM6ceKEqlWrpltvvVUbNmxQtWrVJEnjx4+Xh4eHEhISlJ2drS5dumjixImuLBkAALgRlwaZ2bNnX3G93W7XhAkTNGHChOtUEQAAsBK3miMDAABQEgQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWQQZAABgWW4TZMaNGyebzabhw4c72rKysjR48GAFBwfL399fCQkJSktLc12RAADArbhFkPn+++/1wQcfqGnTpk7tI0aM0KJFizR37lytXbtWhw4dUs+ePV1UJQAAcDcuDzJnz57VQw89pMmTJ6tKlSqO9vT0dE2ZMkVvvvmmOnbsqJiYGE2dOlXffvutNmzY4MKKAQCAu3B5kBk8eLDuuecexcXFObVv3rxZFy5ccGqPiopSzZo1tX79+iK3l52drYyMDKcFAACUT16u3Pns2bO1ZcsWff/99wXWHTlyRBUqVFDlypWd2kNDQ3XkyJEit5mcnKzRo0eXdqkAAMANuWxE5sCBA3riiSc0c+ZM2e32UttuUlKS0tPTHcuBAwdKbdsAAMC9uCzIbN68WUePHlWLFi3k5eUlLy8vrV27Vu+88468vLwUGhqqnJwcnT592ul5aWlpCgsLK3K7Pj4+CggIcFoAAED55LJTS506ddL27dud2gYMGKCoqCg988wzioiIkLe3t1auXKmEhARJUkpKivbv36/Y2FhXlAwAANyMy4JMpUqV1KRJE6c2Pz8/BQcHO9oHDhyoxMREBQUFKSAgQEOHDlVsbKzatGnjipIBAICbcelk36sZP368PDw8lJCQoOzsbHXp0kUTJ050dVkAAMBNuFWQWbNmjdNju92uCRMmaMKECa4pCAAAuDWXf48MAADAtSLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAyyLIAAAAy7rmIJOTk6OUlBRdvHixNOsBAAAothIHmXPnzmngwIGqWLGiGjdurP3790uShg4dqnHjxpV6gQAAAEUpcZBJSkrStm3btGbNGqe7VsfFxWnOnDmlWhwAAMCVlPibfRcsWKA5c+aoTZs2stlsjvbGjRtr7969pVocAADAlZR4RObYsWMKCQkp0J6ZmekUbAAAAMpaiYNMy5Yt9eWXXzoe54eXjz76SLGxsaVXGQAAwFWU+NTS2LFjFR8fr507d+rixYt6++23tXPnTn377bdau3ZtWdQIAABQqBKPyNx6663atm2bLl68qOjoaC1btkwhISFav369YmJiyqJGAACAQpVoRObChQsaNGiQXnjhBU2ePLmsagIAACiWEo3IeHt7a968eWVVCwAAQImU+NRS9+7dtWDBgjIoBQAAoGRKPNm3Xr16GjNmjNatW6eYmBj5+fk5rR82bFipFQcAAHAlJQ4yU6ZMUeXKlbV582Zt3rzZaZ3NZiPIAACA66bEQSY1NbUs6gAAACixa777tSQZY2SMKa1aAAAASuSagsyMGTMUHR0tX19f+fr6qmnTpvr4449LuzYAAIArKvGppTfffFMvvPCChgwZonbt2kmS/vOf/+gvf/mLjh8/rhEjRpR6kQAAAIUpcZB599139f7776tv376OtnvvvVeNGzfWqFGjCDIAAOC6KfGppcOHD6tt27YF2tu2bavDhw+XSlEAAADFUeIgc9NNN+lf//pXgfY5c+aoXr16pVIUAABAcZT41NLo0aN1//336+uvv3bMkVm3bp1WrlxZaMABAAAoKyUOMgkJCdq4caPGjx/vuFVBw4YN9d1336l58+alXZ/bM8YoKyvL1WVAcvo98DtxH3a7XTabzdVlACinShxkJCkmJkaffPJJaddiSVlZWYqPj3d1GbhMjx49XF0C/r/FixfL19fX1WVcE/5QcR/8oeKe3OEPlRIHma+++kqenp7q0qWLU/vSpUuVl5fHhzqAcoM/VNwTf6i4D3f4Q6XEQebZZ5/VuHHjCrQbY/Tss8/e0Af92WYPyHhc0yAXSoMxUt7F33728JI4neEytryL8t/6qavLAHADKPGn7u7du9WoUaMC7VFRUdqzZ0+pFGVVxsNL8vR2dRk3uAquLgCSyuONS9679aR8PMvjK7MGY6ScvN9+ruDB3ymulJ1r05D/BLm6DIcSB5nAwED9/PPPql27tlP7nj175OfnV1p1AYBb8fE08vF0dRU3NrurC8D/516BvsTfI9OtWzcNHz5ce/fudbTt2bNHTz75pO69995SLQ4AAOBKShxkXnvtNfn5+SkqKkqRkZGKjIxUw4YNFRwcrDfeeKMsagQAACjUNZ1a+vbbb7V8+XJt27bNcffr9u3bl0V9AAAARbqmS2xsNpvuvPNO3XnnnaVdDwAAQLEV+9TS+vXr9cUXXzi1zZgxQ5GRkQoJCdFjjz2m7OzsUi8QAACgKMUOMmPGjNF///tfx+Pt27dr4MCBiouL07PPPqtFixYpOTm5TIoEAAAoTLGDzNatW9WpUyfH49mzZ6t169aaPHmyEhMT9c4773DTSAAAcF0VO8icOnVKoaGhjsdr1651+hbfVq1a6cCBA6VbHQAAwBUUO8iEhoYqNTVVkpSTk6MtW7aoTZs2jvVnzpyRtzffagsAAK6fYgeZu+++W88++6y++eYbJSUlqWLFirrtttsc63/88UfVrVu3TIoEAAAoTLEvv37ppZfUs2dPdejQQf7+/po+fboqVPi/+9r885//5HJsAABwXRU7yFStWlVff/210tPT5e/vL09P55uOzJ07V/7+/qVeIAAAQFGu6Zt9CxMU5D53wgQAADeGEt9rCQAAwF0QZAAAgGW5NMi8//77atq0qQICAhQQEKDY2FgtXrzYsT4rK0uDBw9WcHCw/P39lZCQoLS0NBdWDAAA3IlLg0yNGjU0btw4bd68WZs2bVLHjh3VrVs3x60QRowYoUWLFmnu3Llau3atDh06pJ49e7qyZAAA4Eau6e7Xu3fv1urVq3X06FHl5eU5rRs5cmSxt9O1a1enx6+88oref/99bdiwQTVq1NCUKVM0a9YsdezYUZI0depUNWzYUBs2bHD6Mj4AAHBjKnGQmTx5sh5//HFVrVpVYWFhstlsjnU2m61EQeZSubm5mjt3rjIzMxUbG6vNmzfrwoULiouLc/SJiopSzZo1tX79+iKDTHZ2ttNduDMyMq6pHgAA4P5KHGRefvllvfLKK3rmmWdKpYDt27crNjZWWVlZ8vf31/z589WoUSNt3bpVFSpUUOXKlZ36h4aG6siRI0VuLzk5WaNHjy6V2gAAgHsr8RyZU6dOqVevXqVWQIMGDbR161Zt3LhRjz/+uPr166edO3de8/aSkpKUnp7uWLiRJQAA5VeJg0yvXr20bNmyUiugQoUKuummmxQTE6Pk5GTdfPPNevvttxUWFqacnBydPn3aqX9aWprCwsKK3J6Pj4/jKqj8BQAAlE/FOrX0zjvvOH6+6aab9MILL2jDhg2Kjo4ucMfrYcOG/a6C8vLylJ2drZiYGHl7e2vlypVKSEiQJKWkpGj//v2KjY39XfsAAADlQ7GCzPjx450e+/v7a+3atVq7dq1Tu81mK1GQSUpKUnx8vGrWrKkzZ85o1qxZWrNmjZYuXarAwEANHDhQiYmJCgoKUkBAgIYOHarY2FiuWAIAAJKKGWRSU1PLZOdHjx5V3759dfjwYQUGBqpp06ZaunSpOnfuLOm3AOXh4aGEhARlZ2erS5cumjhxYpnUAgAArKfEc2TGjBmjc+fOFWg/f/68xowZU6JtTZkyRfv27VN2draOHj2qFStWOEKMJNntdk2YMEEnT55UZmamPvvssyvOjwEAADeWEgeZ0aNH6+zZswXaz507x2XPAADguipxkDHGOH0JXr5t27YpKCioVIoCAAAojmJ/IV6VKlVks9lks9lUv359pzCTm5urs2fP6i9/+UuZFAkAAFCYYgeZt956S8YYPfLIIxo9erQCAwMd6ypUqKDatWtzWTQAALiuih1k+vXrJ0mKjIxU27ZtC3x/DAAAwPVWrCBz6Y0XmzdvrvPnz+v8+fOF9uWbdAEAwPVSrCBTuXLlQif4Xip/EnBubm6pFAYAAHA1xQoyq1evLus6AAAASqxYQaZDhw5lXQcAAECJFXuy7+XOnTun/fv3Kycnx6m9adOmv7soAACA4ihxkDl27JgGDBigxYsXF7qeOTIAAOB6KfE3+w4fPlynT5/Wxo0b5evrqyVLlmj69OmqV6+ePv/887KoEQAAoFAlHpFZtWqVFi5cqJYtW8rDw0O1atVS586dFRAQoOTkZN1zzz1lUScAAEABJR6RyczMVEhIiKTfbltw7NgxSVJ0dLS2bNlSutUBAABcQYmDTIMGDZSSkiJJuvnmm/XBBx/o4MGDmjRpkqpXr17qBQIAABSlxKeWnnjiCR0+fFiS9OKLL+quu+7SzJkzVaFCBU2bNq206wMAAChSiYPMww8/7Pg5JiZGv/zyi3766SfVrFlTVatWLdXiAAAArqTEp5by5eTkKCUlRRUqVFCLFi0IMQAA4LorcZA5d+6cBg4cqIoVK6px48bav3+/JGno0KEaN25cqRcIAABQlBIHmaSkJG3btk1r1qyR3W53tMfFxWnOnDmlWhwAAMCVlHiOzIIFCzRnzhy1adPG6Y7YjRs31t69e0u1OAAAgCsp8YjMsWPHHN8jc6nMzEynYAMAAFDWShxkWrZsqS+//NLxOD+8fPTRR4qNjS29ygAAAK6ixKeWxo4dq/j4eO3cuVMXL17U22+/rZ07d+rbb7/V2rVry6JGt2aM+b8HuRdcVwjgTi45FpyOEYu5tPZs7ocLSHI+Ftzh+C5xkLn11lu1detWjRs3TtHR0Vq2bJlatGih9evXKzo6uixqdGvZ2dmOnyttm+3CSgD3lJ2drYoVK7q6jGty6fE95D/BLqwEcE/ucHyXOMhIUt26dTV58uTSrgUAAKBEih1kMjIyitUvICDgmouxIh8fH8fPZ27uI3l6u7AawE3kXnCMUF56jFjNpbW/d+sJ+Xi6sBjATWTn/t8IpTsc38UOMpUrV77iVUnGGNlsNuXm3lgnkp3eE09vggxwGStfzXhp7T6eIsgAl3GH47vYQWb16tWOn40xuvvuu/XRRx/pD3/4Q5kUBgAAcDXFDjIdOnRweuzp6ak2bdqoTp06pV4UAABAcVzzTSMBAABcjSADAAAs63cFGXeY5AMAAG5cxZ4j07NnT6fHWVlZ+stf/iI/Pz+n9s8++6x0KgMAALiKYgeZwMBAp8cPP/xwqRcDAABQEsUOMlOnTi3LOgAAAEqMyb4AAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyCDIAAMCyXBpkkpOT1apVK1WqVEkhISHq3r27UlJSnPpkZWVp8ODBCg4Olr+/vxISEpSWluaiigEAgDtxaZBZu3atBg8erA0bNmj58uW6cOGC7rzzTmVmZjr6jBgxQosWLdLcuXO1du1aHTp0SD179nRh1QAAwF14uXLnS5YscXo8bdo0hYSEaPPmzWrfvr3S09M1ZcoUzZo1Sx07dpQkTZ06VQ0bNtSGDRvUpk0bV5QNAADchFvNkUlPT5ckBQUFSZI2b96sCxcuKC4uztEnKipKNWvW1Pr16wvdRnZ2tjIyMpwWAABQPrlNkMnLy9Pw4cPVrl07NWnSRJJ05MgRVahQQZUrV3bqGxoaqiNHjhS6neTkZAUGBjqWiIiIsi4dAAC4iNsEmcGDB2vHjh2aPXv279pOUlKS0tPTHcuBAwdKqUIAAOBuXDpHJt+QIUP0xRdf6Ouvv1aNGjUc7WFhYcrJydHp06edRmXS0tIUFhZW6LZ8fHzk4+NT1iUDAAA34NIRGWOMhgwZovnz52vVqlWKjIx0Wh8TEyNvb2+tXLnS0ZaSkqL9+/crNjb2epcLAADcjEtHZAYPHqxZs2Zp4cKFqlSpkmPeS2BgoHx9fRUYGKiBAwcqMTFRQUFBCggI0NChQxUbG8sVSwAAwLVB5v3335ck3X777U7tU6dOVf/+/SVJ48ePl4eHhxISEpSdna0uXbpo4sSJ17lSAADgjlwaZIwxV+1jt9s1YcIETZgw4TpUBAAArMRtrloCAAAoKYIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLIIMAACwLJfea6m8seVd1NXvHoUyY4yUd/G3nz28JJvNtfXcwGz5v4dyJDvXJnGEu4wxUk7ebz9X8ODwdqXfjgX3QZApRf5bP3V1CQDKyJD/BLm6BACF4NQSAACwLJsxplyPlWZkZCgwMFDp6ekKCAgo9e0bY5SVlVXq20XJZWVlqUePHpKk+fPny263u7giSJLdbpfNoucBOL7dB8e3eyrL47u4n9+cWvqdbDabfH19XV0GLmO32/m94Hfj+HZPHN+4FKeWAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZbk0yHz99dfq2rWrwsPDZbPZtGDBAqf1xhiNHDlS1atXl6+vr+Li4rR7927XFAsAANyOS4NMZmambr75Zk2YMKHQ9a+99preeecdTZo0SRs3bpSfn5+6dOmirKys61wpAABwR16u3Hl8fLzi4+MLXWeM0VtvvaXnn39e3bp1kyTNmDFDoaGhWrBggfr06XM9SwUAAG7IbefIpKam6siRI4qLi3O0BQYGqnXr1lq/fn2Rz8vOzlZGRobTAgAAyie3DTJHjhyRJIWGhjq1h4aGOtYVJjk5WYGBgY4lIiKiTOsEAACu47ZB5lolJSUpPT3dsRw4cMDVJQEAgDLitkEmLCxMkpSWlubUnpaW5lhXGB8fHwUEBDgtAACgfHLbIBMZGamwsDCtXLnS0ZaRkaGNGzcqNjbWhZUBAAB34dKrls6ePas9e/Y4Hqempmrr1q0KCgpSzZo1NXz4cL388suqV6+eIiMj9cILLyg8PFzdu3d3XdEAAMBtuDTIbNq0SXfccYfjcWJioiSpX79+mjZtmp5++mllZmbqscce0+nTp3XrrbdqyZIlstvtrioZAAC4EZcGmdtvv13GmCLX22w2jRkzRmPGjLmOVQEAAKtw2zkyAAAAV0OQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAlkWQAQAAluXl6gLgesYYZWVlubqM3+3S11AeXo8k2e122Ww2V5cBC+P4dl8c36XDZowxri6iLGVkZCgwMFDp6ekKCAhwdTlu6fz584qPj3d1GSjE4sWL5evr6+oyYGEc3+6L4/vKivv5zaklAABgWYzIoNwMPRtjlJ2dLUny8fEpF0O2DD3j9+L4dl8c31dW3M9v5shANput3AxvVqxY0dUlAG6F4xvlHaeWAACAZRFkAACAZRFkAACAZRFkAACAZRFkAACAZVkiyEyYMEG1a9eW3W5X69at9d1337m6JAAA4AbcPsjMmTNHiYmJevHFF7VlyxbdfPPN6tKli44ePerq0gAAgIu5fZB588039eijj2rAgAFq1KiRJk2apIoVK+qf//ynq0sDAAAu5tZBJicnR5s3b1ZcXJyjzcPDQ3FxcVq/fn2hz8nOzlZGRobTAgAAyie3DjLHjx9Xbm6uQkNDndpDQ0N15MiRQp+TnJyswMBAxxIREXE9SgUAAC7g1kHmWiQlJSk9Pd2xHDhwwNUlAQCAMuLW91qqWrWqPD09lZaW5tSelpamsLCwQp/j4+MjHx+f61EeAABwMbcekalQoYJiYmK0cuVKR1teXp5Wrlyp2NhYF1YGAADcgVuPyEhSYmKi+vXrp5YtW+qWW27RW2+9pczMTA0YMKBYzzfGSBKTfgEAsJD8z+38z/GiuH2Quf/++3Xs2DGNHDlSR44cUbNmzbRkyZICE4CLcubMGUli0i8AABZ05swZBQYGFrneZq4WdSwuLy9Phw4dUqVKlWSz2VxdDspYRkaGIiIidODAAQUEBLi6HACliOP7xmKM0ZkzZxQeHi4Pj6Jnwrj9iMzv5eHhoRo1ari6DFxnAQEB/I8OKKc4vm8cVxqJyefWk30BAACuhCADAAAsiyCDcsXHx0cvvvgi3yUElEMc3yhMuZ/sCwAAyi9GZAAAgGURZAAAgGURZAAAgGURZAAAgGURZGA5/fv3l81m07hx45zaFyxYwLc3AxZkjFFcXJy6dOlSYN3EiRNVuXJl/frrry6oDFZAkIEl2e12vfrqqzp16pSrSwHwO9lsNk2dOlUbN27UBx984GhPTU3V008/rXfffZdvaEeRCDKwpLi4OIWFhSk5ObnIPvPmzVPjxo3l4+Oj2rVr6x//+Md1rBBASUREROjtt9/WU089pdTUVBljNHDgQN15551q3ry54uPj5e/vr9DQUP3pT3/S8ePHHc/997//rejoaPn6+io4OFhxcXHKzMx04avB9USQgSV5enpq7Nixevfddwsdct68ebN69+6tPn36aPv27Ro1apReeOEFTZs27foXC6BY+vXrp06dOumRRx7Re++9px07duiDDz5Qx44d1bx5c23atElLlixRWlqaevfuLUk6fPiwHnjgAT3yyCPatWuX1qxZo549e4qvSLtx8IV4sJz+/fvr9OnTWrBggWJjY9WoUSNNmTJFCxYsUI8ePWSM0UMPPaRjx45p2bJljuc9/fTT+vLLL/Xf//7XhdUDuJKjR4+qcePGOnnypObNm6cdO3bom2++0dKlSx19fv31V0VERCglJUVnz55VTEyM9u3bp1q1armwcrgKIzKwtFdffVXTp0/Xrl27nNp37dqldu3aObW1a9dOu3fvVm5u7vUsEUAJhISEaNCgQWrYsKG6d++ubdu2afXq1fL393csUVFRkqS9e/fq5ptvVqdOnRQdHa1evXpp8uTJzJ27wRBkYGnt27dXly5dlJSU5OpSAJQSLy8veXl5SZLOnj2rrl27auvWrU7L7t271b59e3l6emr58uVavHixGjVqpHfffVcNGjRQamqqi18FrhcvVxcA/F7jxo1Ts2bN1KBBA0dbw4YNtW7dOqd+69atU/369eXp6Xm9SwRwjVq0aKF58+apdu3ajnBzOZvNpnbt2qldu3YaOXKkatWqpfnz5ysxMfE6VwtXYEQGlhcdHa2HHnpI77zzjqPtySef1MqVK/XSSy/pf//7n6ZPn6733ntPTz31lAsrBVBSgwcP1smTJ/XAAw/o+++/1969e7V06VINGDBAubm52rhxo8aOHatNmzZp//79+uyzz3Ts2DE1bNjQ1aXjOiHIoFwYM2aM8vLyHI9btGihf/3rX5o9e7aaNGmikSNHasyYMerfv7/rigRQYuHh4Vq3bp1yc3N15513Kjo6WsOHD1flypXl4eGhgIAAff3117r77rtVv359Pf/88/rHP/6h+Ph4V5eO64SrlgAAgGUxIgMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAMAACyLIAOgzNlsNi1YsKDI9bVr19Zbb7113eoBUH4QZIAbxLFjx/T444+rZs2a8vHxUVhYmLp06VLgnlSu8P333+uxxx4r8/1s27ZN9957r0JCQmS321W7dm3df//9Onr0qCRpzZo1stlsOn36dIm2u2/fPtlsNm3durX0iwZwRdw0ErhBJCQkKCcnR9OnT1edOnWUlpamlStX6sSJE64uTdWqVSvzfRw7dkydOnXSH//4Ry1dulSVK1fWvn379PnnnyszM7PM9w+gjBgA5d6pU6eMJLNmzZor9pNkJk2aZO655x7j6+troqKizLfffmt2795tOnToYCpWrGhiY2PNnj17nJ43ceJEU6dOHePt7W3q169vZsyYUWC78+fPdzweOXKkCQsLM9u2bTPGGFOrVi0zfvx4p/6TJ0823bt3N76+vuamm24yCxcudNrmwoULzU033WR8fHzM7bffbqZNm2YkmVOnThX62ubPn2+8vLzMhQsXCl2fmppqJDkt/fr1M8YYs3jxYtOuXTsTGBhogoKCzD333OP0Hlz+vA4dOhhjjOnQoYN54oknnPbTrVs3x3aNMWbChAmO1xESEmISEhIKrQ9A4Ti1BNwA/P395e/vrwULFig7O/uKfV966SX17dtXW7duVVRUlB588EENGjRISUlJ2rRpk4wxGjJkiKP//Pnz9cQTT+jJJ5/Ujh07NGjQIA0YMECrV68usG1jjIYOHaoZM2bom2++UdOmTYusY/To0erdu7d+/PFH3X333XrooYd08uRJSVJqaqruu+8+de/eXdu2bdOgQYP03HPPXfF1hYWF6eLFi5o/f75MIbeYi4iI0Lx58yRJKSkpOnz4sN5++21JUmZmphITE7Vp0yatXLlSHh4e6tGjh+NGpd99950kacWKFTp8+LA+++yzK9aSb9OmTRo2bJjGjBmjlJQULVmyRO3bty/WcwH8fy4OUgCuk3//+9+mSpUqxm63m7Zt25qkpCTHiEg+Seb55593PF6/fr2RZKZMmeJo+/TTT43dbnc8btu2rXn00UedttOrVy9z9913O2137ty55sEHHzQNGzY0v/76q1P/wkZkLq3j7NmzRpJZvHixMcaYZ555xjRp0sRpG88999wVR2SMMebvf/+78fLyMkFBQeauu+4yr732mjly5Ihj/erVq6+6DWOMOXbsmJFktm/fboz5v9GcH374wanf1UZk5s2bZwICAkxGRsYV9wegaIzIADeIhIQEHTp0SJ9//rnuuusurVmzRi1atNC0adOc+l06ShIaGipJio6OdmrLyspSRkaGJGnXrl1q166d0zbatWunXbt2ObWNGDFCGzdu1Ndff60//OEPV6330jr8/PwUEBDgmJSbkpKiVq1aOfW/5ZZbrrrNV155RUeOHNGkSZPUuHFjTZo0SVFRUdq+ffsVn7d792498MADqlOnjgICAlS7dm1J0v79+6+6zyvp3LmzatWqpTp16uhPf/qTZs6cqXPnzv2ubQI3GoIMcAOx2+3q3LmzXnjhBX377bfq37+/XnzxRac+3t7ejp9tNluRbfmnVYqrc+fOOnjwoJYuXVqs/pfuM3+/Jd1nYYKDg9WrVy+98cYb2rVrl8LDw/XGG29c8Tldu3bVyZMnNXnyZG3cuFEbN26UJOXk5FzxeR4eHgVOY124cMHxc6VKlbRlyxZ9+umnql69ukaOHKmbb765xFdNATcyggxwA2vUqNHvvmKnYcOGBS7hXrdunRo1auTUdu+992rWrFn685//rNmzZ/+ufTZo0ECbNm1yavv+++9LvJ0KFSqobt26jvegQoUKkqTc3FxHnxMnTiglJUXPP/+8OnXqpIYNG+rUqVMFtnP586TfrsY6fPiw43Fubq527Njh1MfLy0txcXF67bXX9OOPP2rfvn1atWpViV8LcKPi8mvgBnDixAn16tVLjzzyiJo2bapKlSpp06ZNeu2119StW7ffte2//e1v6t27t5o3b664uDgtWrRIn332mVasWFGgb48ePfTxxx/rT3/6k7y8vHTfffdd0z4HDRqkN998U88884wGDhyorVu3Ok6R5Y8YXe6LL77Q7Nmz1adPH9WvX1/GGC1atEhfffWVpk6dKkmqVauWbDabvvjiC919993y9fVVlSpVFBwcrA8//FDVq1fX/v379eyzzzptOyQkRL6+vlqyZIlq1Kghu92uwMBAdezYUYmJifryyy9Vt25dvfnmm06jLV988YV+/vlntW/fXlWqVNFXX32lvLw8NWjQ4JreF+CG5OpJOgDKXlZWlnn22WdNixYtTGBgoKlYsaJp0KCBef755825c+cc/XTZZdKFTWItbEJsSS+/njNnjrHb7WbevHnGmMIn+17a3xhjAgMDzdSpUx2PL7/8+v333zeSzPnz5wt9D/bu3WseffRRU79+fePr62sqV65sWrVq5bRNY4wZM2aMCQsLMzabzTEpd/ny5aZhw4bGx8fHNG3a1KxZs6ZAjZMnTzYRERHGw8PDcfl1Tk6Oefzxx01QUJAJCQkxycnJTpN9v/nmG9OhQwdTpUoV4+vra5o2bWrmzJlTaP0ACmczppDrEAHAYl555RVNmjRJBw4ccHUpAK4jTi0BsKSJEyeqVatWCg4O1rp16/T66687fb8NgBsDQQaAJe3evVsvv/yyTp48qZo1a+rJJ59UUlKSq8sCcJ1xagkAAFgWl18DAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADLIsgAAADL+n9UYtE1rKaJfQAAAABJRU5ErkJggg=="},"metadata":{}}],"execution_count":244},{"cell_type":"code","source":"#Marital Status vs. Number of Dependents\ndf.groupby('Marital Status')['Number of Dependents'].mean().plot(kind='bar', color='skyblue')\nplt.title(\"Average Number of Dependents by Marital Status\")\nplt.ylabel(\"Average Number of Dependents\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:35.947932Z","iopub.execute_input":"2024-12-18T15:36:35.948777Z","iopub.status.idle":"2024-12-18T15:36:36.227573Z","shell.execute_reply.started":"2024-12-18T15:36:35.948708Z","shell.execute_reply":"2024-12-18T15:36:36.22661Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":245},{"cell_type":"code","source":"#Exercise Frequency vs. Health Score\nsns.boxplot(x='Exercise Frequency', y='Health Score', data=df)\nplt.title(\"Health Score by Exercise Frequency\")\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:36.229147Z","iopub.execute_input":"2024-12-18T15:36:36.229566Z","iopub.status.idle":"2024-12-18T15:36:36.929075Z","shell.execute_reply.started":"2024-12-18T15:36:36.229522Z","shell.execute_reply":"2024-12-18T15:36:36.928098Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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"},"metadata":{}}],"execution_count":246},{"cell_type":"code","source":"# Location vs. Premium Amount\nsns.boxplot(x='Location', y='Premium Amount', data=df)\nplt.title(\"Premium Amount by Location\")\nplt.xticks(rotation=45)\nplt.show()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:36.930625Z","iopub.execute_input":"2024-12-18T15:36:36.931096Z","iopub.status.idle":"2024-12-18T15:36:37.708095Z","shell.execute_reply.started":"2024-12-18T15:36:36.931052Z","shell.execute_reply":"2024-12-18T15:36:37.707088Z"}},"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 640x480 with 1 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92L27NmYOnUq9PT04Ofnh6VLlxY4duDAgZg8ebLGB1MnJSUV+Dm0bdsW/fv3h7+/P2xsbLBmzRqMHz8eVlZWGDFiBBYsWAB9fX1xvKenJ06fPo2AgAAsX74cKpUK1apVw/Dhw8Uxu3fvxujRo7F27VoIgoAOHTrg2LFjcHBwUJt3kyZNMG/ePGzYsAHHjx+HSqVCbGxsgYFIk8+V6EMpBB5lRkRUJgQFBWH8+PF4+PBhvjOpiKhoGIiIiMoAQRBQv359WFtb57t2FREVHXeZERGVYmlpafjf//6H06dPIzo6usB7wBFR0XELERFRKfbw4UM4OzvD0tJSvI8XERU/BiIiIiKSPZ6rSERERLLHQERERESyx4OqC0GlUuHRo0cwMzP74MvzExERkXYJgoBXr17BwcHhXy/gyUBUCI8ePXrnlXaJiIiodIuPj4ejo+N7xzAQFYKZmRmAN2+oubm5xNUQERFRYaSkpKBSpUri9/j7MBAVQt5uMnNzcwYiIiKiMqYwh7vwoGoiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9XqmatKpdu3bIysqCgYEBTp48KXU5RPDw8BB/Pnv2rISVEP2tVatWUKlU0NHRQXh4uNTlyIKkW4j8/f2hUCjUHjVr1hT7MzIy4OvrC2tra5iamsLb2xuJiYlq04iLi4OXlxeMjY1ha2uLSZMmIScnR21MeHg4GjZsCKVSCRcXFwQHB2tj8egfzp49i6ysLABAVlYWv3xIcj/++ON7nxNJ4dixY1CpVAAAlUqFY8eOSVyRPEi+y6x27dp4/Pix+Dh//rzYN378eBw+fBj79+/HmTNn8OjRI/Ts2VPsz83NhZeXF7KyshAREYHt27cjODgYs2fPFsfExsbCy8sLrVu3RlRUFMaNG4dhw4YhNDRUq8tJwMyZM9/7nEjbgoKC3vucSAoLFy5873MqGQpBEASpZu7v74+ffvoJUVFR+fqSk5NhY2OD3bt3o1evXgCAO3fuoFatWoiMjETz5s1x7NgxdOnSBY8ePYKdnR0AYMOGDZgyZQqePHkCAwMDTJkyBSEhIbh586Y47b59++Lly5c4fvx4gXVlZmYiMzNTfJ53t9zk5GTe3PUD+fn54caNG/na69WrhzVr1khQEcld7969kZCQkK/d3t4e+/btk6AiImDQoEGIjY3N1+7s7Izt27dLUFHZlpKSAgsLi0J9f0u+heju3btwcHBA1apV0b9/f8TFxQEArly5guzsbLRr104cW7NmTVSuXBmRkZEAgMjISNStW1cMQwDg6emJlJQU3Lp1Sxzz9jTyxuRNoyALFy6EhYWF+KhUqVKxLa8cpaenFxiGAODGjRtIT0/XckUkd69evSowDAFAQkICXr16peWKiIC0tLQCwxDwZm9HWlqaliuSF0kDUbNmzRAcHIzjx49j/fr1iI2NxX/+8x/xl5WBgQEsLS3VXmNnZyf+IktISFALQ3n9eX3vG5OSkvLOL+Jp06YhOTlZfMTHxxfH4sqWp6dnkfqJipuXl1eR+olKQqdOnYrUT0Uj6Vlmb3+49erVQ7NmzVClShXs27cPRkZGktWlVCqhVColmz8RERFpl+S7zN5maWmJTz75BPfu3YO9vT2ysrLw8uVLtTGJiYmwt7cH8GZf/z/POst7/m9jzM3NJQ1dREREVHqUqkCUmpqK+/fvo0KFCmjUqBH09fURFhYm9sfExCAuLg7u7u4AAHd3d0RHRyMpKUkcc+LECZibm8PV1VUc8/Y08sbkTYOIiIhI0kA0ceJEnDlzBg8fPkRERAQ+//xz6Orq4r///S8sLCzg4+ODCRMm4PTp07hy5QqGDBkCd3d3NG/eHADQoUMHuLq6YsCAAbh+/TpCQ0Mxc+ZM+Pr6iru8Ro4ciQcPHmDy5Mm4c+cO1q1bh3379mH8+PFSLjoRERGVIpIeQ/Tnn3/iv//9L549ewYbGxt89tlnuHjxImxsbAAAK1euhI6ODry9vZGZmQlPT0+sW7dOfL2uri6OHDmCUaNGwd3dHSYmJhg0aBDmzp0rjnF2dkZISAjGjx+PoKAgODo64ttvv+WBvERERCSS9DpEZYUm1zGg/N6+NcK78KrVpE1cJ6k04npZ/MrUdYiIiIiIpMZARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARCXOwsKiSP1ExU1P7/23cfy3fqKSYGBgUKR+KhoGIipxr169KlI/UXHLyckpUj9RScjKyipSPxUNAxEREVEpYGdnV6R+KhoGIipxgiAUqZ+ISA5SUlKK1E9Fw0BEJa5q1apF6icqbiYmJkXqJyoJPLZNWgxEVOLu379fpH6i4pabm1ukfqKS8Pr16yL1U9EwEBGR7GRkZBSpn4g+PgxEREREpQC3XEqLgYhKnJGRUZH6iYqbqalpkfqJSgKPbZMWAxGVuPT09CL1ExW31NTUIvUTlYS0tLQi9VPRMBARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEFGJUygUReonIpIDXV3dIvVT0TAQUYkTBKFI/UREcsB7mUmLgYiIZId/iRPRPzEQEZHs6OvrF6mfiD4+DEREJDsZGRlF6ieijw8DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DEREREckeAxERERHJHgMRERERyR4DERHJjp2dXZH6iejjw0BERLKTmJhYpH4i+vgwEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkeyVmkC0aNEiKBQKjBs3TmzLyMiAr68vrK2tYWpqCm9v73xXkI2Li4OXlxeMjY1ha2uLSZMmIScnR21MeHg4GjZsCKVSCRcXFwQHB2thiYiIiKisKBWB6PLly9i4cSPq1aun1j5+/HgcPnwY+/fvx5kzZ/Do0SP07NlT7M/NzYWXlxeysrIQERGB7du3Izg4GLNnzxbHxMbGwsvLC61bt0ZUVBTGjRuHYcOGITQ0VGvLR0RERKWb5IEoNTUV/fv3x+bNm1GuXDmxPTk5GVu2bMGKFSvQpk0bNGrUCNu2bUNERAQuXrwIAPj5559x+/Zt7Ny5E25ubujUqRPmzZuHtWvXIisrCwCwYcMGODs7Y/ny5ahVqxb8/PzQq1cvrFy5UpLlJSIiotJH8kDk6+sLLy8vtGvXTq39ypUryM7OVmuvWbMmKleujMjISABAZGQk6tatq3Znak9PT6SkpODWrVvimH9O29PTU5xGQTIzM5GSkqL2ICIioo+XnpQz37NnD65evYrLly/n60tISICBgQEsLS3V2u3s7JCQkCCOeTsM5fXn9b1vTEpKCtLT02FkZJRv3gsXLkRAQMAHLxcRERGVLZJtIYqPj8fYsWOxa9cuGBoaSlVGgaZNm4bk5GTxER8fL3VJREREVIIkC0RXrlxBUlISGjZsCD09Pejp6eHMmTNYtWoV9PT0YGdnh6ysLLx8+VLtdYmJibC3twcA2Nvb5zvrLO/5v40xNzcvcOsQACiVSpibm6s9iIiI6OMlWSBq27YtoqOjERUVJT4aN26M/v37iz/r6+sjLCxMfE1MTAzi4uLg7u4OAHB3d0d0dDSSkpLEMSdOnIC5uTlcXV3FMW9PI29M3jSIiIiIJDuGyMzMDHXq1FFrMzExgbW1tdju4+ODCRMmwMrKCubm5hg9ejTc3d3RvHlzAECHDh3g6uqKAQMGYMmSJUhISMDMmTPh6+sLpVIJABg5ciTWrFmDyZMnY+jQoTh16hT27duHkJAQ7S4wERERlVqSHlT9b1auXAkdHR14e3sjMzMTnp6eWLdundivq6uLI0eOYNSoUXB3d4eJiQkGDRqEuXPnimOcnZ0REhKC8ePHIygoCI6Ojvj222/h6ekpxSIRERFRKaQQBEGQuojSLiUlBRYWFkhOTubxRB/Aw8PjX8ecPXtWC5UQvcF1kkojrpfFT5Pvb8mvQ0REREQkNQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPY0D0dmzZ5GTk5OvPScnB2fPni2WooiIiIi0SeNA1Lp1azx//jxfe3JyMlq3bl0sRRERERFpk8aBSBAEKBSKfO3Pnj2DiYlJsRRFREREpE16hR3Ys2dPAIBCocDgwYOhVCrFvtzcXNy4cQOffvpp8VdIREREVMIKHYgsLCwAvNlCZGZmBiMjI7HPwMAAzZs3x/Dhw4u/QiIiIqISVuhAtG3bNgCAk5MTJk6cyN1jRERE9NEodCDKM2fOnJKog4iIiEgyGh9UnZiYiAEDBsDBwQF6enrQ1dVVexARERGVNRpvIRo8eDDi4uIwa9YsVKhQocAzzoiIiIjKEo0D0fnz53Hu3Dm4ubmVQDlERERE2qfxLrNKlSpBEISSqIWIiIhIEhoHosDAQEydOhUPHz4sgXKIiIiItE/jXWZ9+vTB69evUa1aNRgbG0NfX1+tv6DbehARERGVZhoHosDAwBIog4iIiEg6GgeiQYMGlUQdRERERJLROBDFxcW9t79y5cofXAwRERGRFDQORE5OTu+99lBubm6RCiIiIiLSNo0D0bVr19SeZ2dn49q1a1ixYgXmz59fbIURERERaYvGgah+/fr52ho3bgwHBwcsXboUPXv2LJbCiIiIiLRF4+sQvUuNGjVw+fLl4pocERERkdZovIUoJSVF7bkgCHj8+DH8/f1RvXr1YiuMiIiISFs0DkSWlpb5DqoWBAGVKlXCnj17iq0wIiIiIm3ROBCdPn1a7bmOjg5sbGzg4uICPT2NJ0dEREQkOY2PIWrZsqXa4z//+Q9q1qz5QWFo/fr1qFevHszNzWFubg53d3ccO3ZM7M/IyICvry+sra1hamoKb29vJCYmqk0jLi4OXl5eMDY2hq2tLSZNmoScnBy1MeHh4WjYsCGUSiVcXFwQHBysca1ERET08fqgTTr3799HYGAgfvvtNwCAq6srxo4di2rVqmk0HUdHRyxatAjVq1eHIAjYvn07unfvjmvXrqF27doYP348QkJCsH//flhYWMDPzw89e/bEhQsXALy55pGXlxfs7e0RERGBx48fY+DAgdDX18eCBQsAALGxsfDy8sLIkSOxa9cuhIWFYdiwYahQoQI8PT0/ZPHLJEEQkJGRIXUZ75Senq71eRoaGr73mlpEJD/8XZmfXH5XKgRBEDR5QWhoKLp16wY3Nze0aNECAHDhwgVcv34dhw8fRvv27YtUkJWVFZYuXYpevXrBxsYGu3fvRq9evQAAd+7cQa1atRAZGYnmzZvj2LFj6NKlCx49egQ7OzsAwIYNGzBlyhQ8efIEBgYGmDJlCkJCQnDz5k1xHn379sXLly9x/PjxAmvIzMxEZmam+DwlJQWVKlVCcnIyzM3Ni7R8UklPT5dVACyM0NBQGBkZSV2GbEn5xVOY/wuhoaFaqESdXL54SjP+rsyvLP+uTElJgYWFRaG+vzXeQjR16lSMHz8eixYtytc+ZcqUDw5Eubm52L9/P9LS0uDu7o4rV64gOzsb7dq1E8fUrFkTlStXFgNRZGQk6tatK4Yh4M0vulGjRuHWrVto0KABIiMj1aaRN2bcuHHvrGXhwoUICAj4oOUgosLJyMgo1V88UtRWlr94iMo6jQPRb7/9hn379uVrHzp0KAIDAzUuIDo6Gu7u7sjIyICpqSkOHjwIV1dXREVFwcDAAJaWlmrj7ezskJCQAABISEhQC0N5/Xl97xuTkpKC9PT0An/5TJs2DRMmTBCf520hKssMDQ0l+YsXAPbs2YNt27a9s3/IkCHo27evFit6w9DQUOvzJKLSTcrflSEhIVi1atU7+8eMGQMvLy8tVvSGXH5XahyIbGxsEBUVle+aQ1FRUbC1tdW4gBo1aiAqKgrJycn44YcfMGjQIJw5c0bj6RQnpVIJpVIpaQ3FTaFQSPaX55AhQ/41EJH8SPnFA7x/C5BUdcnli6c0k/J3Za9evd4biPIOH6GSoXEgGj58OEaMGIEHDx7g008/BfDmGKLFixerbVUpLAMDA7i4uAAAGjVqhMuXLyMoKAh9+vRBVlYWXr58qbaVKDExEfb29gAAe3t7XLp0SW16eWehvT3mn2emJSYmwtzcnJumtejs2bPw8PAosJ3kScovHoDrJJVOXC+lo3EgmjVrFszMzLB8+XJMmzYNAODg4AB/f3+MGTOmyAWpVCpkZmaiUaNG0NfXR1hYGLy9vQEAMTExiIuLg7u7OwDA3d0d8+fPR1JSkrh16sSJEzA3N4erq6s45ujRo2rzOHHihDgNIiIiIo3PMnvbq1evAABmZmYf9Ppp06ahU6dOqFy5Ml69eoXdu3dj8eLFCA0NRfv27TFq1CgcPXoUwcHBMDc3x+jRowEAERERAN4ciO3m5gYHBwcsWbIECQkJGDBgAIYNG6Z22n2dOnXg6+uLoUOH4tSpUxgzZgxCQkIKfdCkJkep07v98+wN/sVDUuM6SaUR18viU6Jnmb3tQ4NQnqSkJAwcOBCPHz+GhYUF6tWrJ4YhAFi5ciV0dHTg7e2NzMxMeHp6Yt26deLrdXV1ceTIEYwaNQru7u4wMTHBoEGDMHfuXHGMs7MzQkJCMH78eAQFBcHR0RHffvttqT67RQ6kPHaEqCBcJ6k04nqpPRoHomfPnmH27Nk4ffo0kpKSoFKp1PqfP39e6Glt2bLlvf2GhoZYu3Yt1q5d+84xVapUybdL7J9atWqFa9euFbouIiIikheNA9GAAQNw7949+Pj4wM7OjhcRIyIiojJP40B07tw5nD9/HvXr1y+JeoiIiIi0TuObu9asWVOSe6kQERERlRSNA9G6deswY8YMnDlzBs+ePUNKSorag4iIiKis0XiXmaWlJVJSUtCmTRu1dkEQoFAokJubW2zFEREREWmDxoGof//+0NfXx+7du3lQNREREX0UNA5EN2/exLVr11CjRo2SqIeIiIhI6zQ+hqhx48aIj48viVqIiIiIJKHxFqLRo0dj7NixmDRpEurWrQt9fX21/nr16hVbcURERETaoHEg6tOnDwBg6NChYptCoeBB1URERFRmaRyIYmNjS6IOIiIiIsloHIiqVKlSYLtKpcLRo0ff2U9ERERUWhXpbvcAcO/ePWzduhXBwcF48uQJsrOzi6MuIiIiIq3R+CwzAEhPT8eOHTvg4eGBGjVqICIiArNnz8aff/5Z3PURERERlTiNthBdvnwZ3377Lfbs2YNq1aqhf//+iIiIwLp16+Dq6lpSNRIRERGVqEIHonr16iElJQX9+vVDREQEateuDQCYOnVqiRVHREREpA2F3mUWExMDDw8PtG7dmluDiIiI6KNS6ED04MED1KhRA6NGjYKjoyMmTpyIa9eu8V5mREREVOYVOhBVrFgRM2bMwL179/Ddd98hISEBLVq0QE5ODoKDg/H777+XZJ1EREREJeaDzjJr06YNdu7cicePH2PNmjU4deoUatasydt2EBERUZn0QYEoj4WFBb7++mv8+uuvuHr1Klq1alVMZRERERFpT5EC0dvc3NywatWq4pocERERkdYUWyAiIiIiKqsYiIiIiEj2GIiIiIhI9hiIiIiISPY+6G73ly9fxunTp5GUlASVSqXWt2LFimIpjIiIiEhbNA5ECxYswMyZM1GjRg3Y2dmpXamaV60mIiKiskjjQBQUFIStW7di8ODBJVAOERERkfZpfAyRjo4OWrRoURK1EBEREUlC40A0fvx4rF27tiRqISIiIpKExrvMJk6cCC8vL1SrVg2urq7Q19dX6z9w4ECxFUdERESkDRoHojFjxuD06dNo3bo1rK2teSA1ERERlXkaB6Lt27fjxx9/hJeXV0nUQ0RERKR1Gh9DZGVlhWrVqpVELURERESS0DgQ+fv7Y86cOXj9+nVJ1ENERESkdRrvMlu1ahXu378POzs7ODk55Tuo+urVq8VWHBEREZE2aByIevToUQJlEBEREUlH40A0Z86ckqiDiIiISDK82z0RERHJnsZbiHR0dN577aHc3NwiFURERESkbRoHooMHD6o9z87OxrVr17B9+3YEBAQUW2FERERE2qJxIOrevXu+tl69eqF27drYu3cvfHx8iqUwIiIiIm0ptmOImjdvjrCwsOKaHBEREZHWFEsgSk9Px6pVq1CxYsXimBwRERGRVmm8y6xcuXJqB1ULgoBXr17B2NgYO3fuLNbiiIiIiLRB40C0cuVKtUCko6MDGxsbNGvWDOXKlSvW4oiIiIi0QeNANHjw4BIog4iIiEg6hQpEN27cQJ06daCjo4MbN268d2y9evWKpTAiIiIibSlUIHJzc0NCQgJsbW3h5uYGhUIBQRDyjVMoFLwwIxEREZU5hQpEsbGxsLGxEX8mIiIi+pgUKhBVqVKlwJ+JiIiIPgYaH1QNAI8ePcL58+eRlJQElUql1jdmzJhiKYyIiIhIWzQORMHBwfjqq69gYGAAa2trtVPwFQoFAxERERGVORoHolmzZmH27NmYNm0adHSK7c4fRERERJLRONG8fv0affv2ZRgiIiKij4bGqcbHxwf79+8vlpkvXLgQTZo0gZmZGWxtbdGjRw/ExMSojcnIyICvry+sra1hamoKb29vJCYmqo2Ji4uDl5cXjI2NYWtri0mTJiEnJ0dtTHh4OBo2bAilUgkXFxcEBwcXyzIQERFR2afxLrOFCxeiS5cuOH78OOrWrQt9fX21/hUrVhR6WmfOnIGvry+aNGmCnJwcTJ8+HR06dMDt27dhYmICABg/fjxCQkKwf/9+WFhYwM/PDz179sSFCxcAALm5ufDy8oK9vT0iIiLw+PFjDBw4EPr6+liwYAGAN5cK8PLywsiRI7Fr1y6EhYVh2LBhqFChAjw9PTV9C4iIiOgj80GBKDQ0FDVq1ACAfAdVa+L48eNqz4ODg2Fra4srV67Aw8MDycnJ2LJlC3bv3o02bdoAALZt24ZatWrh4sWLaN68OX7++Wfcvn0bJ0+ehJ2dHdzc3DBv3jxMmTIF/v7+MDAwwIYNG+Ds7Izly5cDAGrVqoXz589j5cqVDERERESkeSBavnw5tm7dWiL3NEtOTgYAWFlZAQCuXLmC7OxstGvXThxTs2ZNVK5cGZGRkWjevDkiIyNRt25d2NnZiWM8PT0xatQo3Lp1Cw0aNEBkZKTaNPLGjBs3rsA6MjMzkZmZKT5PSUkprkUkIiKiUkjjY4iUSiVatGhR7IWoVCqMGzcOLVq0QJ06dQAACQkJMDAwgKWlpdpYOzs7JCQkiGPeDkN5/Xl97xuTkpKC9PT0fLUsXLgQFhYW4qNSpUrFsoxERERUOmkciMaOHYvVq1cXeyG+vr64efMm9uzZU+zT1tS0adOQnJwsPuLj46UuiYiIiEqQxrvMLl26hFOnTuHIkSOoXbt2voOqDxw4oHERfn5+OHLkCM6ePQtHR0ex3d7eHllZWXj58qXaVqLExETY29uLYy5duqQ2vbyz0N4e888z0xITE2Fubg4jI6N89SiVSiiVSo2Xg4iIiMomjbcQWVpaomfPnmjZsiXKly+vtmvJwsJCo2kJggA/Pz8cPHgQp06dgrOzs1p/o0aNoK+vj7CwMLEtJiYGcXFxcHd3BwC4u7sjOjoaSUlJ4pgTJ07A3Nwcrq6u4pi3p5E3Jm8aREREJG8abyHatm1bsc3c19cXu3fvxqFDh2BmZiYe82NhYQEjIyNYWFjAx8cHEyZMgJWVFczNzTF69Gi4u7ujefPmAIAOHTrA1dUVAwYMwJIlS5CQkICZM2fC19dX3MozcuRIrFmzBpMnT8bQoUNx6tQp7Nu3DyEhIcW2LERERFR2fdDlpnNycnDy5Els3LgRr169AvDmhq+pqakaTWf9+vVITk5Gq1atUKFCBfGxd+9ecczKlSvRpUsXeHt7w8PDA/b29mq75XR1dXHkyBHo6urC3d0dX375JQYOHIi5c+eKY5ydnRESEoITJ06gfv36WL58Ob799lueck9EREQAPmAL0R9//IGOHTsiLi4OmZmZaN++PczMzLB48WJkZmZiw4YNhZ6WIAj/OsbQ0BBr167F2rVr3zmmSpUqOHr06Hun06pVK1y7dq3QtREREZF8fNBZZo0bN8aLFy/UDkj+/PPP8x2nQ0RERFQWaLyF6Ny5c4iIiICBgYFau5OTE/76669iK4yIiIhIWzTeQqRSqZCbm5uv/c8//4SZmVmxFEVERESkTRoHog4dOiAwMFB8rlAokJqaijlz5qBz587FWRsRERGRVmi8y2zZsmXo2LEjXF1dkZGRgX79+uHu3bsoX748vv/++5KokYiIiKhEaRyIKlWqhOvXr2Pv3r24fv06UlNT4ePjg/79+xd41WciIiKi0k6jQJSdnY2aNWviyJEj6N+/P/r3719SdRERERFpjUbHEOnr6yMjI6OkaiEiIiKShMYHVfv6+mLx4sXIyckpiXqIiIiItE7jY4guX76MsLAw/Pzzz6hbty5MTEzU+j/kbvdEREREUtI4EFlaWsLb27skaiEiIiKShKR3uyciIiIqDQp9DJFKpcLixYvRokULNGnSBFOnTkV6enpJ1kZERESkFYUORPPnz8f06dNhamqKihUrIigoCL6+viVZGxEREZFWFDoQ7dixA+vWrUNoaCh++uknHD58GLt27YJKpSrJ+oiIiIhKXKEDUVxcnNq9ytq1aweFQoFHjx6VSGFERERE2lLoQJSTkwNDQ0O1Nn19fWRnZxd7UURERETaVOizzARBwODBg6FUKsW2jIwMjBw5Uu1aRLwOEREREZU1hQ5EgwYNytf25ZdfFmsxRERERFIodCDi9YeIiIjoY6XxvcyIiIiIPjYMRERERCR7DEREREQkewxEREREJHsa39yVNCcIAjIyMqQuQ3Jvvwd8P94wNDSEQqHQ+ny5Tr7BdbJgXC+lxfUyP22skwpBEIQSncNHICUlBRYWFkhOToa5ubnGr09PT4enp2cJVEZlXWhoKIyMjLQ+X66T9D5cL6m0+dB1UpPvb+4yIyIiItnjLjMtS2vYH9CR6dsuCIAq583POnqABJvkSwVVDkyu7pK6CtFaj5dQ6spzQ7EgAFn/d39qAx35rpIAkJmrgO9ZS6nLEOV2zZXvN5QAIPf/ftYFINf1MgfQPayrtdnJdXWTjo4eoKsvdRUSMpC6APoHpa4AQ+39zil1tL9jqLQqZaFYD/L+hpLz14REuMuMiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkT9JAdPbsWXTt2hUODg5QKBT46aef1PoFQcDs2bNRoUIFGBkZoV27drh7967amOfPn6N///4wNzeHpaUlfHx8kJqaqjbmxo0b+M9//gNDQ0NUqlQJS5YsKelFIyIiojJE0kCUlpaG+vXrY+3atQX2L1myBKtWrcKGDRvwyy+/wMTEBJ6ensjIyBDH9O/fH7du3cKJEydw5MgRnD17FiNGjBD7U1JS0KFDB1SpUgVXrlzB0qVL4e/vj02bNpX48hEREVHZoCflzDt16oROnToV2CcIAgIDAzFz5kx0794dALBjxw7Y2dnhp59+Qt++ffHbb7/h+PHjuHz5Mho3bgwAWL16NTp37oxly5bBwcEBu3btQlZWFrZu3QoDAwPUrl0bUVFRWLFihVpwIiIiIvkqtccQxcbGIiEhAe3atRPbLCws0KxZM0RGRgIAIiMjYWlpKYYhAGjXrh10dHTwyy+/iGM8PDxgYGAgjvH09ERMTAxevHhR4LwzMzORkpKi9iAiIqKPV6kNRAkJCQAAOzs7tXY7OzuxLyEhAba2tmr9enp6sLKyUhtT0DTensc/LVy4EBYWFuKjUqVKRV8gIiIiKrVKbSCS0rRp05CcnCw+4uPjpS6JiIiISlCpDUT29vYAgMTERLX2xMREsc/e3h5JSUlq/Tk5OXj+/LnamIKm8fY8/kmpVMLc3FztQURERB+vUhuInJ2dYW9vj7CwMLEtJSUFv/zyC9zd3QEA7u7uePnyJa5cuSKOOXXqFFQqFZo1ayaOOXv2LLKzs8UxJ06cQI0aNVCuXDktLQ0RERGVZpIGotTUVERFRSEqKgrAmwOpo6KiEBcXB4VCgXHjxuGbb77B//73P0RHR2PgwIFwcHBAjx49AAC1atVCx44dMXz4cFy6dAkXLlyAn58f+vbtCwcHBwBAv379YGBgAB8fH9y6dQt79+5FUFAQJkyYINFSExERUWkj6Wn3v/76K1q3bi0+zwspgwYNQnBwMCZPnoy0tDSMGDECL1++xGeffYbjx4/D0NBQfM2uXbvg5+eHtm3bQkdHB97e3li1apXYb2FhgZ9//hm+vr5o1KgRypcvj9mzZ/OUeyIiIhJJGohatWoFQRDe2a9QKDB37lzMnTv3nWOsrKywe/fu986nXr16OHfu3AfXSURERB83SQORXKiFvtzsdw8keXhrHXjfHwQl6e35ZuZKUgKVMm+vB6VhvUSOJCVQafLWOqCNdZKBSAsyMzPFn02uvX9rFslLZmYmjI2NJZlvHt+zPLmA1JWG9VL3sK7W50+llzbWyVJ7lhkRERGRtnALkRYolUrx57QG/QBdfQmrIcnlZotbCt9eN7Tp7fmu9XgBJf8Yl73M3L+3FpaG9TK3ay6/oeQu5+8thdpYJ7m6aYFCofj7ia4+AxGJ1NYNiear1AUMGYjoLaVhvYQe+A1FIm2sk9xlRkRERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARERERLKnJ3UBsqPKkboC6QjC38uvowcoFNLWI5VStg5k5ioACFKXIQlBALJUb3420JHvKgnkrQelSOn6b6JdAoDc//tZF0Ap+2i0RsvrAAORlplc3SV1CURqfM9aSl0CUT66h3WlLoFkhrvMiIiISPYUgiDIc1u5BlJSUmBhYYHk5GSYm5tr/HpBEJCRkVEClZUtGRkZ6N69OwDg0KFDMDQ0lLgi6RkaGkIhwX4arpNvcJ0sGNdLaXG9zO9D10lNvr+5y0wLFAoFjIyMpC6jVDE0NOR7IiGuk/lxnZQe18v8uF5qD3eZERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkewxEBEREZHsMRARERGR7DEQERERkezJKhCtXbsWTk5OMDQ0RLNmzXDp0iWpSyIiIqJSQE/qArRl7969mDBhAjZs2IBmzZohMDAQnp6eiImJga2trdTllThBEJCRkSFpDW/PX+paAMDQ0BAKhULqMmSL62R+XCelx/UyP7mslwpBEASpi9CGZs2aoUmTJlizZg0AQKVSoVKlShg9ejSmTp363tempKTAwsICycnJMDc310a5xS49PR2enp5Sl1GqhIaGwsjISOoyZIvrZH5cJ6XH9TK/srxeavL9LYtdZllZWbhy5QratWsntuno6KBdu3aIjIzMNz4zMxMpKSlqDyIiIvp4yWKX2dOnT5Gbmws7Ozu1djs7O9y5cyff+IULFyIgIEBb5WmFoaEhQkNDJa1BEARkZmYCAJRKpeSbYA0NDSWdv9xxncyP66T0uF7mJ5f1UhaBSFPTpk3DhAkTxOcpKSmoVKmShBUVnUKhKBWbPI2NjaUugUoJrpNUGnG9lC9ZBKLy5ctDV1cXiYmJau2JiYmwt7fPN16pVEKpVGqrPCIiIpKYLI4hMjAwQKNGjRAWFia2qVQqhIWFwd3dXcLKiIiIqDSQxRYiAJgwYQIGDRqExo0bo2nTpggMDERaWhqGDBkidWlEREQkMdkEoj59+uDJkyeYPXs2EhIS4ObmhuPHj+c70JqIiIjkRzbXISqKj+E6RERERHLD6xARERERaYCBiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZE82F2YsirxLNaWkpEhcCRERERVW3vd2YS65yEBUCK9evQKAMn/HeyIiIjl69eoVLCws3juGV6ouBJVKhUePHsHMzAwKhULqcsq0lJQUVKpUCfHx8bzqN5UKXCepNOJ6WTwEQcCrV6/g4OAAHZ33HyXELUSFoKOjA0dHR6nL+KiYm5vzPzmVKlwnqTTiell0/7ZlKA8PqiYiIiLZYyAiIiIi2WMgIq1SKpWYM2cOlEql1KUQAeA6SaUT10vt40HVREREJHvcQkRERESyx0BEREREssdARERERLLHQERERESyx0BEREREssdARET0L3gyLtHHj4GIiOgdDh48iN9++w0KhYKhiOgjx+sQEREVIDo6GgMGDEDVqlWxePFiVK9eHYIg8AbPRB8pbiGiUiUvn6enpxfYTqQtdevWxdixY5GcnIzp06fj999/55YiKlPy1tWYmBjcvHkTt2/fztdHf2MgolIj76/vkJAQdO3aFT179kRAQAAA8IuItEqlUgEAhgwZgoEDB+LJkyeYMWMGQxGVKQqFAj/88ANatmyJzp07o3v37ggMDBT7uB6rYyCiUkOhUOD8+fPo2bMnatasCUNDQ2zZsgW9evUS+/kfmLRBR0cHubm5AIBBgwZh8ODBDEVUZuStm8+fP8eMGTOwcOFCbN26FYMHD8akSZMwf/58APyd+k88hohKjZiYGNy/fx8xMTEYP348Xr9+jdOnT2PQoEHw8PDAgQMHALz5611Hh1metGvr1q3YsWMHbGxsMH/+fHzyySc8pohKrbCwMISHhyMlJQXLli2Dvr4+Xr16ha1bt2LChAmYO3cuZsyYAQBcj/8Pv1WoVPjrr7/g4eGB3r17i2HH2NgYHTp0wPbt23H27Fl88cUXAMAwRCUm7+/DK1euYNOmTdi+fTt++eUXAMDQoUPVdp/dvXuXf2FTqZSZmYmTJ09iwYIFiIyMhL6+PgDAzMwMPj4+WLFiBebNmycGIoahN/jNQqWCqakp5syZAysrK1y8eFFs19fXR4cOHfDdd9/hxx9/xIABAySskj5meX8lHzhwAB07dsSuXbuwYsUKTJw4EVu3bgXwdyh68eIFRo8ejfv37/PLhEqNvHCuVCoxYsQIzJo1C7/++is2b94sjjE1NYWPjw8CAgKwYcMGPH36lKE+j0BUSjx79kzYtGmTYGFhIfj5+an1ZWVlCaGhoUJMTIxE1ZEchIeHC/b29sL69esFQRCEU6dOCebm5kLlypWFoKAgcdzatWuFLl26CH/++adUpRKJVCqVIAiCkJ2drdb+xx9/CFOmTBFMTU2Fb7/9Vq0vNTVVeP78udZqLAt4DBFpnfB/f4lHR0fj4cOHUKlUaNu2LUxNTfHixQv88MMPmDFjBvr06YPVq1dLXS7JyOzZs/HkyROsX78ecXFxaNWqFZo0aQITExOEhoZi3rx5GDp0KAAgOTkZFhYWEldMcpf3+/TUqVPYuXMnsrOzUblyZfHA6fj4eKxbtw5r165FUFAQhgwZInHFpRcDEWlV3n/egwcPYsKECVAqlTA2NkZWVhZOnDiBChUqiKFozpw58PT0xLZt26Qumz5SeevjuXPnYGpqCicnJ/z2229wc3NDmzZtULt2bWzZsgWRkZHo0KEDBEHA4sWL4evrK3XpRKKDBw9i8ODB6N27N8qVK4d9+/ahUaNG+OGHH6BQKBAfH4+NGzdiwYIF2L59Ow89eBfJtk2RbIWFhQmWlpbCpk2bxOcKhUKoWrWqcO/ePUEQBOH58+fCqlWrBBcXFyEhIUHKcukjd/LkScHExEQ4ePCg2Hb+/HnBzc1N+P333wVBEITo6Gihc+fOQkBAgPDgwQOJKiXK7/r160L16tXF3byxsbFChQoVBIVCIbRs2VLIyckR2wMCAoQ7d+5IWW6pxi1EpFWpqamYMWMGbG1tMWPGDDx69Aju7u7w8PBAbGws/vzzT5w5cwZVqlTBy5cvAQCWlpaS1kwfr8TERGzevBl6enqYOnWq2H7u3Dl069YNmzZtwhdffIEZM2bg3r172LBhA8qVKydhxUTqQkJCEBYWhhUrViA+Ph6tWrVC27Zt0bt3b/To0QMdO3bE3r17oauri5ycHOjp6UldcqnFQERad+zYMdjY2MDFxQXt2rVD48aNsWHDBhw8eBDe3t6wtLTElStX4OzsLHWp9JFSqVS4e/cuXF1dYW9vjxkzZuDrr78W++Pj4zF9+nScPHkSdnZ2iI2NxdmzZ1G/fn0JqyYq2LVr1+Dm5obPP/8cZmZm2LFjB9LS0tCqVStcvXoVHTt2xNGjR6Uus9RjVKQSk5ubCx0dnXynJXfq1AkAEBoaCgMDA0yZMgUAYG1tja5du0KpVCI7O1vr9ZJ86OjooEaNGpg+fTrmz5+PGzdu4OXLl+LWyEqVKmHatGno3Lkz4uPj0bNnT7i4uEhbNMmaIAgQBAE6OjpIS0uDvr4+9PX1oVAo0KBBAzx58gTx8fGYM2cOFAoF9PT00KBBAwQEBMDV1VXq8ssEBiIqdk+fPkX58uWhq6sLALh48SJ+/fVXmJqaolatWmjWrBkAIDY2FleuXIGdnR0A4Oeff4a5uTk2b94MQ0NDyeqnj9uVK1fw6NEjdO3aFfPmzYNCocA333yD+vXrY9CgQTA2NgYAuLq68ouEJHfhwgVUqVIFjo6OUCgUOHToEDZt2oQ///wT7dq1Q+vWrdGlSxcYGxvjyZMn2LNnD9zc3LBmzRqcO3cO8+bNg729vdSLUTZIeQATfXxWr14t9O3bV4iOjhYEQRAOHTok6OnpCZ9++qlgY2Mj1KlTR5g4caIgCIKQnJws1K1bVyhXrpzQpk0bwcTERLh+/bqU5dNHTKVSCampqUKTJk0EDw8PISQkROybOnWqoKenJ6xfv15IS0uTsEqiv50/f14wMjISAgIChOTkZOGXX34RjIyMhMmTJwujR48WunXrJlSuXFnYvHmzIAiCcODAAcHa2lqoXLmy4OjoKFy9elXiJShbuIWIipWdnR3Cw8NhZmaGwYMHY/369Vi9ejVGjhyJuLg47N+/H2vWrIGenh4WLlyIgwcPYtu2bVAoFFi7di1q1qwp9SLQR0qhUMDExARbt27FqFGjsGbNGqhUKnTp0gULFy4EAEyYMAEZGRkYMWKEuKWISCotWrTA1KlTsXXrVhgZGeHFixeYOnUqZs+eDQC4f/8+tmzZgm+++QZVq1bF559/Dnd3d9y/fx/VqlXjliEN8aBqKjZ5N109dOgQRo8eDU9PTzx8+BAbNmxAtWrVAADPnj3Dli1bsHv3buzevVvcJcEbtlJJEP7vOkOvXr2CmZmZ2H779m34+PigXLly8PPzQ+fOnQEAo0ePxp49e3D37l2e3UiSevuMsKVLl2LVqlXQ09ODj48PZs6cKY67d+8efH190bRpU8ybN0+qcj8K/AaiYpOXrbt37459+/bhyJEjCAsLw2+//SaOsba2Ro8ePfDgwQPcunVLbGcYouKgUqnUnudddHHIkCG4dOmS2O7q6opvv/0WcXFxWLBgAY4fPw4AWL16NW7dusUwRJLLOwYzLCwMo0aNwowZMxAfH48LFy4gLi5OHOfi4gIHBwecOXMm3/pPmuG3EBUbXV1d7N27F3Z2dqhXrx6+++472NvbIzg4GFFRUeK4ypUrw8XFBRkZGdIVSx+dvK2M8fHx2LJlCzZv3oxff/0Vtra2OHv2LAIDA3HlyhVxfO3atbFhwwZERUVh0aJFOHbsGADAxsZGqkUgEikUCly4cAHt27fH//73P4wcORKLFi3CtWvXsHXrVrVQlJ2dDQcHB+Tk5EhYcdnHY4jog7y9iys7Oxv6+vr466+/8OOPP8Lf3x9GRkZo164dNm7ciK+//hr+/v748ssv4eLign379uHu3bv47LPPJF4K+ljkrY83btxAt27dYGdnh/v378PMzAzff/89wsLC0KNHDyxbtgwTJ05Eo0aNxNc1adIEenp6qFu3LgDw7vVUKty9exfh4eFYvHgx+vXrBwCYOHEiMjMzsWrVKpw9exbNmjVDWloaQkJCEB4eDgMDA4mrLuOkPaabyrK4uDjx58jISOGLL74QWrduLcTFxQlZWVli3+HDh8VLyXfo0EHo0aOHEBUVJUXJ9BHKzc0VBOHNLQyMjY2FqVOnCmlpacKJEyeEChUqCJ06dRIE4c2d7KtWrSr07t1bOH78uJCdnS3Mnj1bmDlzppCamirlIhCpuXPnjuDh4SFUrFhRPIMsIyND7F+2bJmgVCoFFxcX4ZtvvhFvMUNFw0BEHyQtLU349NNPhfr16wuCIAjbtm0TqlatKlhZWQnx8fGCIAhqoSgsLExQKpXC5MmThRcvXkhQMX3M4uLihPLlywtffPGFWnuTJk0EFxcX4dmzZ4IgCMLNmzeF5s2bC05OToKLi4tgZWUlXLt2TYKKidSpVCrx5ydPnghjxowRbGxsBG9vb7E9MzNT/DkgIEBwc3Pj79NixLPM6IPk5OTg6NGjmDVrFipWrIijR4/ixx9/xIQJE9CwYUOsXbtW3Kedd6ZESEgIatSowSv+UrF7+PAhevfujQoVKmDy5Mlo0aIFFi5ciBkzZqBJkyawtbWFlZUVunTpAhsbGzx9+hQ5OTlo3Lgx10cqNc6dOwddXV18+umnePHiBZYsWYLDhw+jR48e4kVEs7KyxF1jeRfBpeLBQEQayzteIycnBydPnsS4cePg7OyMY8eOYffu3QgKCoKrqysWLlwIe3t73lCQtOLu3bsYM2YMDAwMYGtri0OHDmHdunVo2rQprl69iujoaKxevRpmZmZo1KgR9u3bJ3XJRKKUlBQMGTIEFy5cwE8//YTmzZvj2bNnWLRoEc6dO4f27dtj7ty5UCgU4nGbwv9dVoKKBwMR/au8AJT3n/DttpycHJw4cQLjx48XQ9GOHTuwbt061KlTB3PnzoWDg4PES0By8fvvv8PPz0+8ZcHEiRPV+p89e4bTp0+jfv36qF69ukRVEv3t7VBz8eJFBAUF4fLly9ixYwc+/fRTMRRFRkaiWbNmWLZsGUNQCeFp9/SvdHR08Mcff2Dy5Mm4ceOG2KZSqaCnp4f27dsjMDAQf/zxBwYOHIiBAwfCx8cHkZGRmD9/PnJzcyVeApKLTz75BOvXr4eHhwdOnTqF8+fPi33Z2dmwtrZGr169GIao1EhPTxd/bt68OSZMmIAGDRpg4MCBiIyMhLW1NaZOnYp69erhxo0bePr0qYTVfty4hYgKJTo6Gt27d4enpyf8/PxQu3ZtAH9vKcrIyMC+ffuwePFibNmyBc2bN8fGjRvh6ekJJycnaYsn2cnbfSYIAmbNmoUWLVpIXRJRPleuXIGfnx+2bdumdtuiS5cuYe7cuYiJicG+ffvQoEEDPH/+HDk5ObC1tZWw4o8btxBRodStWxcHDhzApUuXEBgYKF5lWkdHB7m5uTA0NET37t3x9OlTXLx4EQDw1VdfMQyRJKpXr45Vq1ZBX18fEydOFNdJotLk8ePH0NXVxciRI/H777+L7U2bNkW/fv1w//59tG/fHr/88gusrKwYhkoYAxEVmpubG7799ltcvXoVgYGBuH37NoA3V6jOycmBrq4uGjRoIIYgbnwkKVWvXh1Lly6Fo6Mjj2MjyRX0+7BLly6YPn069PX1MWTIEMTExIh9n3zyCTw9PdG3b19YW1trs1TZ4i4z0ti1a9cwbNgw1K9fH6NHj0aDBg2QnZ2N+fPnY8eOHQgPD0flypWlLpMIANROUyaSSm5uLnR1dXH9+nU8ffoUmZmZ4k2Fw8LCsGjRIrx69QpbtmzBJ598gm+++QZxcXFYtWqV2o2JqeQwENEHuX79OkaPHo1nz56hRo0a0NXVxcWLF3H48GG4ublJXR4RkeQWLlyIV69eYcGCBQCA/fv3w8fHB/b29rh//z4+++wzTJw4EV27dkV4eDiWLl2KY8eOoWHDhoiJiUFERIR4SxkqeQxE9MHi4uJw8OBBnDt3Dg0aNMAXX3yBTz75ROqyiIgkp1KpEBQUhP/3//4f5s2bhylTpqBhw4YYO3YsOnXqhOzsbAwbNgwZGRmYOnUqvLy88Pz5c5w4cQKpqalo3bo1qlatKvViyAoDERERUQnIyspCcHAwRo0aha+//hrPnj3DmjVrYGVlBQBISkpC3759oVAoEBYWJnG1xEBERERUjN6+2GJ2dja2bNmCyZMnQ09PD9HR0ahYsaJ4bFtMTAxq1aqF8PBweHh4SFy5vPEsMyIiohLw6tUr6OvrY+jQoQgMDERaWhpWrVoFAOKB/gqFAlWrVoWhoaGUpRIA3mCKiIioGCkUCly6dAm9e/fG3r170axZMwwcOBBZWVnw8/ODSqXCiBEjYGpqih07diA1NZWXhigFuMuMiIiomOXk5KBp06ZIS0vDrl270LhxY+Tm5mLz5s0YN24c9PT00KdPH1y/fh2bNm1Cw4YNpS5Z9rjLjIiIqIhUKpX4syAI0NPTw6VLl2BnZ4fevXvj119/ha6uLoYNG4aNGzfi9evXqFixIs6dO8cwVEpwCxEREZGG8u7jmPcvAERGRsLR0RGVKlUSD6zOzs5G27Zt8ddff2Hv3r1o3LgxMjMzsXv3bjRv3hy1atWSeEkoDwMRERGRBvJC0MOHD/Hzzz+jQYMGqFevHurVqwd9fX0cP34cjo6OYihKTU1F06ZNUa5cOSxbtgzu7u5SLwIVgLvMiIiICikvDEVHR8PT0xPHjx9HUlISlEolTpw4AR0dHXh7eyM+Pl489d7ExARubm6IjIyEr68vMjMzJV4KKggDERERUSHp6Ojgzp07aNmyJXr27Ik1a9bAy8sLAFC5cmUcPXoUaWlp+PzzzxEfHy9uJXJ0dMTFixdx6NAhKJVKiZeCCsJdZkRERIWUkZGBgQMHwtbWFmvWrBHbs7OzkZCQAJVKBYVCgX79+iEhIQFffPEFnj9/jh9++AFRUVGoVKmShNXT+/A6RERERIWkp6eHhIQEtatKh4aG4vjx49iyZQusra1Rt25dnDx5EqNGjcKFCxegUqlw6tQphqFSjoGIiIiokF6/fo0nT57gxo0biImJwYEDB7B9+3bUqVMH33zzDUxNTTFv3jwsWbIE27ZtQ0ZGBlQqFYyNjaUunf4Fd5kRERFp4NSpU/D09ETFihXx/PlzLF26FG3btoWLiwuys7PRpUsX2Nra4rvvvpO6VNIAtxARERFpoE2bNnjw4AGSkpJQpUoVlC9fXuzT1dWFhYUFnJyckLe9Ie9sMyrduIWIiIioGGRlZWHevHnYunUrwsPDUb16dalLIg1wCxEREVER7dy5E5cvX8bevXtx7NgxhqEyiIGIiIioCGJiYrBlyxaUK1cOp0+f5u04yijuMiMiIiqivKtVW1hYSF0KfSAGIiIiIpI93rqDiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIiIiGSPgYiIiIhkj4GIiIiIZI+BiIioEBQKBX766SepyyCiEsJARESlzuDBg9GjRw9J5u3v7w83N7d87Y8fP0anTp20XxARaQXvZUZEVAj29vZSl0BEJYhbiIioTDlz5gyaNm0KpVKJChUqYOrUqcjJyRH7VSoVlixZAhcXFyiVSlSuXBnz588X+6dMmYJPPvkExsbGqFq1KmbNmoXs7GwAQHBwMAICAnD9+nUoFAooFAoEBwcDyL/LLDo6Gm3atIGRkRGsra0xYsQIpKamiv15W7mWLVuGChUqwNraGr6+vuK8iKh04RYiIioz/vrrL3Tu3BmDBw/Gjh07cOfOHQwfPhyGhobw9/cHAEybNg2bN2/GypUr8dlnn+Hx48e4c+eOOA0zMzMEBwfDwcEB0dHRGD58OMzMzDB58mT06dMHN2/exPHjx3Hy5EkAKPBmnWlpafD09IS7uzsuX76MpKQkDBs2DH5+fmKAAoDTp0+jQoUKOH36NO7du4c+ffrAzc0Nw4cPL9H3iYg+gEBEVMoMGjRI6N69e7726dOnCzVq1BBUKpXYtnbtWsHU1FTIzc0VUlJSBKVSKWzevLnQ81q6dKnQqFEj8fmcOXOE+vXr5xsHQDh48KAgCIKwadMmoVy5ckJqaqrYHxISIujo6AgJCQniMlSpUkXIyckRx3zxxRdCnz59Cl0bEWkPtxARUZnx22+/wd3dHQqFQmxr0aIFUlNT8eeffyIhIQGZmZlo27btO6exd+9erFq1Cvfv30dqaipycnJgbm6ucR3169eHiYmJWh0qlQoxMTGws7MDANSuXRu6urrimAoVKiA6OlqjeRGRdvAYIiL6aBgZGb23PzIyEv3790fnzp1x5MgRXLt2DTNmzEBWVlaJ1KOvr6/2XKFQQKVSlci8iKhoGIiIqMyoVasWIiMjIQiC2HbhwgWYmZnB0dER1atXh5GREcLCwgp8fUREBKpUqYIZM2agcePGqF69Ov744w+1MQYGBsjNzf3XOq5fv460tDS1OnR0dFCjRo0iLCERSYWBiIhKpeTkZERFRak9RowYgfj4eIwePRp37tzBoUOHMGfOHEyYMAE6OjowNDTElClTMHnyZOzYsQP379/HxYsXsWXLFgBA9erVERcXhz179uD+/ftYtWoVDh48qDZfJycnxMbGIioqCk+fPkVmZma+2vr37w9DQ0MMGjQIN2/exOnTpzF69GgMGDBA3F1GRGULjyEiolIpPDwcDRo0UGvz8fHB0aNHMWnSJNSvXx9WVlbw8fHBzJkzxTGzZs2Cnp4eZs+ejUePHqFChQoYOXIkAKBbt24YP348/Pz8kJmZCS8vL8yaNUs8Qw0AvL29ceDAAbRu3RovX77Etm3bMHjwYLU6jI2NERoairFjx6JJkyYwNjaGt7c3VqxYUWLvBxGVLIXw9rZnIiIiIhniLjMiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikj0GIiIiIpI9BiIiIiKSPQYiIiIikr3/D7Y5gp4nPby0AAAAAElFTkSuQmCC"},"metadata":{}}],"execution_count":247},{"cell_type":"code","source":"#df.info()\n#test.info()\n#test.head()\ndf['Age Group'].unique()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:37.709372Z","iopub.execute_input":"2024-12-18T15:36:37.70972Z","iopub.status.idle":"2024-12-18T15:36:37.72435Z","shell.execute_reply.started":"2024-12-18T15:36:37.709669Z","shell.execute_reply":"2024-12-18T15:36:37.723448Z"}},"outputs":[{"execution_count":248,"output_type":"execute_result","data":{"text/plain":"['0-25', '26-40', '41-60', '60+']\nCategories (4, object): ['0-25' < '26-40' < '41-60' < '60+']"},"metadata":{}}],"execution_count":248},{"cell_type":"code","source":"#df.info()\ntotal_rows = test.shape[0]\nprint(f\"Total rows: {total_rows}\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:37.725653Z","iopub.execute_input":"2024-12-18T15:36:37.726153Z","iopub.status.idle":"2024-12-18T15:36:37.731588Z","shell.execute_reply.started":"2024-12-18T15:36:37.72611Z","shell.execute_reply":"2024-12-18T15:36:37.730632Z"}},"outputs":[{"name":"stdout","text":"Total rows: 800000\n","output_type":"stream"}],"execution_count":249},{"cell_type":"code","source":"df['Age Group'] = df['Age Group'].astype('object')\nprint(df.dtypes)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:37.733121Z","iopub.execute_input":"2024-12-18T15:36:37.733881Z","iopub.status.idle":"2024-12-18T15:36:37.76488Z","shell.execute_reply.started":"2024-12-18T15:36:37.733834Z","shell.execute_reply":"2024-12-18T15:36:37.763858Z"}},"outputs":[{"name":"stdout","text":"id                        int64\nAge                       int64\nGender                   object\nAnnual Income           float64\nMarital Status           object\nNumber of Dependents    float64\nEducation Level          object\nOccupation               object\nHealth Score            float64\nLocation                 object\nPolicy Type              object\nPrevious Claims         float64\nVehicle Age             float64\nCredit Score            float64\nInsurance Duration      float64\nPolicy Start Date        object\nCustomer Feedback        object\nSmoking Status           object\nExercise Frequency       object\nProperty Type            object\nPremium Amount          float64\nAge Group                object\ndtype: object\n","output_type":"stream"}],"execution_count":250},{"cell_type":"code","source":"test['Age Group'] = test['Age Group'].astype('object')\nprint(test.dtypes)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:37.766365Z","iopub.execute_input":"2024-12-18T15:36:37.766682Z","iopub.status.idle":"2024-12-18T15:36:37.782858Z","shell.execute_reply.started":"2024-12-18T15:36:37.766651Z","shell.execute_reply":"2024-12-18T15:36:37.781792Z"}},"outputs":[{"name":"stdout","text":"id                        int64\nAge                       int64\nGender                   object\nAnnual Income           float64\nMarital Status           object\nNumber of Dependents    float64\nEducation Level          object\nOccupation               object\nHealth Score            float64\nLocation                 object\nPolicy Type              object\nPrevious Claims         float64\nVehicle Age             float64\nCredit Score            float64\nInsurance Duration      float64\nPolicy Start Date        object\nCustomer Feedback        object\nSmoking Status           object\nExercise Frequency       object\nProperty Type            object\nAge Group                object\ndtype: object\n","output_type":"stream"}],"execution_count":251},{"cell_type":"code","source":"categorical_columns = test.select_dtypes(include=['object']).columns.tolist()\n\nfor col in categorical_columns:\n    test[col] = test[col].astype('category')\n\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:37.784001Z","iopub.execute_input":"2024-12-18T15:36:37.784306Z","iopub.status.idle":"2024-12-18T15:36:38.824117Z","shell.execute_reply.started":"2024-12-18T15:36:37.784277Z","shell.execute_reply":"2024-12-18T15:36:38.823031Z"}},"outputs":[],"execution_count":252},{"cell_type":"code","source":"categorical_columns = df.select_dtypes(include=['object']).columns.tolist()\n\nfor col in categorical_columns:\n    df[col] = df[col].astype('category')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:38.825523Z","iopub.execute_input":"2024-12-18T15:36:38.826428Z","iopub.status.idle":"2024-12-18T15:36:40.305389Z","shell.execute_reply.started":"2024-12-18T15:36:38.826381Z","shell.execute_reply":"2024-12-18T15:36:40.30453Z"}},"outputs":[],"execution_count":253},{"cell_type":"code","source":"numerical_columns = df.select_dtypes(include=['int64', 'float64']).columns.tolist()\n\nfor col in numerical_columns:\n    df[col] = df[col].astype('int64')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:40.306863Z","iopub.execute_input":"2024-12-18T15:36:40.307558Z","iopub.status.idle":"2024-12-18T15:36:40.447567Z","shell.execute_reply.started":"2024-12-18T15:36:40.30751Z","shell.execute_reply":"2024-12-18T15:36:40.446763Z"}},"outputs":[],"execution_count":254},{"cell_type":"code","source":"numerical_columns = test.select_dtypes(include=['int64', 'float64']).columns.tolist()\n\nfor col in numerical_columns:\n    test[col] = test[col].astype('int64')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:40.448981Z","iopub.execute_input":"2024-12-18T15:36:40.449679Z","iopub.status.idle":"2024-12-18T15:36:40.519043Z","shell.execute_reply.started":"2024-12-18T15:36:40.449632Z","shell.execute_reply":"2024-12-18T15:36:40.518141Z"}},"outputs":[],"execution_count":255},{"cell_type":"code","source":"test.info()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T15:36:40.543466Z","iopub.execute_input":"2024-12-18T15:36:40.543803Z","iopub.status.idle":"2024-12-18T15:36:40.586806Z","shell.execute_reply.started":"2024-12-18T15:36:40.543773Z","shell.execute_reply":"2024-12-18T15:36:40.585664Z"}},"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nRangeIndex: 800000 entries, 0 to 799999\nData columns (total 21 columns):\n #   Column                Non-Null Count   Dtype   \n---  ------                --------------   -----   \n 0   id                    800000 non-null  int64   \n 1   Age                   800000 non-null  int64   \n 2   Gender                800000 non-null  category\n 3   Annual Income         800000 non-null  int64   \n 4   Marital Status        800000 non-null  category\n 5   Number of Dependents  800000 non-null  int64   \n 6   Education Level       800000 non-null  category\n 7   Occupation            800000 non-null  category\n 8   Health Score          800000 non-null  int64   \n 9   Location              800000 non-null  category\n 10  Policy Type           800000 non-null  category\n 11  Previous Claims       800000 non-null  int64   \n 12  Vehicle Age           800000 non-null  int64   \n 13  Credit Score          800000 non-null  int64   \n 14  Insurance Duration    800000 non-null  int64   \n 15  Policy Start Date     800000 non-null  category\n 16  Customer Feedback     800000 non-null  category\n 17  Smoking Status        800000 non-null  category\n 18  Exercise Frequency    800000 non-null  category\n 19  Property Type         800000 non-null  category\n 20  Age Group             800000 non-null  category\ndtypes: category(12), int64(9)\nmemory usage: 64.9 MB\n","output_type":"stream"}],"execution_count":258},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport xgboost as xgb\nfrom sklearn.model_selection import train_test_split, StratifiedKFold\nfrom sklearn.metrics import mean_squared_error, r2_score\nimport optuna\n\n# Function to calculate RMSLE\ndef rmsle(y_true, y_pred):\n    log_true = np.log1p(y_true)\n    log_pred = np.log1p(y_pred)\n    return np.sqrt(np.mean((log_true - log_pred) ** 2))\n\n# Load the data (replace with actual data loading)\nX = df.drop(columns=['Premium Amount', 'id'])\ny = df['Premium Amount']\ntest_features = test.drop(columns=['id'])\n\n# Convert categorical columns to category dtype (XGBoost supports this natively)\ncat_columns = X.select_dtypes(include='category').columns\nX[cat_columns] = X[cat_columns].apply(lambda col: col.cat.codes)\ntest_features[cat_columns] = test_features[cat_columns].apply(lambda col: col.cat.codes)\n\n# Create DMatrix for faster computation\ndtest = xgb.DMatrix(test_features, enable_categorical=True)\n\n# Define Optuna objective function with adjusted parameters\ndef objective(trial):\n    params = {\n        'objective': 'reg:squarederror',\n        'n_estimators': trial.suggest_int('n_estimators', 200, 1500),  # Increased range for more flexibility\n        'max_depth': trial.suggest_int('max_depth', 5, 15),  # Increased range for deeper trees\n        'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.15),  # Smaller learning rates for better performance\n        'subsample': trial.suggest_float('subsample', 0.6, 1.0),\n        'colsample_bytree': trial.suggest_float('colsample_bytree', 0.6, 1.0),\n        'alpha': trial.suggest_float('alpha', 0.1, 10),  # L1 regularization\n        'lambda': trial.suggest_float('lambda', 0.1, 10),  # L2 regularization\n        'tree_method': 'gpu_hist',  # Use GPU if available\n        'enable_categorical': True,\n        'n_jobs': -1  # Use all available cores\n    }\n\n    # Split data into training and validation\n    X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=42)\n    dtrain_fold = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)\n    dvalid_fold = xgb.DMatrix(X_valid, label=y_valid, enable_categorical=True)\n\n    # Train the XGBoost model\n    model = xgb.train(params, dtrain_fold, num_boost_round=params['n_estimators'], \n                      early_stopping_rounds=50, evals=[(dvalid_fold, 'eval')], verbose_eval=False)\n\n    # Predict and calculate RMSLE\n    y_pred = model.predict(dvalid_fold)\n    return rmsle(y_valid, y_pred)\n\n# Run Optuna study with pruning for faster convergence (parallelized search)\nstudy = optuna.create_study(direction='minimize', sampler=optuna.samplers.TPESampler())\nstudy.optimize(objective, n_trials=50, n_jobs=4)  # Parallelizing Optuna trials\n\n# Get best parameters\nbest_params = study.best_params\nprint(\"Best parameters:\", best_params)\n\n# Add GPU support to best parameters\nbest_params['tree_method'] = 'gpu_hist'\n\n# Cross-validation with best parameters\nkf = StratifiedKFold(n_splits=5, shuffle=True, random_state=42)\nfinal_predictions = np.zeros(len(test_features))\nrmsle_scores = []\nr2_scores = []\n\n# Storing base model predictions to use for ensembling\nbase_model_predictions = []\n\n# Using pre-split data for training\nfor fold, (train_idx, valid_idx) in enumerate(kf.split(X, y)):  # StratifiedKFold ensures better split\n    X_train, X_valid = X.iloc[train_idx], X.iloc[valid_idx]\n    y_train, y_valid = y.iloc[train_idx], y.iloc[valid_idx]\n\n    dtrain_fold = xgb.DMatrix(X_train, label=y_train, enable_categorical=True)\n    dvalid_fold = xgb.DMatrix(X_valid, label=y_valid, enable_categorical=True)\n\n    # Train the XGBoost model\n    model = xgb.train(best_params, dtrain_fold, num_boost_round=best_params['n_estimators'], \n                      early_stopping_rounds=50, evals=[(dvalid_fold, 'eval')], verbose_eval=False)\n\n    # Predict on validation and test sets\n    y_pred = model.predict(dvalid_fold)\n    fold_test_pred = model.predict(dtest)\n    final_predictions += fold_test_pred / kf.n_splits\n\n    # Store base model predictions for ensembling (bagging/stacking)\n    base_model_predictions.append(fold_test_pred)\n\n    # Calculate metrics\n    rmsle_scores.append(rmsle(y_valid, y_pred))\n    r2_scores.append(r2_score(y_valid, y_pred))\n\n    print(f\"Fold {fold + 1}: RMSLE = {rmsle_scores[-1]:.4f}, R2 = {r2_scores[-1]:.4f}\")\n\n# Print average metrics\nprint(f\"Average RMSLE: {np.mean(rmsle_scores):.4f}\")\nprint(f\"Average R-squared: {np.mean(r2_scores):.4f}\")\n\n# Stacking: Train a meta-model on the base model predictions (optional)\n# For simplicity, we can use a simple average, but a linear regression meta-model can also be used\nstacked_predictions = np.mean(base_model_predictions, axis=0)\n\n# Final submission\nsubmission = pd.DataFrame({\n    'id': test['id'],  # Ensure 'id' is included from test\n    'Premium Amount': stacked_predictions\n})\nsubmission.to_csv('submission.csv', index=False)\nprint(\"Submission file saved as 'submission.csv'.\")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-18T16:03:27.845964Z","iopub.execute_input":"2024-12-18T16:03:27.846757Z","iopub.status.idle":"2024-12-18T16:20:51.853581Z","shell.execute_reply.started":"2024-12-18T16:03:27.846696Z","shell.execute_reply":"2024-12-18T16:20:51.852538Z"}},"outputs":[{"name":"stderr","text":"[I 2024-12-18 16:03:28,258] A new study created in memory with name: no-name-5f678126-b5df-4454-8a53-e7bf39cc5ed1\n[16:03:30] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:30] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:30] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:30] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:30] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:30] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:30] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:30] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:34] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:03:35,057] Trial 1 finished with value: 1.1411579298571928 and parameters: {'n_estimators': 706, 'max_depth': 6, 'learning_rate': 0.14886026132616895, 'subsample': 0.792045307078802, 'colsample_bytree': 0.9084302035668737, 'alpha': 5.729765406968023, 'lambda': 2.4974597807296712}. Best is trial 1 with value: 1.1411579298571928.\n[16:03:36] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:36] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:37] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:03:37,892] Trial 0 finished with value: 1.1376398288763283 and parameters: {'n_estimators': 538, 'max_depth': 10, 'learning_rate': 0.14810960378683818, 'subsample': 0.788124200364824, 'colsample_bytree': 0.716918268763661, 'alpha': 8.940603606584762, 'lambda': 7.646877327273603}. Best is trial 0 with value: 1.1376398288763283.\n[16:03:39] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:39] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:41] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:03:41,507] Trial 2 finished with value: 1.135170673971382 and parameters: {'n_estimators': 1488, 'max_depth': 11, 'learning_rate': 0.07928077921804332, 'subsample': 0.9045477845563109, 'colsample_bytree': 0.9548874428788887, 'alpha': 2.5142178810166094, 'lambda': 8.288957085171225}. Best is trial 2 with value: 1.135170673971382.\n[16:03:42] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:42] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:51] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:03:51,640] Trial 6 finished with value: 1.1410502995555085 and parameters: {'n_estimators': 1419, 'max_depth': 6, 'learning_rate': 0.11095694520245392, 'subsample': 0.8912276227127072, 'colsample_bytree': 0.7526613055944947, 'alpha': 7.973472054453676, 'lambda': 7.067554357874238}. Best is trial 2 with value: 1.135170673971382.\n[16:03:52] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:52] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:54] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:03:54,640] Trial 5 finished with value: 1.1384191049032777 and parameters: {'n_estimators': 668, 'max_depth': 10, 'learning_rate': 0.05295099248339688, 'subsample': 0.8048651193380256, 'colsample_bytree': 0.6852921433806507, 'alpha': 2.169914975954231, 'lambda': 8.156016310468612}. Best is trial 2 with value: 1.135170673971382.\n[16:03:55] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:03:55] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:03:59] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:03:59,515] Trial 7 finished with value: 1.1392328608517448 and parameters: {'n_estimators': 1441, 'max_depth': 9, 'learning_rate': 0.07124228911657926, 'subsample': 0.6348353741723801, 'colsample_bytree': 0.6720404442913022, 'alpha': 6.784919598623864, 'lambda': 5.0242738827759625}. Best is trial 2 with value: 1.135170673971382.\n[16:04:00] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:04:00] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:04:14] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:04:14,773] Trial 8 finished with value: 1.1365854937002509 and parameters: {'n_estimators': 667, 'max_depth': 10, 'learning_rate': 0.04428416487094413, 'subsample': 0.9836091032506404, 'colsample_bytree': 0.766032005574306, 'alpha': 6.516447217822212, 'lambda': 0.9600693093621909}. Best is trial 2 with value: 1.135170673971382.\n[16:04:15] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:04:15] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:04:20] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:04:20,660] Trial 4 finished with value: 1.1397430762602265 and parameters: {'n_estimators': 1365, 'max_depth': 12, 'learning_rate': 0.05564037378082731, 'subsample': 0.7542390027956517, 'colsample_bytree': 0.6481209284316971, 'alpha': 5.575845545166515, 'lambda': 4.630059449580281}. Best is trial 2 with value: 1.135170673971382.\n[16:04:21] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:04:21] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:04:22] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:04:23,040] Trial 9 finished with value: 1.1467959140317832 and parameters: {'n_estimators': 1237, 'max_depth': 5, 'learning_rate': 0.013953935851851663, 'subsample': 0.6615383649746224, 'colsample_bytree': 0.8147373306264218, 'alpha': 5.676411350640077, 'lambda': 7.140416424467902}. Best is trial 2 with value: 1.135170673971382.\n[16:04:24] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:04:24] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:04:27] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:04:27,580] Trial 11 finished with value: 1.138144146543154 and parameters: {'n_estimators': 867, 'max_depth': 8, 'learning_rate': 0.10003609506341708, 'subsample': 0.8326202993257984, 'colsample_bytree': 0.9167573607874115, 'alpha': 7.580268118143881, 'lambda': 0.6381910643700908}. Best is trial 2 with value: 1.135170673971382.\n[16:04:28] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:04:28] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:04:30] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:04:30,471] Trial 12 finished with value: 1.1393943672550246 and parameters: {'n_estimators': 248, 'max_depth': 7, 'learning_rate': 0.07917282467672236, 'subsample': 0.9190687961268289, 'colsample_bytree': 0.9125777371627317, 'alpha': 9.864243950366973, 'lambda': 3.751690520537829}. Best is trial 2 with value: 1.135170673971382.\n[16:04:31] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:04:31] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:05:10] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:05:11,053] Trial 3 finished with value: 1.1367314081358266 and parameters: {'n_estimators': 1093, 'max_depth': 12, 'learning_rate': 0.013488116419658702, 'subsample': 0.6063319568240114, 'colsample_bytree': 0.7863818527876989, 'alpha': 5.84322879309981, 'lambda': 7.690525117247136}. Best is trial 2 with value: 1.135170673971382.\n[16:05:12] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:05:12] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:05:24] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n/tmp/ipykernel_23/2075871384.py:11: RuntimeWarning: invalid value encountered in log1p\n  log_pred = np.log1p(y_pred)\n[I 2024-12-18 16:05:24,917] Trial 13 finished with value: 1.135890871624135 and parameters: {'n_estimators': 1051, 'max_depth': 15, 'learning_rate': 0.10472232678623654, 'subsample': 0.9797494870330841, 'colsample_bytree': 0.9798999631059937, 'alpha': 0.13081962632296484, 'lambda': 9.249912305406268}. Best is trial 2 with value: 1.135170673971382.\n[16:05:26] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:05:26] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:05:29] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:05:29,803] Trial 10 finished with value: 1.1367528976512942 and parameters: {'n_estimators': 532, 'max_depth': 14, 'learning_rate': 0.050616931681966015, 'subsample': 0.689852480735616, 'colsample_bytree': 0.7756533721167711, 'alpha': 7.983361217336117, 'lambda': 6.298387844664161}. Best is trial 2 with value: 1.135170673971382.\n[16:05:30] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:05:30] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:06:19] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n/tmp/ipykernel_23/2075871384.py:11: RuntimeWarning: invalid value encountered in log1p\n  log_pred = np.log1p(y_pred)\n[I 2024-12-18 16:06:19,965] Trial 16 finished with value: 1.1365839933276582 and parameters: {'n_estimators': 1006, 'max_depth': 15, 'learning_rate': 0.10911194979653167, 'subsample': 0.999929381301485, 'colsample_bytree': 0.9907865543241201, 'alpha': 0.23838688596581648, 'lambda': 9.590256766711269}. Best is trial 2 with value: 1.135170673971382.\n[16:06:21] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:06:21] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:06:25] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n/tmp/ipykernel_23/2075871384.py:11: RuntimeWarning: invalid value encountered in log1p\n  log_pred = np.log1p(y_pred)\n[I 2024-12-18 16:06:25,570] Trial 17 finished with value: 1.1366219920084557 and parameters: {'n_estimators': 1067, 'max_depth': 15, 'learning_rate': 0.11355766369324749, 'subsample': 0.9849240456680484, 'colsample_bytree': 0.9979208407758761, 'alpha': 0.1628584561080202, 'lambda': 9.696156210095962}. Best is trial 2 with value: 1.135170673971382.\n[16:06:26] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:06:26] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:06:40] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n/tmp/ipykernel_23/2075871384.py:11: RuntimeWarning: invalid value encountered in log1p\n  log_pred = np.log1p(y_pred)\n[I 2024-12-18 16:06:40,552] Trial 19 finished with value: 1.1362343273030222 and parameters: {'n_estimators': 1220, 'max_depth': 12, 'learning_rate': 0.13064568000493237, 'subsample': 0.9187615789240355, 'colsample_bytree': 0.8642316456865653, 'alpha': 3.169267034734995, 'lambda': 8.941301941696478}. Best is trial 2 with value: 1.135170673971382.\n[16:06:41] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:06:41] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:06:41] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n/tmp/ipykernel_23/2075871384.py:11: RuntimeWarning: invalid value encountered in log1p\n  log_pred = np.log1p(y_pred)\n[I 2024-12-18 16:06:41,961] Trial 18 finished with value: 1.135637314324366 and parameters: {'n_estimators': 1179, 'max_depth': 13, 'learning_rate': 0.1253397493441769, 'subsample': 0.9257951317845693, 'colsample_bytree': 0.976223602013525, 'alpha': 3.2935128951200277, 'lambda': 9.404778107723406}. Best is trial 2 with value: 1.135170673971382.\n[16:06:43] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:06:43] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:06:45] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:06:45,866] Trial 15 finished with value: 1.1341230364320032 and parameters: {'n_estimators': 995, 'max_depth': 15, 'learning_rate': 0.038416365722863687, 'subsample': 0.9961918195922297, 'colsample_bytree': 0.9983829846430413, 'alpha': 3.0272936189424096, 'lambda': 9.981918556126784}. Best is trial 15 with value: 1.1341230364320032.\n[16:06:47] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:06:47] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:07:04] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:07:05,015] Trial 20 finished with value: 1.1353473339720093 and parameters: {'n_estimators': 1259, 'max_depth': 13, 'learning_rate': 0.09180705372579252, 'subsample': 0.8697476271357206, 'colsample_bytree': 0.9544075505205634, 'alpha': 1.984753174497759, 'lambda': 5.986939163594962}. Best is trial 15 with value: 1.1341230364320032.\n[16:07:06] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:07:06] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:07:08] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:07:08,813] Trial 21 finished with value: 1.1356682372438016 and parameters: {'n_estimators': 1274, 'max_depth': 13, 'learning_rate': 0.08749167203710331, 'subsample': 0.8704834684181654, 'colsample_bytree': 0.948758523688822, 'alpha': 3.8753749659600283, 'lambda': 5.851025523866259}. Best is trial 15 with value: 1.1341230364320032.\n[16:07:09] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:07:09] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:07:22] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:07:23,249] Trial 14 finished with value: 1.135879371003336 and parameters: {'n_estimators': 1019, 'max_depth': 15, 'learning_rate': 0.025477104050131535, 'subsample': 0.9900013169090425, 'colsample_bytree': 0.8114343956860853, 'alpha': 3.256013692470716, 'lambda': 9.711604663002845}. Best is trial 15 with value: 1.1341230364320032.\n[16:07:24] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:07:24] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:07:31] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:07:31,136] Trial 23 finished with value: 1.1356326543269835 and parameters: {'n_estimators': 898, 'max_depth': 11, 'learning_rate': 0.027044631490392913, 'subsample': 0.9427051346294217, 'colsample_bytree': 0.8546475468323443, 'alpha': 4.243100400815044, 'lambda': 8.700281460274214}. Best is trial 15 with value: 1.1341230364320032.\n[16:07:32] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:07:32] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:07:55] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:07:55,790] Trial 25 finished with value: 1.135131000172478 and parameters: {'n_estimators': 1338, 'max_depth': 13, 'learning_rate': 0.06695706528090811, 'subsample': 0.856998219135465, 'colsample_bytree': 0.8560292041662204, 'alpha': 1.7170618613288817, 'lambda': 8.450099015856576}. Best is trial 15 with value: 1.1341230364320032.\n[16:07:56] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:07:56,971] Trial 24 finished with value: 1.1353262348720667 and parameters: {'n_estimators': 1489, 'max_depth': 13, 'learning_rate': 0.035621022117713216, 'subsample': 0.8674886484723189, 'colsample_bytree': 0.8622227848674043, 'alpha': 1.6722166568652512, 'lambda': 8.497231219816348}. Best is trial 15 with value: 1.1341230364320032.\n[16:07:57] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:07:57] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:07:58] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:07:58] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:08:01] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:08:01,883] Trial 26 finished with value: 1.1350257583598948 and parameters: {'n_estimators': 1342, 'max_depth': 13, 'learning_rate': 0.06516820395828982, 'subsample': 0.855440809507666, 'colsample_bytree': 0.9441873178862948, 'alpha': 1.8399374487942277, 'lambda': 5.902183581395597}. Best is trial 15 with value: 1.1341230364320032.\n[16:08:03] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:08:03] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:08:46] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:08:46,119] Trial 28 finished with value: 1.1350951071274122 and parameters: {'n_estimators': 1322, 'max_depth': 14, 'learning_rate': 0.06685549879586156, 'subsample': 0.952071531067456, 'colsample_bytree': 0.9295541151764442, 'alpha': 1.4162056176322528, 'lambda': 6.71149571540978}. Best is trial 15 with value: 1.1341230364320032.\n[16:08:47] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:08:47,155] Trial 27 finished with value: 1.1352301896346841 and parameters: {'n_estimators': 1466, 'max_depth': 14, 'learning_rate': 0.07082594152800413, 'subsample': 0.8449945012507676, 'colsample_bytree': 0.8658273305575177, 'alpha': 1.6767307265645286, 'lambda': 8.256306487778067}. Best is trial 15 with value: 1.1341230364320032.\n[16:08:47] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:08:47] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:08:48] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:08:48] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:08:54] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:08:54,171] Trial 29 finished with value: 1.1365636014904015 and parameters: {'n_estimators': 1350, 'max_depth': 14, 'learning_rate': 0.0666742111673036, 'subsample': 0.7381563845989998, 'colsample_bytree': 0.8899558640912367, 'alpha': 1.4655683912946071, 'lambda': 3.17641391754344}. Best is trial 15 with value: 1.1341230364320032.\n[16:08:55] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:08:55] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:09:13] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:09:14,092] Trial 22 finished with value: 1.137828251038313 and parameters: {'n_estimators': 904, 'max_depth': 13, 'learning_rate': 0.02454772802488349, 'subsample': 0.8665001121799772, 'colsample_bytree': 0.6001978338943469, 'alpha': 3.957885445470706, 'lambda': 8.513071770675586}. Best is trial 15 with value: 1.1341230364320032.\n[16:09:15] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:09:15] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:09:40] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:09:40,126] Trial 30 finished with value: 1.135629136165737 and parameters: {'n_estimators': 1169, 'max_depth': 14, 'learning_rate': 0.06291994847726641, 'subsample': 0.959620314826139, 'colsample_bytree': 0.8932217439096657, 'alpha': 1.0673932500921204, 'lambda': 3.6583534670136615}. Best is trial 15 with value: 1.1341230364320032.\n[16:09:41] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:09:41] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:10:08] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:10:08,940] Trial 32 finished with value: 1.1348146613984464 and parameters: {'n_estimators': 1135, 'max_depth': 14, 'learning_rate': 0.03521976250092039, 'subsample': 0.9497844460121512, 'colsample_bytree': 0.9409906185429634, 'alpha': 0.9251082989969737, 'lambda': 6.71489596537427}. Best is trial 15 with value: 1.1341230364320032.\n[16:10:10] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:10:10] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:10:17] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:10:17,292] Trial 33 finished with value: 1.13503167771642 and parameters: {'n_estimators': 1136, 'max_depth': 14, 'learning_rate': 0.044171395724855386, 'subsample': 0.9557979528437331, 'colsample_bytree': 0.9342489347276919, 'alpha': 0.9664544450081495, 'lambda': 4.34222373142845}. Best is trial 15 with value: 1.1341230364320032.\n[16:10:18] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:10:18] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:10:29] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:10:29,902] Trial 31 finished with value: 1.1404218090029838 and parameters: {'n_estimators': 1130, 'max_depth': 14, 'learning_rate': 0.059872736867609196, 'subsample': 0.9495639834007817, 'colsample_bytree': 0.6051953329696047, 'alpha': 0.946348128076272, 'lambda': 2.350013567040258}. Best is trial 15 with value: 1.1341230364320032.\n[16:10:31] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:10:31] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:10:41] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:10:41,209] Trial 34 finished with value: 1.1346004865329957 and parameters: {'n_estimators': 1302, 'max_depth': 14, 'learning_rate': 0.04391853601704315, 'subsample': 0.9518217797427126, 'colsample_bytree': 0.9337903554505453, 'alpha': 2.6027842218005914, 'lambda': 6.723432311646582}. Best is trial 15 with value: 1.1341230364320032.\n[16:10:42] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:10:42] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:11:12] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:11:13,096] Trial 35 finished with value: 1.1347774381351325 and parameters: {'n_estimators': 1116, 'max_depth': 14, 'learning_rate': 0.039892567579612015, 'subsample': 0.9544089353637113, 'colsample_bytree': 0.9364907295889386, 'alpha': 1.1502793569689778, 'lambda': 6.717091965710184}. Best is trial 15 with value: 1.1341230364320032.\n[16:11:14] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:11:14] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:11:29] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:11:29,189] Trial 39 finished with value: 1.1354060159910115 and parameters: {'n_estimators': 965, 'max_depth': 11, 'learning_rate': 0.037860355118351965, 'subsample': 0.896738053648141, 'colsample_bytree': 0.9682850421888902, 'alpha': 2.6132459500026837, 'lambda': 7.659432207660679}. Best is trial 15 with value: 1.1341230364320032.\n[16:11:30] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:11:30] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:11:58] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:11:58,162] Trial 36 finished with value: 1.1346340976772025 and parameters: {'n_estimators': 1129, 'max_depth': 15, 'learning_rate': 0.04159411237073487, 'subsample': 0.9559679334216029, 'colsample_bytree': 0.9404647842580733, 'alpha': 0.6850751598086768, 'lambda': 5.294004125687989}. Best is trial 15 with value: 1.1341230364320032.\n[16:11:59] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:11:59] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:12:21] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:12:21,936] Trial 38 finished with value: 1.1343887560238026 and parameters: {'n_estimators': 785, 'max_depth': 15, 'learning_rate': 0.038075241277358285, 'subsample': 0.8907549877478681, 'colsample_bytree': 0.9692629009531774, 'alpha': 2.67405913744022, 'lambda': 5.845847163922602}. Best is trial 15 with value: 1.1341230364320032.\n[16:12:23] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:12:23] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:12:27] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:12:27,901] Trial 37 finished with value: 1.1343914031961284 and parameters: {'n_estimators': 783, 'max_depth': 15, 'learning_rate': 0.034780549254633156, 'subsample': 0.9059414565805316, 'colsample_bytree': 0.9392622014891849, 'alpha': 2.5687530456176777, 'lambda': 5.503164882618573}. Best is trial 15 with value: 1.1341230364320032.\n[16:12:29] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:12:29] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:13:34] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:13:34,996] Trial 40 finished with value: 1.1353835138455894 and parameters: {'n_estimators': 749, 'max_depth': 15, 'learning_rate': 0.03438083354238696, 'subsample': 0.9301090773182012, 'colsample_bytree': 0.8949348596741613, 'alpha': 2.8123042473427105, 'lambda': 5.367362977390195}. Best is trial 15 with value: 1.1341230364320032.\n[16:13:36] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:13:36] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:15:24] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:15:24,959] Trial 42 finished with value: 1.134235427131735 and parameters: {'n_estimators': 764, 'max_depth': 15, 'learning_rate': 0.017900694806065945, 'subsample': 0.8188410700312427, 'colsample_bytree': 0.9986604977284005, 'alpha': 4.608100248788141, 'lambda': 5.257545174158868}. Best is trial 15 with value: 1.1341230364320032.\n[16:15:26] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:15:26] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:16:31] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:16:31,630] Trial 43 finished with value: 1.1349383724818494 and parameters: {'n_estimators': 753, 'max_depth': 15, 'learning_rate': 0.011673857773051046, 'subsample': 0.8960231101089863, 'colsample_bytree': 0.967837479398122, 'alpha': 4.968769468934302, 'lambda': 5.184747383330485}. Best is trial 15 with value: 1.1341230364320032.\n[16:16:32] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:16:32] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:17:13] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:17:13,547] Trial 41 finished with value: 1.1362041874889206 and parameters: {'n_estimators': 957, 'max_depth': 15, 'learning_rate': 0.011447277734534404, 'subsample': 0.9671576892070654, 'colsample_bytree': 0.8954863966582796, 'alpha': 2.8605141619706442, 'lambda': 5.105675286280423}. Best is trial 15 with value: 1.1341230364320032.\n[16:17:14] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:17:14] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:17:57] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:17:57,360] Trial 46 finished with value: 1.134832882501607 and parameters: {'n_estimators': 544, 'max_depth': 15, 'learning_rate': 0.04966434209741852, 'subsample': 0.8218640536294683, 'colsample_bytree': 0.9936706169476357, 'alpha': 4.5636389198742, 'lambda': 4.647112918827773}. Best is trial 15 with value: 1.1341230364320032.\n[16:17:58] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:17:58] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:18:16] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:18:16,897] Trial 45 finished with value: 1.1345798680543053 and parameters: {'n_estimators': 758, 'max_depth': 15, 'learning_rate': 0.017863120694491003, 'subsample': 0.8127879229075465, 'colsample_bytree': 0.9648633954525098, 'alpha': 4.78632485579094, 'lambda': 4.4720936509252445}. Best is trial 15 with value: 1.1341230364320032.\n[16:18:18] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[16:18:18] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"enable_categorical\", \"n_estimators\" } are not used.\n\n[16:18:19] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:18:19,200] Trial 48 finished with value: 1.1363880435937845 and parameters: {'n_estimators': 829, 'max_depth': 9, 'learning_rate': 0.018742352927202926, 'subsample': 0.7950591423800751, 'colsample_bytree': 0.9699747231546266, 'alpha': 2.323266626819015, 'lambda': 7.1654880965342}. Best is trial 15 with value: 1.1341230364320032.\n[16:18:24] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:18:25,074] Trial 44 finished with value: 1.1347341692632529 and parameters: {'n_estimators': 792, 'max_depth': 15, 'learning_rate': 0.010189453117460827, 'subsample': 0.899410406696143, 'colsample_bytree': 0.9625421896697194, 'alpha': 4.618343603244885, 'lambda': 5.055974920131299}. Best is trial 15 with value: 1.1341230364320032.\n[16:18:34] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:18:34,344] Trial 49 finished with value: 1.137912500132291 and parameters: {'n_estimators': 786, 'max_depth': 9, 'learning_rate': 0.020060477639014963, 'subsample': 0.7775690520818086, 'colsample_bytree': 0.7381916231010801, 'alpha': 5.145476136069247, 'lambda': 2.2271514020885466}. Best is trial 15 with value: 1.1341230364320032.\n[16:18:50] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n[I 2024-12-18 16:18:51,092] Trial 47 finished with value: 1.1343891867540574 and parameters: {'n_estimators': 561, 'max_depth': 15, 'learning_rate': 0.020155115517263258, 'subsample': 0.8164047497950621, 'colsample_bytree': 0.9995079598666168, 'alpha': 4.63740954130264, 'lambda': 4.604758681737537}. Best is trial 15 with value: 1.1341230364320032.\n","output_type":"stream"},{"name":"stdout","text":"Best parameters: {'n_estimators': 995, 'max_depth': 15, 'learning_rate': 0.038416365722863687, 'subsample': 0.9961918195922297, 'colsample_bytree': 0.9983829846430413, 'alpha': 3.0272936189424096, 'lambda': 9.981918556126784}\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/sklearn/model_selection/_split.py:700: UserWarning: The least populated class in y has only 1 members, which is less than n_splits=5.\n  warnings.warn(\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:18:55] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:18:55] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"n_estimators\" } are not used.\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:19:17] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n","output_type":"stream"},{"name":"stdout","text":"Fold 1: RMSLE = 1.1333, R2 = 0.0547\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:19:18] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:19:18] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"n_estimators\" } are not used.\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:19:40] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n","output_type":"stream"},{"name":"stdout","text":"Fold 2: RMSLE = 1.1329, R2 = 0.0540\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:19:41] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:19:41] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"n_estimators\" } are not used.\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:20:03] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n","output_type":"stream"},{"name":"stdout","text":"Fold 3: RMSLE = 1.1311, R2 = 0.0578\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:20:04] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:20:04] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"n_estimators\" } are not used.\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:20:27] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n","output_type":"stream"},{"name":"stdout","text":"Fold 4: RMSLE = 1.1310, R2 = 0.0586\n","output_type":"stream"},{"name":"stderr","text":"/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:20:28] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:20:28] WARNING: /workspace/src/learner.cc:742: \nParameters: { \"n_estimators\" } are not used.\n\n  warnings.warn(smsg, UserWarning)\n/opt/conda/lib/python3.10/site-packages/xgboost/core.py:160: UserWarning: [16:20:50] WARNING: /workspace/src/common/error_msg.cc:27: The tree method `gpu_hist` is deprecated since 2.0.0. To use GPU training, set the `device` parameter to CUDA instead.\n\n    E.g. tree_method = \"hist\", device = \"cuda\"\n\n  warnings.warn(smsg, UserWarning)\n","output_type":"stream"},{"name":"stdout","text":"Fold 5: RMSLE = 1.1324, R2 = 0.0553\nAverage RMSLE: 1.1322\nAverage R-squared: 0.0561\nSubmission file saved as 'submission.csv'.\n","output_type":"stream"}],"execution_count":261}]}