# jupytext_version: 1.6.0 # kernelspec: # display_name: Python 3 # language: python # name: python3 # --- # + [markdown] papermill={"duration": 0.092568, "end_time": "2020-08-07T07:17:20.608866", "exception": false, "start_time": "2020-08-07T07:17:20.516298", "status": "completed"} tags=[] # # PyCaret 2 Regression Example # - https://github.com/pycaret/pycaret/blob/master/examples/PyCaret%202%20Regression.ipynb # + execution={"iopub.execute_input": "2020-08-07T07:17:20.800167Z", "iopub.status.busy": "2020-08-07T07:17:20.799149Z", "iopub.status.idle": "2020-08-07T07:17:20.827968Z", "shell.execute_reply": "2020-08-07T07:17:20.828775Z"} papermill={"duration": 0.127113, "end_time": "2020-08-07T07:17:20.829021", "exception": false, "start_time": "2020-08-07T07:17:20.701908", "status": "completed"} tags=[] # check version from pycaret.utils import version version() # + execution={"iopub.execute_input": "2020-08-07T07:17:21.220348Z", "iopub.status.busy": "2020-08-07T07:17:21.219385Z", "iopub.status.idle": "2020-08-07T07:17:28.141603Z", "shell.execute_reply": "2020-08-07T07:17:28.140751Z"} papermill={"duration": 7.02273, "end_time": "2020-08-07T07:17:28.141797", "exception": false, "start_time": "2020-08-07T07:17:21.119067", "status": "completed"} tags=[] # %reload_ext autoreload # %autoreload 2 # %matplotlib inline from IPython.core.display import display, HTML display( HTML("<style>.container { width:96% !important; }</style>")) # デフォルトは75% import os import sys import numpy as np import pandas as pd import matplotlib.pyplot as plt
import urllib.request # display imports from IPython.display import display, IFrame from IPython.core.display import HTML # import notebook styling for tables and width etc. response = urllib.request.urlopen( 'https://raw.githubusercontent.com/DataScienceUWL/DS775v2/master/ds755.css' ) HTML(response.read().decode("utf-8")) import os # check version from pycaret.utils import version print(version()) def missing_values_table(df): mis_val = df.isnull().sum() mis_val_percent = 100 * df.isnull().sum() / len(df) mis_val_table = pd.concat([mis_val, mis_val_percent], axis=1) mis_val_table_ren_columns = mis_val_table.rename(columns={ 0: 'Missing Values', 1: '% of Total Values' }) mis_val_table_ren_columns = mis_val_table_ren_columns[ mis_val_table_ren_columns.iloc[:, 1] != 0].sort_values( '% of Total Values', ascending=False).round(1) print("Your selected dataframe has " + str(df.shape[1]) + " columns.\n" "There are " + str(mis_val_table_ren_columns.shape[0]) +