{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.10.14"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":81933,"databundleVersionId":9643020,"sourceType":"competition"}],"dockerImageVersionId":30804,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"Hello Kaggle community,\nThis time, I'm going to try a new notebook to practice my data science skills. There are fewer than 20 days until this competition ends, which is why I believe this will be my final notebook for this challenge. I want to give my best effort to complete the project with good results or at least gather valuable insights. I believe thata this kind of work helps us uncover the truth behind the data.\n\nI apologize if you find any code or comments that are not in english. My native language is Spanish, and I'm still working on improving my English proficiency.","metadata":{}},{"cell_type":"markdown","source":"# Functions\nI want to work on this project in a modular way, which is why I plan to try to do most of it using functions.","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport matplotlib.pyplot  as plt\nimport seaborn as sns\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:00.586505Z","iopub.execute_input":"2024-12-09T20:26:00.586977Z","iopub.status.idle":"2024-12-09T20:26:01.808854Z","shell.execute_reply.started":"2024-12-09T20:26:00.586934Z","shell.execute_reply":"2024-12-09T20:26:01.807788Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def distr_hist(df , ancho = 20, alto = 15):\n    # Crear histogramas para todas las variables del DataFrame\n    df.hist(bins=10, figsize=(ancho, alto))\n    plt.tight_layout()\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:01.810814Z","iopub.execute_input":"2024-12-09T20:26:01.811393Z","iopub.status.idle":"2024-12-09T20:26:01.816946Z","shell.execute_reply.started":"2024-12-09T20:26:01.811352Z","shell.execute_reply":"2024-12-09T20:26:01.815788Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def contar_nulos(df):\n    #Contar valores  nulos por cada variable\n    null_counts = df.isnull().sum()\n    print(null_counts)\n    print()\n    print(10*'-',\"Archivo Generado\",10*'-')\n    null_counts.to_excel('/kaggle/working/null_counts.xlsx')","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:01.818283Z","iopub.execute_input":"2024-12-09T20:26:01.818788Z","iopub.status.idle":"2024-12-09T20:26:01.831371Z","shell.execute_reply.started":"2024-12-09T20:26:01.818741Z","shell.execute_reply":"2024-12-09T20:26:01.830226Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def graficar_nulos(df, ancho = 20, alto = 7):\n    null_counts = df.isnull().sum()\n    null_counts.plot(kind='bar',figsize=(ancho,alto))\n    plt.xlabel('variables')\n    plt.ylabel('numero de datos nulos')\n    plt.title('Datos nulos por variable')\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:01.832677Z","iopub.execute_input":"2024-12-09T20:26:01.833061Z","iopub.status.idle":"2024-12-09T20:26:01.844180Z","shell.execute_reply.started":"2024-12-09T20:26:01.833022Z","shell.execute_reply":"2024-12-09T20:26:01.843045Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\n\ndef eliminar_nulos(df, metodo=\"drop\", valor=None):\n    if metodo == \"drop\":\n        # Elimina filas con valores nulos\n        return df.dropna()\n    \n    elif metodo == \"fill\":\n        if valor is None:\n            # Rellenar con la media de las columnas numéricas y la moda de las categóricas\n            for columna in df.columns:\n                if df[columna].dtype == 'object' or df[columna].dtype == 'category':  # Categórica\n                    df[columna] = df[columna].fillna(df[columna].mode()[0])\n                else:  # Numérica\n                    df[columna] = df[columna].fillna(df[columna].mean())\n        else:\n            # Rellena valores nulos con el valor proporcionado\n            return df.fillna(valor)\n        return df\n    else:\n        raise ValueError(\"Método no válido. Use 'drop' o 'fill'.\")\n","metadata":{"execution":{"iopub.status.busy":"2024-12-09T21:00:34.074064Z","iopub.execute_input":"2024-12-09T21:00:34.074948Z","iopub.status.idle":"2024-12-09T21:00:34.082102Z","shell.execute_reply.started":"2024-12-09T21:00:34.074892Z","shell.execute_reply":"2024-12-09T21:00:34.080834Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"\ndef distribution(df, feature1, feature2):\n    # Crear una tabla cruzada para contar las categorías por género\n    tabla = pd.crosstab(df[feature1], df[feature2])\n    # Crear el gráfico\n    tabla.plot(kind='bar', figsize=(10, 6), alpha=0.7)\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:01.856144Z","iopub.execute_input":"2024-12-09T20:26:01.856495Z","iopub.status.idle":"2024-12-09T20:26:01.871709Z","shell.execute_reply.started":"2024-12-09T20:26:01.856464Z","shell.execute_reply":"2024-12-09T20:26:01.870614Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nimport matplotlib.pyplot as plt\n\n# Función corregida para manejar datos faltantes\ndef kmeans_clustering(df, num_var1, num_var2, n_clusters=3):\n    # Copia del DataFrame para no modificar el original\n    df_copy = df.copy()\n    \n    # Asegurarnos de que las columnas categóricas sean uniformemente 'str'\n    for col in df_copy.select_dtypes(include=['object', 'category']).columns:\n        df_copy[col] = df_copy[col].astype(str)\n        df_copy[col] = LabelEncoder().fit_transform(df_copy[col])\n    \n    # Escalar los datos\n    scaler = StandardScaler()\n    scaled_data = scaler.fit_transform(df_copy.fillna(0))  # Imputación solo para el modelo, no para el gráfico\n    \n    # Aplicar K-means\n    kmeans = KMeans(n_clusters=n_clusters, random_state=42)\n    df_copy['Cluster'] = kmeans.fit_predict(scaled_data)\n    \n    # Filtrar datos faltantes solo en las columnas seleccionadas para el gráfico\n    df_filtered = df_copy.dropna(subset=[num_var1, num_var2])\n    \n    for cluster in df_filtered['Cluster'].unique():\n        cluster_data = df_filtered[df_filtered['Cluster'] == cluster]\n        plt.scatter(\n            cluster_data[num_var1],\n            cluster_data[num_var2],\n            label=f'Cluster {cluster}',\n            alpha=0.7\n        )\n    plt.legend()\n    plt.xlabel(num_var1)\n    plt.ylabel(num_var2)\n    plt.title(f'K-means clustering ({num_var1} vs {num_var2})')\n    plt.show()\n    \n    return df_filtered\n\n#print(clustered_df)","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:01.873089Z","iopub.execute_input":"2024-12-09T20:26:01.873403Z","iopub.status.idle":"2024-12-09T20:26:02.357127Z","shell.execute_reply.started":"2024-12-09T20:26:01.873372Z","shell.execute_reply":"2024-12-09T20:26:02.356042Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def boxplot_by_category(df, num_var, cat_var):\n    plt.figure(figsize=(8, 6))\n    sns.boxplot(x=cat_var, y=num_var, data=df)\n    plt.title(f'Boxplot of {num_var} by {cat_var}')\n    plt.xlabel(cat_var)\n    plt.ylabel(num_var)\n    plt.show()","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:02.358536Z","iopub.execute_input":"2024-12-09T20:26:02.358963Z","iopub.status.idle":"2024-12-09T20:26:02.364626Z","shell.execute_reply.started":"2024-12-09T20:26:02.358926Z","shell.execute_reply":"2024-12-09T20:26:02.363512Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def cambiar_tipo_dato(df, tipos_dict):\n    for columna, tipo in tipos_dict.items():\n        df[columna] = df[columna].astype(tipo, errors='ignore')\n    return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T21:57:25.411334Z","iopub.execute_input":"2024-12-09T21:57:25.411698Z","iopub.status.idle":"2024-12-09T21:57:25.417129Z","shell.execute_reply.started":"2024-12-09T21:57:25.411666Z","shell.execute_reply":"2024-12-09T21:57:25.416014Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"def conver_cat_a_num(df):\n    # Iterar sobre las columnas del DataFrame\n    for columna in df.select_dtypes(include=['object', 'category']).columns:\n            df[columna] = df[columna].astype('category').cat.codes\n    return df","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T21:41:39.371060Z","iopub.execute_input":"2024-12-09T21:41:39.371476Z","iopub.status.idle":"2024-12-09T21:41:39.377503Z","shell.execute_reply.started":"2024-12-09T21:41:39.371442Z","shell.execute_reply":"2024-12-09T21:41:39.376312Z"}},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Brief analytics","metadata":{}},{"cell_type":"markdown","source":"## Distribution","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv(\"/kaggle/input/child-mind-institute-problematic-internet-use/train.csv\")","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:02.365951Z","iopub.execute_input":"2024-12-09T20:26:02.366279Z","iopub.status.idle":"2024-12-09T20:26:02.459229Z","shell.execute_reply.started":"2024-12-09T20:26:02.366247Z","shell.execute_reply":"2024-12-09T20:26:02.457647Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"distribution(train,'sii','Basic_Demos-Sex')","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:02.460394Z","iopub.execute_input":"2024-12-09T20:26:02.460752Z","iopub.status.idle":"2024-12-09T20:26:02.764380Z","shell.execute_reply.started":"2024-12-09T20:26:02.460691Z","shell.execute_reply":"2024-12-09T20:26:02.763285Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"## K-means.","metadata":{}},{"cell_type":"code","source":"clustered_df = kmeans_clustering(train, num_var1='Physical-BMI', num_var2='PCIAT-PCIAT_Total', n_clusters=5)","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:02.765862Z","iopub.execute_input":"2024-12-09T20:26:02.766300Z","iopub.status.idle":"2024-12-09T20:26:04.686142Z","shell.execute_reply.started":"2024-12-09T20:26:02.766253Z","shell.execute_reply":"2024-12-09T20:26:04.684961Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"boxplot_by_category(train,'Physical-BMI','sii')","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:04.687955Z","iopub.execute_input":"2024-12-09T20:26:04.688309Z","iopub.status.idle":"2024-12-09T20:26:04.923856Z","shell.execute_reply.started":"2024-12-09T20:26:04.688276Z","shell.execute_reply":"2024-12-09T20:26:04.922761Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"boxplot_by_category(train,'Physical-Height','sii')","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:04.925364Z","iopub.execute_input":"2024-12-09T20:26:04.925874Z","iopub.status.idle":"2024-12-09T20:26:05.113828Z","shell.execute_reply.started":"2024-12-09T20:26:04.925825Z","shell.execute_reply":"2024-12-09T20:26:05.112708Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"boxplot_by_category(train,'Physical-Weight','sii')","metadata":{"execution":{"iopub.status.busy":"2024-12-09T20:26:05.117002Z","iopub.execute_input":"2024-12-09T20:26:05.117387Z","iopub.status.idle":"2024-12-09T20:26:05.420921Z","shell.execute_reply.started":"2024-12-09T20:26:05.117351Z","shell.execute_reply":"2024-12-09T20:26:05.419766Z"},"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"markdown","source":"# Training the model","metadata":{}},{"cell_type":"code","source":"columns_to_remove = ['id',\n                    'PCIAT-PCIAT_Total',\n                     'PCIAT-Season',\n                     'PCIAT-PCIAT_01',\n                     'PCIAT-PCIAT_02',\n                     'PCIAT-PCIAT_03',\n                     'PCIAT-PCIAT_04',\n                     'PCIAT-PCIAT_05',\n                     'PCIAT-PCIAT_06',\n                     'PCIAT-PCIAT_07',\n                     'PCIAT-PCIAT_08',\n                     'PCIAT-PCIAT_09',\n                     'PCIAT-PCIAT_10',\n                     'PCIAT-PCIAT_11',\n                     'PCIAT-PCIAT_12',\n                     'PCIAT-PCIAT_13',\n                     'PCIAT-PCIAT_14',\n                     'PCIAT-PCIAT_15',\n                     'PCIAT-PCIAT_16',\n                     'PCIAT-PCIAT_17',\n                     'PCIAT-PCIAT_18',\n                     'PCIAT-PCIAT_19',\n                     'PCIAT-PCIAT_20']\n\ntrain_data = train.copy()\n\ntrain_data = train_data.drop(columns=columns_to_remove)\ntrain_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T21:24:18.430675Z","iopub.execute_input":"2024-12-09T21:24:18.431131Z","iopub.status.idle":"2024-12-09T21:24:18.467080Z","shell.execute_reply.started":"2024-12-09T21:24:18.431074Z","shell.execute_reply":"2024-12-09T21:24:18.465884Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"tipos_dict = {\n    'Basic_Demos-Enroll_Season': 'category',\n    'Basic_Demos-Age': 'float',\n    'Basic_Demos-Sex': 'category',\n    'CGAS-Season': 'category',\n    'CGAS-CGAS_Score': 'int',\n    'Physical-Season': 'category',\n    'Physical-BMI': 'float',\n    'Physical-Height': 'float',\n    'Physical-Weight': 'float',\n    'Physical-Waist_Circumference': 'int',\n    'Physical-Diastolic_BP': 'int',\n    'Physical-HeartRate': 'int',\n    'Physical-Systolic_BP': 'int',\n    'Fitness_Endurance-Season': 'category',\n    'Fitness_Endurance-Max_Stage': 'int',\n    'Fitness_Endurance-Time_Mins': 'int',\n    'Fitness_Endurance-Time_Sec': 'int',\n    'FGC-Season': 'category',\n    'FGC-FGC_CU': 'int',\n    'FGC-FGC_CU_Zone': 'category',\n    'FGC-FGC_GSND': 'float',\n    'FGC-FGC_GSND_Zone': 'category',\n    'FGC-FGC_GSD': 'float',\n    'FGC-FGC_GSD_Zone': 'category',\n    'FGC-FGC_PU': 'int',\n    'FGC-FGC_PU_Zone': 'category',\n    'FGC-FGC_SRL': 'float',\n    'FGC-FGC_SRL_Zone': 'category',\n    'FGC-FGC_SRR': 'float',\n    'FGC-FGC_SRR_Zone': 'category',\n    'FGC-FGC_TL': 'int',\n    'FGC-FGC_TL_Zone': 'category',\n    'BIA-Season': 'category',\n    'BIA-BIA_Activity_Level_num': 'category',\n    'BIA-BIA_BMC': 'float',\n    'BIA-BIA_BMI': 'float',\n    'BIA-BIA_BMR': 'float',\n    'BIA-BIA_DEE': 'float',\n    'BIA-BIA_ECW': 'float',\n    'BIA-BIA_FFM': 'float',\n    'BIA-BIA_FFMI': 'float',\n    'BIA-BIA_FMI': 'float',\n    'BIA-BIA_Fat': 'float',\n    'BIA-BIA_Frame_num': 'category',\n    'BIA-BIA_ICW': 'float',\n    'BIA-BIA_LDM': 'float',\n    'BIA-BIA_LST': 'float',\n    'BIA-BIA_SMM': 'float',\n    'BIA-BIA_TBW': 'float',\n    'PAQ_A-Season': 'category',\n    'PAQ_A-PAQ_A_Total': 'float',\n    'PAQ_C-Season': 'category',\n    'PAQ_C-PAQ_C_Total': 'float',\n    'SDS-Season': 'category',\n    'SDS-SDS_Total_Raw': 'int',\n    'SDS-SDS_Total_T': 'int',\n    'PreInt_EduHx-Season': 'category',\n    'PreInt_EduHx-computerinternet_hoursday': 'category',\n    'sii': 'category'\n}\n\n\ntrain_data = cambiar_tipo_dato(train_data, tipos_dict)\n\n# Ver los tipos de datos después de la conversión\n# print(train_data.dtypes)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T21:52:56.455957Z","iopub.execute_input":"2024-12-09T21:52:56.456362Z","iopub.status.idle":"2024-12-09T21:52:56.491498Z","shell.execute_reply.started":"2024-12-09T21:52:56.456326Z","shell.execute_reply":"2024-12-09T21:52:56.490400Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"eliminar_nulos(train_data, metodo=\"fill\", valor=None)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T21:24:30.868654Z","iopub.execute_input":"2024-12-09T21:24:30.869597Z","iopub.status.idle":"2024-12-09T21:24:30.926262Z","shell.execute_reply.started":"2024-12-09T21:24:30.869558Z","shell.execute_reply":"2024-12-09T21:24:30.925165Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"train_data = conver_cat_a_num(train_data)\ntrain_data","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T21:41:52.156508Z","iopub.execute_input":"2024-12-09T21:41:52.157355Z","iopub.status.idle":"2024-12-09T21:41:52.195812Z","shell.execute_reply.started":"2024-12-09T21:41:52.157314Z","shell.execute_reply":"2024-12-09T21:41:52.194694Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"from sklearn.ensemble import AdaBoostClassifier\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\n\nfrom sklearn.metrics import accuracy_score\n\nX = train_data.iloc[:, :-1]  # Todas las columnas excepto la última\ny = train_data.iloc[:, -1]   # Solo la última columna\n\n# Dividimos los datos en entrenamiento y prueba\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)\n\n# Creamos el modelo AdaBoost con random_state\nmodel = AdaBoostClassifier(base_estimator=DecisionTreeClassifier(max_depth=1), n_estimators=50, random_state=0, learning_rate=1.0)\n\n# Entrenamos el modelo\nmodel.fit(X_train, y_train)\n\n# Predicciones\ny_pred = model.predict(X_test)\n\n# Evaluación\naccuracy = accuracy_score(y_test, y_pred)\nprint(f'Accuracy: {accuracy * 100:.2f}%')\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T22:01:40.072918Z","iopub.execute_input":"2024-12-09T22:01:40.073305Z","iopub.status.idle":"2024-12-09T22:01:40.554014Z","shell.execute_reply.started":"2024-12-09T22:01:40.073272Z","shell.execute_reply":"2024-12-09T22:01:40.552936Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test = pd.read_csv('/kaggle/input/child-mind-institute-problematic-internet-use/test.csv')","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_data = test.copy()\ntest_data = test_data.drop(columns='id')","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T22:01:45.233627Z","iopub.execute_input":"2024-12-09T22:01:45.234039Z","iopub.status.idle":"2024-12-09T22:01:45.240481Z","shell.execute_reply.started":"2024-12-09T22:01:45.234003Z","shell.execute_reply":"2024-12-09T22:01:45.239408Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"del tipos_dict['sii']","metadata":{"trusted":true},"outputs":[],"execution_count":null},{"cell_type":"code","source":"test_data = cambiar_tipo_dato(test_data, tipos_dict)\n\neliminar_nulos(test_data,\"fill\")\n\nconver_cat_a_num(test_data)\n","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T22:01:50.335781Z","iopub.execute_input":"2024-12-09T22:01:50.336192Z","iopub.status.idle":"2024-12-09T22:01:50.423423Z","shell.execute_reply.started":"2024-12-09T22:01:50.336157Z","shell.execute_reply":"2024-12-09T22:01:50.422382Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"predicciones = model.predict(test_data)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T22:06:11.286851Z","iopub.execute_input":"2024-12-09T22:06:11.287239Z","iopub.status.idle":"2024-12-09T22:06:11.305412Z","shell.execute_reply.started":"2024-12-09T22:06:11.287206Z","shell.execute_reply":"2024-12-09T22:06:11.304498Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_predicciones = pd.DataFrame({\n        'id': test['id'].values,  # Obtener la columna de IDs\n        'sii': predicciones         # Colocar las predicciones\n    })","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T22:12:14.833464Z","iopub.execute_input":"2024-12-09T22:12:14.833991Z","iopub.status.idle":"2024-12-09T22:12:14.869227Z","shell.execute_reply.started":"2024-12-09T22:12:14.833951Z","shell.execute_reply":"2024-12-09T22:12:14.867442Z"}},"outputs":[],"execution_count":null},{"cell_type":"code","source":"df_predicciones.to_csv('/kaggle/working/submission.csv',index=False)","metadata":{"trusted":true,"execution":{"iopub.status.busy":"2024-12-09T22:15:04.463114Z","iopub.execute_input":"2024-12-09T22:15:04.464056Z","iopub.status.idle":"2024-12-09T22:15:04.472052Z","shell.execute_reply.started":"2024-12-09T22:15:04.464000Z","shell.execute_reply":"2024-12-09T22:15:04.470828Z"}},"outputs":[],"execution_count":null}]}