{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","version":"3.10.12","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":84896,"databundleVersionId":10305135,"sourceType":"competition"}],"dockerImageVersionId":30822,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Regression with an Insurance Dataset\n# Playground Series - Season 4, Episode 12\n\n## Using XGB Regressor","metadata":{}},{"cell_type":"markdown","source":"# Importing Required Libraries\n","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport numpy as np\nimport xgboost as xgb\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.metrics import r2_score","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:41:49.015189Z","iopub.execute_input":"2025-01-01T07:41:49.015493Z","iopub.status.idle":"2025-01-01T07:41:50.673045Z","shell.execute_reply.started":"2025-01-01T07:41:49.015467Z","shell.execute_reply":"2025-01-01T07:41:50.672058Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Training Data","metadata":{}},{"cell_type":"code","source":"dataset = pd.read_csv('/kaggle/input/playground-series-s4e12/train.csv')\ndataset.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:41:50.674007Z","iopub.execute_input":"2025-01-01T07:41:50.674437Z","iopub.status.idle":"2025-01-01T07:41:57.479169Z","shell.execute_reply.started":"2025-01-01T07:41:50.674372Z","shell.execute_reply":"2025-01-01T07:41:57.478234Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"print(dataset.shape)\n\nprint(dataset.columns)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:41:57.48018Z","iopub.execute_input":"2025-01-01T07:41:57.480579Z","iopub.status.idle":"2025-01-01T07:41:57.487792Z","shell.execute_reply.started":"2025-01-01T07:41:57.480541Z","shell.execute_reply":"2025-01-01T07:41:57.486893Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset.dtypes.value_counts()\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:41:57.490327Z","iopub.execute_input":"2025-01-01T07:41:57.490647Z","iopub.status.idle":"2025-01-01T07:41:57.515108Z","shell.execute_reply.started":"2025-01-01T07:41:57.490619Z","shell.execute_reply":"2025-01-01T07:41:57.513879Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"datatypes = dataset.columns.to_series().groupby(dataset.dtypes).groups\ndatatypes","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:41:57.516715Z","iopub.execute_input":"2025-01-01T07:41:57.517017Z","iopub.status.idle":"2025-01-01T07:41:57.53737Z","shell.execute_reply.started":"2025-01-01T07:41:57.516988Z","shell.execute_reply":"2025-01-01T07:41:57.53619Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Dropping id from dataset\n\ndataset.drop(['id'], axis=1, inplace = True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:41:57.538451Z","iopub.execute_input":"2025-01-01T07:41:57.538778Z","iopub.status.idle":"2025-01-01T07:41:57.710837Z","shell.execute_reply.started":"2025-01-01T07:41:57.538751Z","shell.execute_reply":"2025-01-01T07:41:57.709834Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Converting Policy Start Date\n\ndataset[['Date', 'Time']] = dataset['Policy Start Date'].str.split(' ', expand=True)\ndataset[['Year', 'Month','Day']] = dataset['Date'].str.split('-', expand=True)\ndataset[['Hour', 'Minutes','seconds']] = dataset['Time'].str.split(':', expand=True)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:41:57.711756Z","iopub.execute_input":"2025-01-01T07:41:57.712113Z","iopub.status.idle":"2025-01-01T07:42:08.32083Z","shell.execute_reply.started":"2025-01-01T07:41:57.712078Z","shell.execute_reply":"2025-01-01T07:42:08.31977Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset.drop(['Policy Start Date','Date', 'Time'], axis=1, inplace = True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:08.321814Z","iopub.execute_input":"2025-01-01T07:42:08.322167Z","iopub.status.idle":"2025-01-01T07:42:08.860111Z","shell.execute_reply.started":"2025-01-01T07:42:08.32213Z","shell.execute_reply":"2025-01-01T07:42:08.859239Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset[['Year', 'Month', 'Day', 'Hour', 'Minutes' ]] = dataset[['Year', 'Month', 'Day', 'Hour', 'Minutes']].astype(int)\ndataset[['seconds']] = dataset[['seconds']].astype(float)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:08.860829Z","iopub.execute_input":"2025-01-01T07:42:08.861098Z","iopub.status.idle":"2025-01-01T07:42:10.238846Z","shell.execute_reply.started":"2025-01-01T07:42:08.861072Z","shell.execute_reply":"2025-01-01T07:42:10.237709Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Converting Categorical Values\n\ndataset[['Gender', 'Marital Status', 'Education Level', 'Occupation', 'Location', 'Policy Type', 'Customer Feedback', 'Smoking Status', 'Exercise Frequency', 'Property Type']] = dataset[['Gender', 'Marital Status', 'Education Level', 'Occupation', 'Location', 'Policy Type', 'Customer Feedback', 'Smoking Status', 'Exercise Frequency', 'Property Type']].apply(LabelEncoder().fit_transform)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:10.239933Z","iopub.execute_input":"2025-01-01T07:42:10.240242Z","iopub.status.idle":"2025-01-01T07:42:12.576322Z","shell.execute_reply.started":"2025-01-01T07:42:10.240214Z","shell.execute_reply":"2025-01-01T07:42:12.575221Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Check for NAN values\n\ndataset.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:12.577361Z","iopub.execute_input":"2025-01-01T07:42:12.577712Z","iopub.status.idle":"2025-01-01T07:42:12.638615Z","shell.execute_reply.started":"2025-01-01T07:42:12.577687Z","shell.execute_reply":"2025-01-01T07:42:12.637367Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Filling NaN values with mean\n\nfor i in dataset.columns[dataset.isnull().any(axis=0)]:    \n    dataset[i].fillna(dataset[i].mean(),inplace=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:12.639821Z","iopub.execute_input":"2025-01-01T07:42:12.640188Z","iopub.status.idle":"2025-01-01T07:42:12.761687Z","shell.execute_reply.started":"2025-01-01T07:42:12.640151Z","shell.execute_reply":"2025-01-01T07:42:12.760682Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset.describe().T","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:12.765345Z","iopub.execute_input":"2025-01-01T07:42:12.765758Z","iopub.status.idle":"2025-01-01T07:42:14.009744Z","shell.execute_reply.started":"2025-01-01T07:42:12.765723Z","shell.execute_reply":"2025-01-01T07:42:14.00848Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Data Visualisation","metadata":{}},{"cell_type":"code","source":"corr_matrix = dataset.corr()\nfig, ax = plt.subplots(figsize=(15, 15))\nax = sns.heatmap(corr_matrix, annot=True, linewidths=0.5,fmt=\".2f\", cmap=\"viridis\");                                  \nbottom, top = ax.get_ylim()\nax.set_ylim(bottom + 0.5, top - 0.5)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:14.012325Z","iopub.execute_input":"2025-01-01T07:42:14.012677Z","iopub.status.idle":"2025-01-01T07:42:17.989854Z","shell.execute_reply.started":"2025-01-01T07:42:14.012642Z","shell.execute_reply":"2025-01-01T07:42:17.988609Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Dropping Columns Hour and Minute as they have same unique values\n\ndataset.drop(['Hour', 'Minutes'], axis=1, inplace = True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:17.990989Z","iopub.execute_input":"2025-01-01T07:42:17.991436Z","iopub.status.idle":"2025-01-01T07:42:18.115067Z","shell.execute_reply.started":"2025-01-01T07:42:17.991392Z","shell.execute_reply":"2025-01-01T07:42:18.113967Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset = dataset.rename(columns={'Premium Amount': 'Premium_Amount'})","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:18.116003Z","iopub.execute_input":"2025-01-01T07:42:18.11645Z","iopub.status.idle":"2025-01-01T07:42:18.326414Z","shell.execute_reply.started":"2025-01-01T07:42:18.116409Z","shell.execute_reply":"2025-01-01T07:42:18.32542Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset.drop('Premium_Amount', axis=1).corrwith(dataset.Premium_Amount).plot(kind='bar', color='Green', figsize=(14, 7), title=\"Correlation with Premium Amount \")\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:18.327304Z","iopub.execute_input":"2025-01-01T07:42:18.327638Z","iopub.status.idle":"2025-01-01T07:42:19.231115Z","shell.execute_reply.started":"2025-01-01T07:42:18.32761Z","shell.execute_reply":"2025-01-01T07:42:19.229966Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:19.232146Z","iopub.execute_input":"2025-01-01T07:42:19.232548Z","iopub.status.idle":"2025-01-01T07:42:19.484891Z","shell.execute_reply.started":"2025-01-01T07:42:19.232518Z","shell.execute_reply":"2025-01-01T07:42:19.483767Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Choosing X and y \n\nX = dataset.drop('Premium_Amount', axis=1)\ny = dataset.Premium_Amount","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:19.485835Z","iopub.execute_input":"2025-01-01T07:42:19.486096Z","iopub.status.idle":"2025-01-01T07:42:19.550933Z","shell.execute_reply.started":"2025-01-01T07:42:19.486074Z","shell.execute_reply":"2025-01-01T07:42:19.549936Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.3, random_state=42)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:19.551994Z","iopub.execute_input":"2025-01-01T07:42:19.552298Z","iopub.status.idle":"2025-01-01T07:42:19.954032Z","shell.execute_reply.started":"2025-01-01T07:42:19.552272Z","shell.execute_reply":"2025-01-01T07:42:19.952835Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Test Data","metadata":{}},{"cell_type":"code","source":"dataset_test = pd.read_csv('/kaggle/input/playground-series-s4e12/test.csv')\ndataset_test1 = dataset_test.copy()\n\ndataset_test.head()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:19.955573Z","iopub.execute_input":"2025-01-01T07:42:19.955966Z","iopub.status.idle":"2025-01-01T07:42:23.989543Z","shell.execute_reply.started":"2025-01-01T07:42:19.955929Z","shell.execute_reply":"2025-01-01T07:42:23.988024Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Dropping id from dataset\n\ndataset_test.drop(['id'], axis=1, inplace = True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:23.990773Z","iopub.execute_input":"2025-01-01T07:42:23.991147Z","iopub.status.idle":"2025-01-01T07:42:24.100102Z","shell.execute_reply.started":"2025-01-01T07:42:23.991108Z","shell.execute_reply":"2025-01-01T07:42:24.099161Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Converting Policy Start Date\n\ndataset_test[['Date', 'Time']] = dataset_test['Policy Start Date'].str.split(' ', expand=True)\ndataset_test[['Year', 'Month','Day']] = dataset_test['Date'].str.split('-', expand=True)\ndataset_test[['Hour', 'Minutes','seconds']] = dataset_test['Time'].str.split(':', expand=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:24.101079Z","iopub.execute_input":"2025-01-01T07:42:24.101403Z","iopub.status.idle":"2025-01-01T07:42:31.921649Z","shell.execute_reply.started":"2025-01-01T07:42:24.101347Z","shell.execute_reply":"2025-01-01T07:42:31.920611Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset_test.drop(['Policy Start Date','Date', 'Time'], axis=1, inplace = True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:31.92264Z","iopub.execute_input":"2025-01-01T07:42:31.922896Z","iopub.status.idle":"2025-01-01T07:42:32.256981Z","shell.execute_reply.started":"2025-01-01T07:42:31.922875Z","shell.execute_reply":"2025-01-01T07:42:32.255932Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset_test[['Year', 'Month', 'Day', 'Hour', 'Minutes' ]] = dataset_test[['Year', 'Month', 'Day', 'Hour', 'Minutes']].astype(int)\ndataset_test[['seconds']] = dataset_test[['seconds']].astype(float)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:32.258218Z","iopub.execute_input":"2025-01-01T07:42:32.258667Z","iopub.status.idle":"2025-01-01T07:42:33.230488Z","shell.execute_reply.started":"2025-01-01T07:42:32.258632Z","shell.execute_reply":"2025-01-01T07:42:33.229342Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"dataset_test.drop(['Hour', 'Minutes'], axis=1, inplace = True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:33.231514Z","iopub.execute_input":"2025-01-01T07:42:33.231808Z","iopub.status.idle":"2025-01-01T07:42:33.356651Z","shell.execute_reply.started":"2025-01-01T07:42:33.231781Z","shell.execute_reply":"2025-01-01T07:42:33.355601Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Converting Categorical Values\n\ndataset_test[['Gender', 'Marital Status', 'Education Level', 'Occupation', 'Location', 'Policy Type', 'Customer Feedback', 'Smoking Status', 'Exercise Frequency', 'Property Type']] = dataset_test[['Gender', 'Marital Status', 'Education Level', 'Occupation', 'Location', 'Policy Type', 'Customer Feedback', 'Smoking Status', 'Exercise Frequency', 'Property Type']].apply(LabelEncoder().fit_transform)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:33.357868Z","iopub.execute_input":"2025-01-01T07:42:33.358169Z","iopub.status.idle":"2025-01-01T07:42:34.970884Z","shell.execute_reply.started":"2025-01-01T07:42:33.358141Z","shell.execute_reply":"2025-01-01T07:42:34.969718Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Check for NAN values\n\ndataset_test.isna().sum()","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:34.971893Z","iopub.execute_input":"2025-01-01T07:42:34.972196Z","iopub.status.idle":"2025-01-01T07:42:35.002904Z","shell.execute_reply.started":"2025-01-01T07:42:34.972169Z","shell.execute_reply":"2025-01-01T07:42:35.001799Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"# Filling NaN values with mean\n\nfor i in dataset_test.columns[dataset_test.isnull().any(axis=0)]:    \n    dataset_test[i].fillna(dataset_test[i].mean(),inplace=True)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:35.004148Z","iopub.execute_input":"2025-01-01T07:42:35.00448Z","iopub.status.idle":"2025-01-01T07:42:35.081713Z","shell.execute_reply.started":"2025-01-01T07:42:35.004453Z","shell.execute_reply":"2025-01-01T07:42:35.080654Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Machine Learning","metadata":{}},{"cell_type":"markdown","source":"### XGB Regressor model","metadata":{}},{"cell_type":"code","source":"reg = xgb.XGBRegressor(learning_rate =0.1, n_estimators=1500, max_depth=5, min_child_weight=1,gamma=0,subsample=0.8,colsample_bytree=0.8,objective= 'reg:linear',nthread=4,scale_pos_weight=1,seed=27)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:35.082846Z","iopub.execute_input":"2025-01-01T07:42:35.083228Z","iopub.status.idle":"2025-01-01T07:42:35.088441Z","shell.execute_reply.started":"2025-01-01T07:42:35.083187Z","shell.execute_reply":"2025-01-01T07:42:35.087237Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"reg.fit(X_train,y_train)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:42:35.089512Z","iopub.execute_input":"2025-01-01T07:42:35.089955Z","iopub.status.idle":"2025-01-01T07:43:24.746406Z","shell.execute_reply.started":"2025-01-01T07:42:35.089918Z","shell.execute_reply":"2025-01-01T07:43:24.745226Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pred_t = reg.predict(X_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:43:24.747651Z","iopub.execute_input":"2025-01-01T07:43:24.748044Z","iopub.status.idle":"2025-01-01T07:43:28.816667Z","shell.execute_reply.started":"2025-01-01T07:43:24.748005Z","shell.execute_reply":"2025-01-01T07:43:28.81584Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Evaluation","metadata":{}},{"cell_type":"markdown","source":"### R-Squared Error","metadata":{}},{"cell_type":"code","source":"print(\"R^2 on training  data \",reg.score(X_train, y_train))\nprint(\"R^2 on testing data \",reg.score(X_test,y_test))","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:43:28.817256Z","iopub.execute_input":"2025-01-01T07:43:28.817548Z","iopub.status.idle":"2025-01-01T07:43:43.087474Z","shell.execute_reply.started":"2025-01-01T07:43:28.817522Z","shell.execute_reply":"2025-01-01T07:43:43.086183Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"### RMSE","metadata":{}},{"cell_type":"code","source":"mse = mean_squared_error(y_test, pred_t)\nrmse = np.sqrt(mse)\nrmse\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:43:43.088563Z","iopub.execute_input":"2025-01-01T07:43:43.088941Z","iopub.status.idle":"2025-01-01T07:43:43.097863Z","shell.execute_reply.started":"2025-01-01T07:43:43.088903Z","shell.execute_reply":"2025-01-01T07:43:43.096712Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Prediction","metadata":{}},{"cell_type":"code","source":"pred_xgb = reg.predict(dataset_test)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:43:43.098802Z","iopub.execute_input":"2025-01-01T07:43:43.099083Z","iopub.status.idle":"2025-01-01T07:43:52.111985Z","shell.execute_reply.started":"2025-01-01T07:43:43.099057Z","shell.execute_reply":"2025-01-01T07:43:52.110733Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"pred = pd.DataFrame(pred_xgb, columns=['Premium Amount'])\noutput = pd.concat([dataset_test1['id'],pred],axis=1).set_index(['id'])","metadata":{"execution":{"iopub.status.busy":"2025-01-01T07:43:52.113057Z","iopub.execute_input":"2025-01-01T07:43:52.113356Z","iopub.status.idle":"2025-01-01T07:43:52.123634Z","shell.execute_reply.started":"2025-01-01T07:43:52.113323Z","shell.execute_reply":"2025-01-01T07:43:52.122515Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"output","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:43:52.124643Z","iopub.execute_input":"2025-01-01T07:43:52.125016Z","iopub.status.idle":"2025-01-01T07:43:52.14298Z","shell.execute_reply.started":"2025-01-01T07:43:52.124968Z","shell.execute_reply":"2025-01-01T07:43:52.14199Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"output.to_csv('submission.csv')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2025-01-01T07:43:52.14387Z","iopub.execute_input":"2025-01-01T07:43:52.144148Z","iopub.status.idle":"2025-01-01T07:43:53.551572Z","shell.execute_reply.started":"2025-01-01T07:43:52.144123Z","shell.execute_reply":"2025-01-01T07:43:53.550503Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"","metadata":{"trusted":true},"outputs":[],"execution_count":null}]}