{"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":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n    for filename in filenames:\n        print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2022-08-04T08:08:36.758568Z","iopub.execute_input":"2022-08-04T08:08:36.758932Z","iopub.status.idle":"2022-08-04T08:08:36.765283Z","shell.execute_reply.started":"2022-08-04T08:08:36.758905Z","shell.execute_reply":"2022-08-04T08:08:36.764389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np \nimport pandas as pd \nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn import preprocessing\nimport time\nfrom datetime import datetime\nfrom scipy import integrate, optimize\nimport warnings\nwarnings.filterwarnings('ignore')\n\n# ML libraries\nimport lightgbm as lgb\nimport xgboost as xgb\nfrom xgboost import plot_importance, plot_tree\nfrom sklearn.model_selection import RandomizedSearchCV, GridSearchCV\nfrom sklearn import linear_model\nfrom sklearn.metrics import mean_squared_error\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:08:37.544028Z","iopub.execute_input":"2022-08-04T08:08:37.544845Z","iopub.status.idle":"2022-08-04T08:08:39.030146Z","shell.execute_reply.started":"2022-08-04T08:08:37.544816Z","shell.execute_reply":"2022-08-04T08:08:39.029175Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"../input/Covid19-Death-Predictions/sample_submission.csv","metadata":{"execution":{"iopub.status.busy":"2022-08-04T05:47:35.140622Z","iopub.execute_input":"2022-08-04T05:47:35.141044Z","iopub.status.idle":"2022-08-04T05:47:35.148273Z","shell.execute_reply.started":"2022-08-04T05:47:35.141017Z","shell.execute_reply":"2022-08-04T05:47:35.146963Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_example = pd.read_csv(\"../input/Covid19-Death-Predictions/sample_submission.csv\")\ntest = pd.read_csv(\"../input/Covid19-Death-Predictions/test.csv\")\ntrain = pd.read_csv(\"../input/Covid19-Death-Predictions/train.csv\")\ndisplay(train.head(5))\ndisplay(train.describe())\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T05:57:36.156369Z","iopub.execute_input":"2022-08-04T05:57:36.156823Z","iopub.status.idle":"2022-08-04T05:57:36.501295Z","shell.execute_reply.started":"2022-08-04T05:57:36.156796Z","shell.execute_reply":"2022-08-04T05:57:36.499534Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(\"Number of Location: \", train['Location'].nunique())\n#print(\"Dates go from day\", max(train['Date']), \"to day\", min(train['Date']), \", a total of\", train['Date'].nunique(), \"days\")\n#print(\"Countries with Province/State informed: \", train[train['Province_State'].isna()==False]['Country_Region'].unique())\nprint(\"Number of Location: \", train['Location'].unique() )\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:26:30.178146Z","iopub.execute_input":"2022-08-04T06:26:30.178491Z","iopub.status.idle":"2022-08-04T06:26:30.200283Z","shell.execute_reply.started":"2022-08-04T06:26:30.178465Z","shell.execute_reply":"2022-08-04T06:26:30.199170Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(submission_example.head(5))\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:49.153471Z","iopub.execute_input":"2022-08-04T06:47:49.153829Z","iopub.status.idle":"2022-08-04T06:47:49.161840Z","shell.execute_reply.started":"2022-08-04T06:47:49.153803Z","shell.execute_reply":"2022-08-04T06:47:49.160946Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train[train[\"Id\"] == 911530868]\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:49.446707Z","iopub.execute_input":"2022-08-04T06:47:49.447451Z","iopub.status.idle":"2022-08-04T06:47:49.464452Z","shell.execute_reply.started":"2022-08-04T06:47:49.447424Z","shell.execute_reply":"2022-08-04T06:47:49.463667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test[test[\"Id\"] == 680387432]\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:49.736560Z","iopub.execute_input":"2022-08-04T06:47:49.736910Z","iopub.status.idle":"2022-08-04T06:47:49.756404Z","shell.execute_reply.started":"2022-08-04T06:47:49.736885Z","shell.execute_reply":"2022-08-04T06:47:49.755122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train2 = train.fillna(0)\ntrain2","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:50.250331Z","iopub.execute_input":"2022-08-04T06:47:50.251304Z","iopub.status.idle":"2022-08-04T06:47:50.296077Z","shell.execute_reply.started":"2022-08-04T06:47:50.251245Z","shell.execute_reply":"2022-08-04T06:47:50.294329Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test2 = test.fillna(0)\ntest2","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:50.610488Z","iopub.execute_input":"2022-08-04T06:47:50.610829Z","iopub.status.idle":"2022-08-04T06:47:50.646948Z","shell.execute_reply.started":"2022-08-04T06:47:50.610806Z","shell.execute_reply":"2022-08-04T06:47:50.645399Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:51.154475Z","iopub.execute_input":"2022-08-04T06:47:51.154803Z","iopub.status.idle":"2022-08-04T06:47:51.158623Z","shell.execute_reply.started":"2022-08-04T06:47:51.154778Z","shell.execute_reply":"2022-08-04T06:47:51.157732Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# No.5\n# XにOverallQual、yにSalePriceをセット\nX = train2[[\"Weekly Deaths\"]].values\ny = train2[\"Next Week's Deaths\"].values\n\n# アルゴリズムに線形回帰(Linear Regression)を採用\nslr = LinearRegression()\n\n# fit関数でモデル作成\nslr.fit(X,y)\n\n# 偏回帰係数(回帰分析において得られる回帰方程式の各説明変数の係数)を出力\n# 偏回帰係数はscikit-learnのcoefで取得\nprint('傾き：{0}'.format(slr.coef_[0]))\n\n# y切片(直線とy軸との交点)を出力\n# 余談：x切片もあり、それは直線とx軸との交点を指す\nprint('y切片: {0}'.format(slr.intercept_))\n\n# No.6\n# 散布図を描画\nplt.scatter(X,y)\n\n# 折れ線グラフを描画\nplt.plot(X,slr.predict(X),color='red')\n\n# 表示\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:51.610249Z","iopub.execute_input":"2022-08-04T06:47:51.610965Z","iopub.status.idle":"2022-08-04T06:47:52.035813Z","shell.execute_reply.started":"2022-08-04T06:47:51.610939Z","shell.execute_reply":"2022-08-04T06:47:52.034446Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test_pred = slr.predict(test2[[\"Weekly Deaths\"]].values)\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:53.057275Z","iopub.execute_input":"2022-08-04T06:47:53.057851Z","iopub.status.idle":"2022-08-04T06:47:53.063370Z","shell.execute_reply.started":"2022-08-04T06:47:53.057817Z","shell.execute_reply":"2022-08-04T06:47:53.062603Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_test_pred","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:53.942379Z","iopub.execute_input":"2022-08-04T06:47:53.943020Z","iopub.status.idle":"2022-08-04T06:47:53.950604Z","shell.execute_reply.started":"2022-08-04T06:47:53.942992Z","shell.execute_reply":"2022-08-04T06:47:53.949755Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test2","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:55.163071Z","iopub.execute_input":"2022-08-04T06:47:55.164080Z","iopub.status.idle":"2022-08-04T06:47:55.198081Z","shell.execute_reply.started":"2022-08-04T06:47:55.164045Z","shell.execute_reply":"2022-08-04T06:47:55.196792Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test2[\"Next Week's Deaths\"] = y_test_pred\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:47:56.704245Z","iopub.execute_input":"2022-08-04T06:47:56.704573Z","iopub.status.idle":"2022-08-04T06:47:56.709556Z","shell.execute_reply.started":"2022-08-04T06:47:56.704548Z","shell.execute_reply":"2022-08-04T06:47:56.708607Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test2","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:26:57.821976Z","iopub.execute_input":"2022-08-04T06:26:57.822291Z","iopub.status.idle":"2022-08-04T06:26:57.855828Z","shell.execute_reply.started":"2022-08-04T06:26:57.822268Z","shell.execute_reply":"2022-08-04T06:26:57.854759Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# No.5\n# XにOverallQual、yにSalePriceをセット\nX2 = test2[[\"Weekly Cases\"]].values\ny2 = test2[\"Next Week's Deaths\"].values\n\n# アルゴリズムに線形回帰(Linear Regression)を採用\nslr = LinearRegression()\n\n# fit関数でモデル作成\nslr.fit(X2,y2)\n\n# 偏回帰係数(回帰分析において得られる回帰方程式の各説明変数の係数)を出力\n# 偏回帰係数はscikit-learnのcoefで取得\nprint('傾き：{0}'.format(slr.coef_[0]))\n\n# y切片(直線とy軸との交点)を出力\n# 余談：x切片もあり、それは直線とx軸との交点を指す\nprint('y切片: {0}'.format(slr.intercept_))\n\n# No.6\n# 散布図を描画\nplt.scatter(X2,y2)\n\n# 折れ線グラフを描画\nplt.plot(X2,slr.predict(X2),color='red')\n\n# 表示\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:57:05.528119Z","iopub.execute_input":"2022-08-04T06:57:05.528477Z","iopub.status.idle":"2022-08-04T06:57:05.765601Z","shell.execute_reply.started":"2022-08-04T06:57:05.528451Z","shell.execute_reply":"2022-08-04T06:57:05.764728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(test2.describe())\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:48:10.407791Z","iopub.execute_input":"2022-08-04T06:48:10.408099Z","iopub.status.idle":"2022-08-04T06:48:10.487583Z","shell.execute_reply.started":"2022-08-04T06:48:10.408075Z","shell.execute_reply":"2022-08-04T06:48:10.486717Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission_example","metadata":{"execution":{"iopub.status.busy":"2022-08-04T06:48:19.846984Z","iopub.execute_input":"2022-08-04T06:48:19.847353Z","iopub.status.idle":"2022-08-04T06:48:19.857543Z","shell.execute_reply.started":"2022-08-04T06:48:19.847320Z","shell.execute_reply":"2022-08-04T06:48:19.856861Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"####################","metadata":{}},{"cell_type":"code","source":"submission_example = pd.read_csv(\"../input/Covid19-Death-Predictions/sample_submission.csv\")\ntest = pd.read_csv(\"../input/Covid19-Death-Predictions/test.csv\")\ntrain = pd.read_csv(\"../input/Covid19-Death-Predictions/train.csv\")\ntrain2 = train.fillna(0)\ntest2 = test.fillna(0)\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:25.988725Z","iopub.execute_input":"2022-08-04T08:16:25.989050Z","iopub.status.idle":"2022-08-04T08:16:26.176986Z","shell.execute_reply.started":"2022-08-04T08:16:25.989024Z","shell.execute_reply":"2022-08-04T08:16:26.176149Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X3 = train2.loc[:, [\"Weekly Deaths\", \"Weekly Cases\", \"Daily People Vaccinated\"]].values\ny3 = train2.loc[:, [\"Next Week's Deaths\"]].values","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:26.400994Z","iopub.execute_input":"2022-08-04T08:16:26.401812Z","iopub.status.idle":"2022-08-04T08:16:26.407173Z","shell.execute_reply.started":"2022-08-04T08:16:26.401781Z","shell.execute_reply":"2022-08-04T08:16:26.406585Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nss = StandardScaler()\nX3 = ss.fit_transform(X3)","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:27.407511Z","iopub.execute_input":"2022-08-04T08:16:27.408112Z","iopub.status.idle":"2022-08-04T08:16:27.414741Z","shell.execute_reply.started":"2022-08-04T08:16:27.408081Z","shell.execute_reply":"2022-08-04T08:16:27.414039Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X3, y3, test_size = 0.3, random_state = 0)","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:28.637117Z","iopub.execute_input":"2022-08-04T08:16:28.637946Z","iopub.status.idle":"2022-08-04T08:16:28.650448Z","shell.execute_reply.started":"2022-08-04T08:16:28.637916Z","shell.execute_reply":"2022-08-04T08:16:28.649850Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.linear_model import LinearRegression\nlr = LinearRegression()\n# trainデータを使って学習する\nlr.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:30.141236Z","iopub.execute_input":"2022-08-04T08:16:30.141814Z","iopub.status.idle":"2022-08-04T08:16:30.154270Z","shell.execute_reply.started":"2022-08-04T08:16:30.141785Z","shell.execute_reply":"2022-08-04T08:16:30.153478Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def adjusted(score, n_sample, n_features):\n    adjusted_score = 1 - (1 - score) * ((n_sample - 1) / (n_sample - n_features - 1))\n    return adjusted_score","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:31.449586Z","iopub.execute_input":"2022-08-04T08:16:31.449967Z","iopub.status.idle":"2022-08-04T08:16:31.454569Z","shell.execute_reply.started":"2022-08-04T08:16:31.449938Z","shell.execute_reply":"2022-08-04T08:16:31.453772Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# モデルの評価\nprint('adjusted R^2')\nprint('train: %.3f' % adjusted(lr.score(X_train, y_train), len(y_train),X_train.shape[1]))\nprint('test : %.3f' % adjusted(lr.score(X_test, y_test), len(y_test), X_test.shape[1]))","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:32.509791Z","iopub.execute_input":"2022-08-04T08:16:32.510649Z","iopub.status.idle":"2022-08-04T08:16:32.519936Z","shell.execute_reply.started":"2022-08-04T08:16:32.510612Z","shell.execute_reply":"2022-08-04T08:16:32.519079Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X4 = test2.loc[:, [\"Weekly Deaths\", \"Weekly Cases\", \"Daily People Vaccinated\"]].values\nX4 = ss.fit_transform(X4)\ny_pred = lr.predict(X4)","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:33.711645Z","iopub.execute_input":"2022-08-04T08:16:33.712034Z","iopub.status.idle":"2022-08-04T08:16:33.719889Z","shell.execute_reply.started":"2022-08-04T08:16:33.712000Z","shell.execute_reply":"2022-08-04T08:16:33.719116Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test3 = test2","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:23:31.687977Z","iopub.execute_input":"2022-08-04T08:23:31.688435Z","iopub.status.idle":"2022-08-04T08:23:31.691469Z","shell.execute_reply.started":"2022-08-04T08:23:31.688410Z","shell.execute_reply":"2022-08-04T08:23:31.690892Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# 配列を一次元に\ny_pred = y_pred.flatten()\nprint(y_pred, y_pred.shape, type(y_pred))\n# 提出ファイルの作成\ntest3[\"Next Week's Deaths\"] = y_pred\ntest3[\"Next Week's Deaths\"] = test3[\"Next Week's Deaths\"].round(0)\ntest3[\"Next Week's Deaths\"] = test3[\"Next Week's Deaths\"].astype(int)\n\ntest3[['Id',\"Next Week's Deaths\"]].to_csv('submission.csv',index=False)","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:25:10.465893Z","iopub.execute_input":"2022-08-04T08:25:10.466211Z","iopub.status.idle":"2022-08-04T08:25:10.516200Z","shell.execute_reply.started":"2022-08-04T08:25:10.466183Z","shell.execute_reply":"2022-08-04T08:25:10.515377Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission2 = pd.read_csv(\"./submission.csv\")\ndisplay(submission2.head(5))","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:25:13.464035Z","iopub.execute_input":"2022-08-04T08:25:13.464349Z","iopub.status.idle":"2022-08-04T08:25:13.479291Z","shell.execute_reply.started":"2022-08-04T08:25:13.464315Z","shell.execute_reply":"2022-08-04T08:25:13.478489Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\ndisplay(submission_example.head(5))","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:26:58.163619Z","iopub.execute_input":"2022-08-04T08:26:58.164529Z","iopub.status.idle":"2022-08-04T08:26:58.172435Z","shell.execute_reply.started":"2022-08-04T08:26:58.164498Z","shell.execute_reply":"2022-08-04T08:26:58.171658Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# No.5\n# XにOverallQual、yにSalePriceをセット\nX2 = test3[[\"Weekly Deaths\"]].values\ny2 = test3[\"Next Week's Deaths\"].values\n\n# アルゴリズムに線形回帰(Linear Regression)を採用\nslr = LinearRegression()\n\n# fit関数でモデル作成\nslr.fit(X2,y2)\n\n# 偏回帰係数(回帰分析において得られる回帰方程式の各説明変数の係数)を出力\n# 偏回帰係数はscikit-learnのcoefで取得\nprint('傾き：{0}'.format(slr.coef_[0]))\n\n# y切片(直線とy軸との交点)を出力\n# 余談：x切片もあり、それは直線とx軸との交点を指す\nprint('y切片: {0}'.format(slr.intercept_))\n\n# No.6\n# 散布図を描画\nplt.scatter(X2,y2)\n\n# 折れ線グラフを描画\nplt.plot(X2,slr.predict(X2),color='red')\n\n# 表示\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:37.571407Z","iopub.execute_input":"2022-08-04T08:16:37.572272Z","iopub.status.idle":"2022-08-04T08:16:37.743874Z","shell.execute_reply.started":"2022-08-04T08:16:37.572236Z","shell.execute_reply":"2022-08-04T08:16:37.743222Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(submission2.describe())\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:39.020729Z","iopub.execute_input":"2022-08-04T08:16:39.021316Z","iopub.status.idle":"2022-08-04T08:16:39.039097Z","shell.execute_reply.started":"2022-08-04T08:16:39.021286Z","shell.execute_reply":"2022-08-04T08:16:39.038242Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission2[\"Next Week's Deaths\"] = submission2[\"Next Week's Deaths\"].where(submission2[\"Next Week's Deaths\"] >= 0, 0)","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:40.361383Z","iopub.execute_input":"2022-08-04T08:16:40.361734Z","iopub.status.idle":"2022-08-04T08:16:40.367429Z","shell.execute_reply.started":"2022-08-04T08:16:40.361706Z","shell.execute_reply":"2022-08-04T08:16:40.366817Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"display(submission2.describe())\n","metadata":{"execution":{"iopub.status.busy":"2022-08-04T08:16:41.967248Z","iopub.execute_input":"2022-08-04T08:16:41.967560Z","iopub.status.idle":"2022-08-04T08:16:41.985791Z","shell.execute_reply.started":"2022-08-04T08:16:41.967533Z","shell.execute_reply":"2022-08-04T08:16:41.984977Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]}