{"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":"import pandas as pd\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport seaborn as sns\n\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OrdinalEncoder, StandardScaler, OneHotEncoder\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.metrics import precision_score, recall_score, f1_score, accuracy_score\n\nfrom sklearn.ensemble import RandomForestClassifier\n","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:13.715902Z","iopub.execute_input":"2022-07-07T21:52:13.716783Z","iopub.status.idle":"2022-07-07T21:52:15.409342Z","shell.execute_reply.started":"2022-07-07T21:52:13.716646Z","shell.execute_reply":"2022-07-07T21:52:15.408249Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Ushbu maʼlumotlar toʻplami aviakompaniya yoʻlovchilarining qoniqish soʻrovini oʻz ichiga oladi. Yo'lovchilarning qoniqishini bashorat qila olasizmi?\n\nTarkib:\n\nGender: yo'lovchilarning jinsi (ayol, erkak)\n\nCustomer Type: mijoz turi (sodiq mijoz, ishonchsiz mijoz)\n\nAge: yo'lovchilarning haqiqiy yoshi\n\nType of Travel: yo'lovchilar parvozining maqsadi (shaxsiy sayohat, biznes sayohat)\n\nClass: yo'lovchilar samolyotida sayohat klassi (Business, Eco, Eco Plus)\n\nFlight distance: Ushbu sayohatning parvoz masofasi\n\nInflight wifi service: Parvoz ichidagi Wi-Fi xizmatidan qoniqish darajasi (0: Tegishli emas; 1-5)\n\nDeparture/Arrival time convenient: Ketish/Kelish vaqtining qoniqish darajasi\n\nEase of Online booking: Onlayn bron qilishdan qoniqish darajasi\n\nGate location: Darvoza joylashuvidan qoniqish darajasi\n\nFood and drink: Oziq-ovqat va ichimlikdan qoniqish darajasi\n\nOnline boarding: Onlayn bortdan qoniqish darajasi\n\nSeat comfort: O'rindiqning qulayligidan qoniqish darajasi\n\nInflight entertainment: Parvoz ichidagi o'yin-kulgidan qoniqish darajasi\n\nOn-board service: Bort xizmatidan qoniqish darajasi\n\nLeg room service: oyoq xonasi xizmatidan qoniqish darajasi\n\nBaggage handling: bagajni tashishdan qoniqish darajasi\n\nCheck-in service: Ro'yxatdan o'tish xizmatidan qoniqish darajasi\n\nInflight service: Parvoz ichidagi xizmatdan qoniqish darajasi\n\nCleanliness: Tozalikdan qoniqish darajasi\n\nDeparture Delay in Minutes: jo‘nash vaqtida kechikish daqiqalari\n\nArrival Delay in Minutes: yetib kelganda kechikish daqiqalari\n\nSatisfaction: Aviakompaniyaning qoniqish darajasi (qoniqish, neytral yoki norozilik)","metadata":{}},{"cell_type":"code","source":"url=\"../input/aviakompaniya/train_dataset.csv\"\ndf = pd.read_csv(url)\ndf.sample(5)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:19.103727Z","iopub.execute_input":"2022-07-07T21:52:19.104175Z","iopub.status.idle":"2022-07-07T21:52:19.198642Z","shell.execute_reply.started":"2022-07-07T21:52:19.104141Z","shell.execute_reply":"2022-07-07T21:52:19.197933Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"url=\"../input/aviakompaniya/test_dataset.csv\"\ndf_test = pd.read_csv(url)\ndf_test.sample(5).T","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:21.105707Z","iopub.execute_input":"2022-07-07T21:52:21.106115Z","iopub.status.idle":"2022-07-07T21:52:21.149137Z","shell.execute_reply.started":"2022-07-07T21:52:21.106082Z","shell.execute_reply":"2022-07-07T21:52:21.148489Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Correlation matrix\ndf.corr().abs().sort_values(by='satisfaction', ascending=False).style.background_gradient(cmap='coolwarm')","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:24.495783Z","iopub.execute_input":"2022-07-07T21:52:24.496282Z","iopub.status.idle":"2022-07-07T21:52:24.639850Z","shell.execute_reply.started":"2022-07-07T21:52:24.496253Z","shell.execute_reply":"2022-07-07T21:52:24.638920Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.head().T","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:27.275193Z","iopub.execute_input":"2022-07-07T21:52:27.275900Z","iopub.status.idle":"2022-07-07T21:52:27.295702Z","shell.execute_reply.started":"2022-07-07T21:52:27.275841Z","shell.execute_reply":"2022-07-07T21:52:27.294985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:30.043800Z","iopub.execute_input":"2022-07-07T21:52:30.044168Z","iopub.status.idle":"2022-07-07T21:52:30.057774Z","shell.execute_reply.started":"2022-07-07T21:52:30.044138Z","shell.execute_reply":"2022-07-07T21:52:30.056850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# fill missing values for Arrival Delay in Minutes column\ndf['Arrival Delay in Minutes'].fillna(df['Arrival Delay in Minutes'].mean(), inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:48.260558Z","iopub.execute_input":"2022-07-07T21:52:48.261074Z","iopub.status.idle":"2022-07-07T21:52:48.268210Z","shell.execute_reply.started":"2022-07-07T21:52:48.261029Z","shell.execute_reply":"2022-07-07T21:52:48.267260Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df.describe().style.background_gradient(cmap='coolwarm')","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:50.558707Z","iopub.execute_input":"2022-07-07T21:52:50.559716Z","iopub.status.idle":"2022-07-07T21:52:50.656354Z","shell.execute_reply.started":"2022-07-07T21:52:50.559669Z","shell.execute_reply":"2022-07-07T21:52:50.655456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"f,ax = plt.subplots(2,3,figsize=(20,10))\nsns.regplot(x='Online boarding', y='satisfaction', data=df, ax=ax[0,0])\nsns.regplot(x='Inflight entertainment', y='satisfaction', data=df, ax=ax[0,1])\nsns.regplot(x='Flight Distance', y='satisfaction', data=df, ax=ax[0,2])\nsns.regplot(x='Seat comfort', y='satisfaction', data=df, ax=ax[1,0])\nsns.regplot(x='On-board service', y='satisfaction', data=df, ax=ax[1,1])\nsns.regplot(x='Leg room service', y='satisfaction', data=df, ax=ax[1,2])\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:52:51.809824Z","iopub.execute_input":"2022-07-07T21:52:51.810459Z","iopub.status.idle":"2022-07-07T21:52:56.905738Z","shell.execute_reply.started":"2022-07-07T21:52:51.810425Z","shell.execute_reply":"2022-07-07T21:52:56.904752Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Drop Id column\ndf.drop('id', axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:53:05.060063Z","iopub.execute_input":"2022-07-07T21:53:05.060479Z","iopub.status.idle":"2022-07-07T21:53:05.068623Z","shell.execute_reply.started":"2022-07-07T21:53:05.060445Z","shell.execute_reply":"2022-07-07T21:53:05.067793Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\ntrain_set, test_set, train_labels, test_labels = train_test_split(df.drop('satisfaction', axis=1), df['satisfaction'], test_size=0.2, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:53:08.804066Z","iopub.execute_input":"2022-07-07T21:53:08.804810Z","iopub.status.idle":"2022-07-07T21:53:08.816614Z","shell.execute_reply.started":"2022-07-07T21:53:08.804764Z","shell.execute_reply":"2022-07-07T21:53:08.815497Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_num = train_set.select_dtypes(include=[np.number]).columns\ncat = train_set.select_dtypes(include=[np.object]).columns\n\nnum_pipeline = Pipeline([\n          ('std_scaler', StandardScaler())             \n])\nnum_attribs = list(X_num)\ncat_attribs = cat\n\nfull_pipeline = ColumnTransformer([\n    ('num', num_pipeline, num_attribs),\n    ('cat', OneHotEncoder(), cat_attribs)\n])","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:53:14.884032Z","iopub.execute_input":"2022-07-07T21:53:14.884486Z","iopub.status.idle":"2022-07-07T21:53:14.895891Z","shell.execute_reply.started":"2022-07-07T21:53:14.884442Z","shell.execute_reply":"2022-07-07T21:53:14.894866Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_prepared = full_pipeline.fit_transform(train_set)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:53:16.711618Z","iopub.execute_input":"2022-07-07T21:53:16.712499Z","iopub.status.idle":"2022-07-07T21:53:16.741104Z","shell.execute_reply.started":"2022-07-07T21:53:16.712461Z","shell.execute_reply":"2022-07-07T21:53:16.740069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# find the best k value with for loop and draw a graph\nk_range = list(range(1, 24))\nscores = []\nfor k in k_range:\n    knn = KNeighborsClassifier(n_neighbors=k)\n    knn.fit(X_prepared, train_labels)\n    y_pred = knn.predict(full_pipeline.transform(test_set))\n    scores.append(f1_score(test_labels, y_pred))\n\nplt.figure(figsize=(10, 6))\nplt.plot(k_range, scores, color='red', marker='o', linestyle='solid')\nplt.xticks(k_range)\nplt.xlabel('Value of K for KNN')\nplt.ylabel('F1 Score')\nplt.grid()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:53:29.687709Z","iopub.execute_input":"2022-07-07T21:53:29.688430Z","iopub.status.idle":"2022-07-07T21:53:40.125559Z","shell.execute_reply.started":"2022-07-07T21:53:29.688392Z","shell.execute_reply":"2022-07-07T21:53:40.124623Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(10, 6))\nplt.plot(k_range, scores, color='red', linestyle='solid', marker='o', markerfacecolor='blue', markersize=10)\nplt.xlabel('Value of K for KNN')\nplt.ylabel('Cross-Validated Accuracy')\nplt.xticks(k_range)\nplt.grid(which='major', linestyle='-', linewidth='0.5', color='red')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:53:54.744440Z","iopub.execute_input":"2022-07-07T21:53:54.744850Z","iopub.status.idle":"2022-07-07T21:53:55.019607Z","shell.execute_reply.started":"2022-07-07T21:53:54.744815Z","shell.execute_reply":"2022-07-07T21:53:55.018679Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"knn = KNeighborsClassifier(n_neighbors=3)\nknn.fit(X_prepared, train_labels)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:00.231886Z","iopub.execute_input":"2022-07-07T21:54:00.233001Z","iopub.status.idle":"2022-07-07T21:54:00.244997Z","shell.execute_reply.started":"2022-07-07T21:54:00.232938Z","shell.execute_reply":"2022-07-07T21:54:00.243882Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred = knn.predict(full_pipeline.transform(test_set))","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:10.014849Z","iopub.execute_input":"2022-07-07T21:54:10.015570Z","iopub.status.idle":"2022-07-07T21:54:10.376822Z","shell.execute_reply.started":"2022-07-07T21:54:10.015531Z","shell.execute_reply":"2022-07-07T21:54:10.376074Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import jaccard_score\n\njaccard_score(test_labels, y_pred)\n","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:11.308414Z","iopub.execute_input":"2022-07-07T21:54:11.309134Z","iopub.status.idle":"2022-07-07T21:54:11.317115Z","shell.execute_reply.started":"2022-07-07T21:54:11.309097Z","shell.execute_reply":"2022-07-07T21:54:11.316172Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Confusion matrix for the test set\nfrom sklearn.metrics import confusion_matrix\nsns.heatmap(confusion_matrix(test_labels, y_pred), annot=True, fmt='d', cmap='winter')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:13.384763Z","iopub.execute_input":"2022-07-07T21:54:13.385378Z","iopub.status.idle":"2022-07-07T21:54:13.567334Z","shell.execute_reply.started":"2022-07-07T21:54:13.385330Z","shell.execute_reply":"2022-07-07T21:54:13.566319Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('Precision: %.3f' % precision_score(test_labels, y_pred))\nprint('Recall: %.3f' % recall_score(test_labels, y_pred))\nprint('F1: %.3f' % f1_score(test_labels, y_pred))\nprint('Accuracy: %.3f' % accuracy_score(test_labels, y_pred))","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:18.194660Z","iopub.execute_input":"2022-07-07T21:54:18.195327Z","iopub.status.idle":"2022-07-07T21:54:18.210663Z","shell.execute_reply.started":"2022-07-07T21:54:18.195279Z","shell.execute_reply":"2022-07-07T21:54:18.209654Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# find the best k value with grid search\nfrom sklearn.model_selection import GridSearchCV\nparam_grid = [\n    {'n_neighbors': [1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29]}\n]\nknn = KNeighborsClassifier()\nknn_cv = GridSearchCV(knn, param_grid, cv=10)\nknn_cv.fit(X_prepared, train_labels)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:22.380479Z","iopub.execute_input":"2022-07-07T21:54:22.381126Z","iopub.status.idle":"2022-07-07T21:54:50.059224Z","shell.execute_reply.started":"2022-07-07T21:54:22.381090Z","shell.execute_reply":"2022-07-07T21:54:50.058324Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"knn_cv.best_params_","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:57.511241Z","iopub.execute_input":"2022-07-07T21:54:57.512086Z","iopub.status.idle":"2022-07-07T21:54:57.517665Z","shell.execute_reply.started":"2022-07-07T21:54:57.512032Z","shell.execute_reply":"2022-07-07T21:54:57.517014Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"knn_cv.best_score_","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:54:59.057435Z","iopub.execute_input":"2022-07-07T21:54:59.058121Z","iopub.status.idle":"2022-07-07T21:54:59.065151Z","shell.execute_reply.started":"2022-07-07T21:54:59.058069Z","shell.execute_reply":"2022-07-07T21:54:59.064177Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Random Forest Classifier\nrf_model = RandomForestClassifier(n_estimators=100, random_state=42)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:55:05.336929Z","iopub.execute_input":"2022-07-07T21:55:05.337462Z","iopub.status.idle":"2022-07-07T21:55:05.342213Z","shell.execute_reply.started":"2022-07-07T21:55:05.337424Z","shell.execute_reply":"2022-07-07T21:55:05.341316Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# fit the model\nrf_model.fit(X_prepared, train_labels)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:55:06.880847Z","iopub.execute_input":"2022-07-07T21:55:06.881889Z","iopub.status.idle":"2022-07-07T21:55:07.766939Z","shell.execute_reply.started":"2022-07-07T21:55:06.881849Z","shell.execute_reply":"2022-07-07T21:55:07.766086Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# predict satisfaction for test dataset\ny_pred = rf_model.predict(full_pipeline.transform(test_set))","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:55:09.541158Z","iopub.execute_input":"2022-07-07T21:55:09.541547Z","iopub.status.idle":"2022-07-07T21:55:09.605200Z","shell.execute_reply.started":"2022-07-07T21:55:09.541517Z","shell.execute_reply":"2022-07-07T21:55:09.604277Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Confusion matrix for the test set\nfrom sklearn.metrics import confusion_matrix\nsns.heatmap(confusion_matrix(test_labels, y_pred), annot=True, fmt='d', cmap='winter')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:55:13.223330Z","iopub.execute_input":"2022-07-07T21:55:13.223707Z","iopub.status.idle":"2022-07-07T21:55:13.428043Z","shell.execute_reply.started":"2022-07-07T21:55:13.223676Z","shell.execute_reply":"2022-07-07T21:55:13.427134Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Accuracy, Precision, Recall, F1 Score\nprint('Precision: %.3f' % precision_score(test_labels, y_pred))\nprint('Recall: %.3f' % recall_score(test_labels, y_pred))\nprint('F1: %.3f' % f1_score(test_labels, y_pred))\nprint('Accuracy: %.3f' % accuracy_score(test_labels, y_pred))","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:56:45.830388Z","iopub.execute_input":"2022-07-07T21:56:45.830812Z","iopub.status.idle":"2022-07-07T21:56:45.844596Z","shell.execute_reply.started":"2022-07-07T21:56:45.830772Z","shell.execute_reply":"2022-07-07T21:56:45.843556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.isnull().sum()","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:56:51.834449Z","iopub.execute_input":"2022-07-07T21:56:51.835597Z","iopub.status.idle":"2022-07-07T21:56:51.847882Z","shell.execute_reply.started":"2022-07-07T21:56:51.835558Z","shell.execute_reply":"2022-07-07T21:56:51.846892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# fill missing values for Arrival Delay in Minutes column\ndf_test['Arrival Delay in Minutes'].fillna(df_test['Arrival Delay in Minutes'].mean(), inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:56:54.658133Z","iopub.execute_input":"2022-07-07T21:56:54.658526Z","iopub.status.idle":"2022-07-07T21:56:54.664941Z","shell.execute_reply.started":"2022-07-07T21:56:54.658491Z","shell.execute_reply":"2022-07-07T21:56:54.663943Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# drop id column\ndf_test.drop('id', axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:56:56.242125Z","iopub.execute_input":"2022-07-07T21:56:56.242840Z","iopub.status.idle":"2022-07-07T21:56:56.248811Z","shell.execute_reply.started":"2022-07-07T21:56:56.242805Z","shell.execute_reply":"2022-07-07T21:56:56.247959Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# predict satisfaction for test dataset\ny_pred = rf_model.predict(full_pipeline.transform(df_test))","metadata":{"execution":{"iopub.status.busy":"2022-07-07T21:57:02.855082Z","iopub.execute_input":"2022-07-07T21:57:02.855469Z","iopub.status.idle":"2022-07-07T21:57:02.949069Z","shell.execute_reply.started":"2022-07-07T21:57:02.855406Z","shell.execute_reply":"2022-07-07T21:57:02.948346Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# read sample submission\nurl=\"../input/aviakompaniya/sample_submission.csv\"\ndf_sub = pd.read_csv(url)","metadata":{"execution":{"iopub.status.busy":"2022-06-07T08:25:41.857177Z","iopub.execute_input":"2022-06-07T08:25:41.857586Z","iopub.status.idle":"2022-06-07T08:25:41.879783Z","shell.execute_reply.started":"2022-06-07T08:25:41.857553Z","shell.execute_reply":"2022-06-07T08:25:41.879125Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# save sample submission\ndf_sub['satisfaction'] = y_pred\ndf_sub.to_csv('sample_submission4.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-06-07T08:25:47.663173Z","iopub.execute_input":"2022-06-07T08:25:47.663576Z","iopub.status.idle":"2022-06-07T08:25:47.678132Z","shell.execute_reply.started":"2022-06-07T08:25:47.663543Z","shell.execute_reply":"2022-06-07T08:25:47.67691Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_sub.head(15)","metadata":{"execution":{"iopub.status.busy":"2022-06-07T08:25:49.390661Z","iopub.execute_input":"2022-06-07T08:25:49.391268Z","iopub.status.idle":"2022-06-07T08:25:49.401381Z","shell.execute_reply.started":"2022-06-07T08:25:49.391228Z","shell.execute_reply":"2022-06-07T08:25:49.400387Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}