{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# Dependencies\nimport numpy as np \nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-01-17T16:02:19.986149Z","iopub.execute_input":"2023-01-17T16:02:19.986619Z","iopub.status.idle":"2023-01-17T16:02:19.992596Z","shell.execute_reply.started":"2023-01-17T16:02:19.986572Z","shell.execute_reply":"2023-01-17T16:02:19.991477Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load Data\ntrain_data = pd.read_feather('/kaggle/input/amexfeather/train_data.ftr')\nprint(\"The intial dataset shape:\", train_data.shape)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:02:23.030894Z","iopub.execute_input":"2023-01-17T16:02:23.031319Z","iopub.status.idle":"2023-01-17T16:02:48.680470Z","shell.execute_reply.started":"2023-01-17T16:02:23.031284Z","shell.execute_reply":"2023-01-17T16:02:48.679341Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data.head(5)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:02:48.682340Z","iopub.execute_input":"2023-01-17T16:02:48.682668Z","iopub.status.idle":"2023-01-17T16:02:48.720188Z","shell.execute_reply.started":"2023-01-17T16:02:48.682638Z","shell.execute_reply":"2023-01-17T16:02:48.719126Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data = train_data.groupby('customer_ID').tail(1).set_index('customer_ID', drop=True).sort_index()\ntrain_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:03:54.542925Z","iopub.execute_input":"2023-01-17T16:03:54.544084Z","iopub.status.idle":"2023-01-17T16:03:57.240670Z","shell.execute_reply.started":"2023-01-17T16:03:54.544032Z","shell.execute_reply":"2023-01-17T16:03:57.239454Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# DATA PREPROCESSING","metadata":{}},{"cell_type":"markdown","source":"**Removing columns with high number of missing data**","metadata":{}},{"cell_type":"code","source":"null_data=pd.DataFrame((train_data.isnull().sum()/len(train_data))*100, columns=['Null %'])\ncols_with_high_null_data = list(null_data[null_data[\"Null %\"]>80].index)\ntrain_data = train_data.drop(cols_with_high_null_data, axis =1)\nprint(\"Columns removed from the dataset:\", cols_with_high_null_data)\nprint(\"Shape without high null value containing columns :\", train_data.shape)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:02.410735Z","iopub.execute_input":"2023-01-17T16:04:02.411175Z","iopub.status.idle":"2023-01-17T16:04:03.113836Z","shell.execute_reply.started":"2023-01-17T16:04:02.411139Z","shell.execute_reply":"2023-01-17T16:04:03.112749Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Removing non-numeric columns**","metadata":{}},{"cell_type":"code","source":"train_data = train_data.drop(['S_2'], axis =1)\nprint('Shape without non-numeric columns : ', train_data.shape)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:08.170882Z","iopub.execute_input":"2023-01-17T16:04:08.171295Z","iopub.status.idle":"2023-01-17T16:04:08.439181Z","shell.execute_reply.started":"2023-01-17T16:04:08.171261Z","shell.execute_reply":"2023-01-17T16:04:08.437991Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Categorical Features**","metadata":{}},{"cell_type":"code","source":"# Run after removing columns to remove D_66\ncategories=[]\nfor categorical_column in train_data.select_dtypes(include=['category']).columns:\n    categories.append(categorical_column)\nprint(\"Identified categorical features : \", categories)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:19.001573Z","iopub.execute_input":"2023-01-17T16:04:19.002005Z","iopub.status.idle":"2023-01-17T16:04:19.013504Z","shell.execute_reply.started":"2023-01-17T16:04:19.001960Z","shell.execute_reply":"2023-01-17T16:04:19.012241Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(categories)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:26.112195Z","iopub.execute_input":"2023-01-17T16:04:26.113164Z","iopub.status.idle":"2023-01-17T16:04:26.119545Z","shell.execute_reply.started":"2023-01-17T16:04:26.113122Z","shell.execute_reply":"2023-01-17T16:04:26.118466Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for col in categories:\n    train_data[col] =  train_data[col].fillna(train_data[col].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:29.300718Z","iopub.execute_input":"2023-01-17T16:04:29.301160Z","iopub.status.idle":"2023-01-17T16:04:29.360312Z","shell.execute_reply.started":"2023-01-17T16:04:29.301124Z","shell.execute_reply":"2023-01-17T16:04:29.359088Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Fill null values**","metadata":{}},{"cell_type":"code","source":"null_columns = train_data.columns[train_data.isna().any()].tolist()\nfor col in null_columns:\n     train_data[col] = train_data[col].fillna(train_data[col].median())","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:34.621506Z","iopub.execute_input":"2023-01-17T16:04:34.621925Z","iopub.status.idle":"2023-01-17T16:04:36.514864Z","shell.execute_reply.started":"2023-01-17T16:04:34.621887Z","shell.execute_reply":"2023-01-17T16:04:36.513824Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Categorical Enconding**","metadata":{}},{"cell_type":"code","source":"train_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:39.320969Z","iopub.execute_input":"2023-01-17T16:04:39.321971Z","iopub.status.idle":"2023-01-17T16:04:39.329515Z","shell.execute_reply.started":"2023-01-17T16:04:39.321912Z","shell.execute_reply":"2023-01-17T16:04:39.328298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import OrdinalEncoder\n\nenc = OrdinalEncoder()\n\ntrain_data[categories] = enc.fit_transform(train_data[categories])","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:42.326043Z","iopub.execute_input":"2023-01-17T16:04:42.326474Z","iopub.status.idle":"2023-01-17T16:04:43.091241Z","shell.execute_reply.started":"2023-01-17T16:04:42.326438Z","shell.execute_reply":"2023-01-17T16:04:43.090286Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:46.010742Z","iopub.execute_input":"2023-01-17T16:04:46.011142Z","iopub.status.idle":"2023-01-17T16:04:46.018129Z","shell.execute_reply.started":"2023-01-17T16:04:46.011109Z","shell.execute_reply":"2023-01-17T16:04:46.017110Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data = pd.read_feather('/kaggle/input/amexfeather/test_data.ftr')\ntest_data = test_data.groupby('customer_ID').tail(1).set_index('customer_ID', drop=True).sort_index()","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:04:56.411324Z","iopub.execute_input":"2023-01-17T16:04:56.411729Z","iopub.status.idle":"2023-01-17T16:05:45.404476Z","shell.execute_reply.started":"2023-01-17T16:04:56.411694Z","shell.execute_reply":"2023-01-17T16:05:45.403179Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import gc\ngc.collect()","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:23.542213Z","iopub.execute_input":"2023-01-17T16:11:23.543424Z","iopub.status.idle":"2023-01-17T16:11:23.691428Z","shell.execute_reply.started":"2023-01-17T16:11:23.543383Z","shell.execute_reply":"2023-01-17T16:11:23.690318Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"null_data_1=pd.DataFrame((test_data.isnull().sum()/len(test_data))*100, columns=['Null %'])\ncols_with_high_null_data_1 = list(null_data_1[null_data_1[\"Null %\"]>80].index)\ntest_data = test_data.drop(cols_with_high_null_data_1, axis =1)\nprint(\"Columns removed from the dataset:\", cols_with_high_null_data_1)\nprint(\"Shape without high null value containing columns :\", test_data.shape)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:27.240817Z","iopub.execute_input":"2023-01-17T16:11:27.241248Z","iopub.status.idle":"2023-01-17T16:11:28.966322Z","shell.execute_reply.started":"2023-01-17T16:11:27.241211Z","shell.execute_reply":"2023-01-17T16:11:28.965095Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data = test_data.drop(['S_2'], axis =1)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:31.601008Z","iopub.execute_input":"2023-01-17T16:11:31.601409Z","iopub.status.idle":"2023-01-17T16:11:32.445934Z","shell.execute_reply.started":"2023-01-17T16:11:31.601377Z","shell.execute_reply":"2023-01-17T16:11:32.444855Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"categories=[]\nfor categorical_column in test_data.select_dtypes(include=['category']).columns:\n    categories.append(categorical_column)\nprint(\"Identified categorical features : \", categories)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:35.120755Z","iopub.execute_input":"2023-01-17T16:11:35.121173Z","iopub.status.idle":"2023-01-17T16:11:35.130763Z","shell.execute_reply.started":"2023-01-17T16:11:35.121137Z","shell.execute_reply":"2023-01-17T16:11:35.129406Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(categories)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:37.380874Z","iopub.execute_input":"2023-01-17T16:11:37.381297Z","iopub.status.idle":"2023-01-17T16:11:37.388243Z","shell.execute_reply.started":"2023-01-17T16:11:37.381261Z","shell.execute_reply":"2023-01-17T16:11:37.387024Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for col in categories:\n    test_data[col] =  test_data[col].fillna(test_data[col].mode()[0])","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:39.315914Z","iopub.execute_input":"2023-01-17T16:11:39.316343Z","iopub.status.idle":"2023-01-17T16:11:39.419090Z","shell.execute_reply.started":"2023-01-17T16:11:39.316306Z","shell.execute_reply":"2023-01-17T16:11:39.417984Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"null_columns = test_data.columns[test_data.isna().any()].tolist()\nfor col in null_columns:\n     test_data[col] = test_data[col].fillna(test_data[col].median())","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:42.576076Z","iopub.execute_input":"2023-01-17T16:11:42.577132Z","iopub.status.idle":"2023-01-17T16:11:46.637069Z","shell.execute_reply.started":"2023-01-17T16:11:42.577087Z","shell.execute_reply":"2023-01-17T16:11:46.635773Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data[categories] = enc.transform(test_data[categories])","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:49.246153Z","iopub.execute_input":"2023-01-17T16:11:49.246581Z","iopub.status.idle":"2023-01-17T16:11:50.321679Z","shell.execute_reply.started":"2023-01-17T16:11:49.246545Z","shell.execute_reply":"2023-01-17T16:11:50.320414Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_data.shape","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:52.130799Z","iopub.execute_input":"2023-01-17T16:11:52.131209Z","iopub.status.idle":"2023-01-17T16:11:52.138000Z","shell.execute_reply.started":"2023-01-17T16:11:52.131172Z","shell.execute_reply":"2023-01-17T16:11:52.136888Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(train_data.isnull().sum().to_string())","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:11:54.225711Z","iopub.execute_input":"2023-01-17T16:11:54.226148Z","iopub.status.idle":"2023-01-17T16:11:54.606676Z","shell.execute_reply.started":"2023-01-17T16:11:54.226110Z","shell.execute_reply":"2023-01-17T16:11:54.605583Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(test_data.isnull().sum().to_string())","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:12:01.161449Z","iopub.execute_input":"2023-01-17T16:12:01.161878Z","iopub.status.idle":"2023-01-17T16:12:01.894345Z","shell.execute_reply.started":"2023-01-17T16:12:01.161843Z","shell.execute_reply":"2023-01-17T16:12:01.892786Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = train_data[[col for col in train_data.columns if col not in ['target']]]\ny = train_data['target']\n# print(\"X shape :\",X.shape)\n# print(\"y shape :\", y.shape)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:12:06.110899Z","iopub.execute_input":"2023-01-17T16:12:06.111327Z","iopub.status.idle":"2023-01-17T16:12:06.410661Z","shell.execute_reply.started":"2023-01-17T16:12:06.111293Z","shell.execute_reply":"2023-01-17T16:12:06.409148Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Train Model","metadata":{}},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\nx_train,x_test,y_train,y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:12:10.780832Z","iopub.execute_input":"2023-01-17T16:12:10.781271Z","iopub.status.idle":"2023-01-17T16:12:12.145958Z","shell.execute_reply.started":"2023-01-17T16:12:10.781234Z","shell.execute_reply":"2023-01-17T16:12:12.144701Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"columns = [col for col in train_data.columns if col not in ['target']]","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:12:16.680566Z","iopub.execute_input":"2023-01-17T16:12:16.681259Z","iopub.status.idle":"2023-01-17T16:12:16.686061Z","shell.execute_reply.started":"2023-01-17T16:12:16.681223Z","shell.execute_reply":"2023-01-17T16:12:16.684990Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Random Forest Classifier**","metadata":{}},{"cell_type":"code","source":"from sklearn.ensemble import RandomForestClassifier\nrf_model = RandomForestClassifier()\nrf_model.fit(x_train, y_train)\npredictions_2= rf_model.predict_proba(test_data[columns])\n","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:12:21.670906Z","iopub.execute_input":"2023-01-17T16:12:21.671369Z","iopub.status.idle":"2023-01-17T16:12:42.704798Z","shell.execute_reply.started":"2023-01-17T16:12:21.671327Z","shell.execute_reply":"2023-01-17T16:12:42.702980Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_dataset = pd.read_csv('/kaggle/input/amex-default-prediction/sample_submission.csv')\noutput = pd.DataFrame({'customer_ID': sample_dataset.customer_ID, 'prediction': predictions_2[:, 1]})\noutput.to_csv('submission_RFC.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T08:58:18.193188Z","iopub.execute_input":"2023-01-17T08:58:18.194146Z","iopub.status.idle":"2023-01-17T08:58:22.512929Z","shell.execute_reply.started":"2023-01-17T08:58:18.194106Z","shell.execute_reply":"2023-01-17T08:58:22.511502Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**KNN**","metadata":{}},{"cell_type":"code","source":"from sklearn.neighbors import KNeighborsClassifier\n\nknn = KNeighborsClassifier(n_neighbors=3)\nknn.fit(x_train, y_train)\npredictions_3= knn.predict_proba(test_data[columns])","metadata":{"execution":{"iopub.status.busy":"2023-01-17T08:58:22.514520Z","iopub.execute_input":"2023-01-17T08:58:22.515232Z","iopub.status.idle":"2023-01-17T10:53:48.619412Z","shell.execute_reply.started":"2023-01-17T08:58:22.515191Z","shell.execute_reply":"2023-01-17T10:53:48.617808Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\" k = {} , score = {} \".format(3,knn.score(x_test,y_test)))","metadata":{"execution":{"iopub.status.busy":"2023-01-17T10:53:48.621610Z","iopub.execute_input":"2023-01-17T10:53:48.622040Z","iopub.status.idle":"2023-01-17T11:05:27.494452Z","shell.execute_reply.started":"2023-01-17T10:53:48.621976Z","shell.execute_reply":"2023-01-17T11:05:27.493294Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_dataset = pd.read_csv('/kaggle/input/amex-default-prediction/sample_submission.csv')\noutput = pd.DataFrame({'customer_ID': sample_dataset.customer_ID, 'prediction': predictions_3[:, 1]})\noutput.to_csv('submission_KNN.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T11:05:27.496209Z","iopub.execute_input":"2023-01-17T11:05:27.496525Z","iopub.status.idle":"2023-01-17T11:05:31.341044Z","shell.execute_reply.started":"2023-01-17T11:05:27.496495Z","shell.execute_reply":"2023-01-17T11:05:31.340120Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**SVM**","metadata":{}},{"cell_type":"code","source":"from sklearn import svm\n\nmodel = svm.SVC()\nmodel.fit(x_train, y_train)\npredictions_4=model.predict_proba(test_data[columns])\n","metadata":{"execution":{"iopub.status.busy":"2023-01-17T11:05:31.342487Z","iopub.execute_input":"2023-01-17T11:05:31.343038Z","iopub.status.idle":"2023-01-17T13:43:11.647427Z","shell.execute_reply.started":"2023-01-17T11:05:31.342981Z","shell.execute_reply":"2023-01-17T13:43:11.642746Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_dataset = pd.read_csv('/kaggle/input/amex-default-prediction/sample_submission.csv')\noutput = pd.DataFrame({'customer_ID': sample_dataset.customer_ID, 'prediction': predictions_4[:, 1]})\noutput.to_csv('submission_SVM.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T13:43:11.651101Z","iopub.status.idle":"2023-01-17T13:43:11.651680Z","shell.execute_reply.started":"2023-01-17T13:43:11.651437Z","shell.execute_reply":"2023-01-17T13:43:11.651461Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**XGB Boost**","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBClassifier\n\nmodel = XGBClassifier()\nmodel.fit(x_train, y_train)\npredictions_5= model.predict_proba(test_data[columns])","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:12:50.470625Z","iopub.execute_input":"2023-01-17T16:12:50.471400Z","iopub.status.idle":"2023-01-17T16:17:01.789650Z","shell.execute_reply.started":"2023-01-17T16:12:50.471358Z","shell.execute_reply":"2023-01-17T16:17:01.788641Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ypredict= model.predict(x_test)\naccuracy_score(y_test, ypredict)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T16:44:10.186898Z","iopub.execute_input":"2023-01-17T16:44:10.187437Z","iopub.status.idle":"2023-01-17T16:44:10.522882Z","shell.execute_reply.started":"2023-01-17T16:44:10.187399Z","shell.execute_reply":"2023-01-17T16:44:10.521540Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_dataset = pd.read_csv('/kaggle/input/amex-default-prediction/sample_submission.csv')\noutput = pd.DataFrame({'customer_ID': sample_dataset.customer_ID, 'prediction': predictions_5[:, 1]})\noutput.to_csv('submission_XGB.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T14:07:05.515677Z","iopub.execute_input":"2023-01-17T14:07:05.516387Z","iopub.status.idle":"2023-01-17T14:07:09.789922Z","shell.execute_reply.started":"2023-01-17T14:07:05.516339Z","shell.execute_reply":"2023-01-17T14:07:09.788537Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# y_predict_final = test_prediction[:,1]\n# test = test.reset_index()\n\n# submission = pd.DataFrame({\"customer_ID\":test.customer_ID,\"prediction\":y_predict_final})\n\n# submission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-01-17T13:43:11.657927Z","iopub.status.idle":"2023-01-17T13:43:11.658309Z","shell.execute_reply.started":"2023-01-17T13:43:11.658129Z","shell.execute_reply":"2023-01-17T13:43:11.658145Z"},"trusted":true},"execution_count":null,"outputs":[]}]}