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「Data Every Day」を毎日やるAdvent Calendar 2020Day 15
@kentakozuka(Kenta Kozuka)

Data Every Day: ニューヨーク州環境修復サイト

Posted at

tldr

KggleのNYS Environmental Remediation SitesをClassifying Environmental Program Types - Data Every Day #041に沿ってやっていきます。

実行環境はGoogle Colaboratorです。

インポート

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

import sklearn.preprocessing as sp
from sklearn.model_selection import train_test_split
import sklearn.linear_model as slm

import tensorflow as tf

データのダウンロード

Google Driveをマウントします。

from google.colab import drive
drive.mount('/content/drive')

Mounted at /content/drive

KaggleのAPIクライアントを初期化し、認証します。
認証情報はGoogle Drive内（/content/drive/My Drive/Colab Notebooks/Kaggle）にkaggle.jsonとして置いてあります。

import os
kaggle_path = "/content/drive/My Drive/Colab Notebooks/Kaggle"
os.environ['KAGGLE_CONFIG_DIR'] = kaggle_path

from kaggle.api.kaggle_api_extended import KaggleApi
api = KaggleApi()
api.authenticate()

Kaggle APIを使ってデータをダウンロードします。

dataset_id = 'new-york-state/nys-environmental-remediation-sites'
dataset = api.dataset_list_files(dataset_id)
file_name = dataset.files[0].name
file_path = os.path.join(api.get_default_download_dir(), file_name)
file_path

'/content/environmental-remediation-sites.csv'

api.dataset_download_file(dataset_id, file_name, force=True, quiet=False)

100%|██████████| 4.82M/4.82M [00:00<00:00, 50.2MB/s]

Downloading environmental-remediation-sites.csv.zip to /content









True

データの読み込み

Pedumagalhaes/quality-prediction-in-a-mining-processadasを使ってダウンロードしてきたCSVファイルを読み込みます。

data = pd.read_csv(file_path + '.zip')

data
Program Number Program Type Program Facility Name Site Class Address1 Address2 Locality County ZIPCode SWIS Code DEC Region Latitude Longitude Control Code Control Type OU Project Name Project Completion Date Waste Name Contaminants Owner Name Owner Address1 Owner Address2 Owner City Owner State Owner ZIP Disposal Name Disposal Address1 Disposal Address2 Disposal City Disposal ZIP Disposal State Operator Name Operator Address1 Operator Address2 Operator City Operator State Operator Zip Georeference NYS Municipal Boundaries 2 New York Zip Codes 2 Counties 2
0 C241159 BCP 11-28 31st Drive C 11-28 31st Drive NaN Queens Queens 11106 4101 2 40.768000 -73.935028 Environmental Easement Environmental Easement 01 Remedial Design 2016-09-12T00:00:00.000 NaN tetrachloroethene (PCE) George Man 1083 Maple Lane NaN New Hyde Park NY 11040 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN {'type': 'Point', 'coordinates': [-73.93502777... 196 878.0 2137.0
1 E633060 ERP 1333 East Dominick Street A 1313-1333 East Dominick Street NaN Rome Oneida 13440 3313 6 43.204978 -75.434194 Environmental Easement Environmental Easement 01 NaN 2018-03-01T00:00:00.000 NaN benzo(a)anthracene JAMES F. BROWN 198 N. WASHINGTON ST. NaN ROME NY 13440 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN {'type': 'Point', 'coordinates': [-75.43419382... 989 1366.0 625.0
2 C915320 BCP 166 Chandler Street C 166 Chandler Street NaN Buffalo Erie 14207 1502 9 42.943614 -78.885092 Environmental Easement Environmental Easement 01 Remedial Investigation 2018-12-12T00:00:00.000 NaN mercury Rocco Termini 391 Washington Street NaN Buffalo NY 14203 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN {'type': 'Point', 'coordinates': [-78.88509166... 300 63.0 2041.0
3 C828192 BCP 1777 East Henrietta Road A 1777 East Henrietta Road NaN Henrietta Monroe 14623 2832 8 43.084683 -77.610928 Environmental Easement Environmental Easement 01 NaN 2020-12-31T00:00:00.000 NaN benzo(a)pyrene Kerry Ventures Tech Park, LLC 195 Middle road, suite 400 NaN henrietta NY 14467 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN {'type': 'Point', 'coordinates': [-77.61092777... 546 1682.0 2093.0
4 V00669 VCP 192 Ralph Avenue C 192 Ralph Ave NaN Brooklyn Kings 11233 2401 2 40.683078 -73.922924 Deed Restriction Deed Restriction 01A Remedial Action 2008-06-11T00:00:00.000 NaN tetrachloroethene (PCE) NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN {'type': 'Point', 'coordinates': [-73.92292392... 894 816.0 2090.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
82605 704005 HW Tri-City Barrel Company 04 Old Route 7 NaN Fenton (Port Crane) Broome 13833 432 7 42.158504 -75.811645 Other Controls Other Controls 01 Remedial Design 2003-01-16T00:00:00.000 DICHLOROETHYLENE, XYLENE, STYRENE, styrene Gary Warner RD 1 / PO Box 88 NaN Port Crane NY 13833 TRI-CITY BARREL COMPANY, INC. NaN NaN NaN NaN ZZ WARNER GARY R.D. 1, PO BOX 88 NaN PORT CRANE NY NaN {'type': 'Point', 'coordinates': [-75.811645, ... 945 453.0 2033.0
82606 C224043 BCP U. S. Dredging Shipyard Site C One Beard Street NaN Brooklyn Kings 11231 2401 2 40.671538 -74.011473 Environmental Easement Environmental Easement 01 Remedial Investigation 2006-03-31T00:00:00.000 NaN methylene chloride cLAUDE bOISVERT 420 Alan Wood Road NaN Conshohocken PA 19428 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN {'type': 'Point', 'coordinates': [-74.011473, ... 894 814.0 2090.0
82607 130043H HW Utility Manufacturing/Wonder King 04 700-712 Main Street NaN Westbury Nassau 11590 3022 1 40.756999 -73.556776 Decision Document Decision Document 01 Remedial Investigation 2003-03-19T00:00:00.000 TETRACHLOROETHYLENE tetrachloroethene (PCE) Audie Kranz and Wilbur Kranz 700-712 Main Street NaN Westbury NY 11590 NEST EQUITIES, INC. NaN NaN NaN NaN ZZ Audie Kranz and Wilbur Kranz 700-712 Main Street NaN Westbury NY 11590 {'type': 'Point', 'coordinates': [-73.55677553... 60 997.0 2094.0
82608 704005 HW Tri-City Barrel Company 04 Old Route 7 NaN Fenton (Port Crane) Broome 13833 432 7 42.158504 -75.811645 Deed Restriction Deed Restriction 01 Remedial Investigation 2000-03-31T00:00:00.000 DICHLOROETHYLENE, XYLENE, STYRENE, styrene Tri-Cities Barrel Co Inc 319 NYS Rte 7 PO Box 88 NaN Port Crane NY 13833 TRI-CITY BARREL COMPANY, INC. NaN NaN NaN NaN ZZ WARNER GARY R.D. 1, PO BOX 88 NaN PORT CRANE NY NaN {'type': 'Point', 'coordinates': [-75.811645, ... 945 453.0 2033.0
82609 413010 HW West Well and Parking Area (Amphenol) 02 40-60 Delaware Street NaN Sidney Delaware 13838 1350 4 42.307482 -75.399926 Deed Restriction Deed Restriction 01 Remedial Design 1987-11-01T00:00:00.000 NaN trans-1,2-dichloroethene joseph m bianchi 40-60 Delaware Avenue NaN Sidney NY 13838 bendix corporation NaN NaN NaN NaN NY joseph m bianchi 40-60 Delaware Avenue NaN Sidney NY 13838 {'type': 'Point', 'coordinates': [-75.39992579... 558 456.0 2039.0

82610 rows × 42 columns

下準備

data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 82610 entries, 0 to 82609
Data columns (total 42 columns):
 #   Column                      Non-Null Count  Dtype  
---  ------                      --------------  -----  
 0   Program Number              82610 non-null  object 
 1   Program Type                82610 non-null  object 
 2   Program Facility Name       82610 non-null  object 
 3   Site Class                  82610 non-null  object 
 4   Address1                    82610 non-null  object 
 5   Address2                    1190 non-null   object 
 6   Locality                    82448 non-null  object 
 7   County                      82610 non-null  object 
 8   ZIPCode                     82610 non-null  object 
 9   SWIS Code                   82610 non-null  int64  
 10  DEC Region                  82610 non-null  int64  
 11  Latitude                    82610 non-null  float64
 12  Longitude                   82610 non-null  float64
 13  Control Code                82610 non-null  object 
 14  Control Type                82610 non-null  object 
 15  OU                          82610 non-null  object 
 16  Project Name                68153 non-null  object 
 17  Project Completion Date     82549 non-null  object 
 18  Waste Name                  36047 non-null  object 
 19  Contaminants                53667 non-null  object 
 20  Owner Name                  79344 non-null  object 
 21  Owner Address1              78689 non-null  object 
 22  Owner Address2              11719 non-null  object 
 23  Owner City                  78843 non-null  object 
 24  Owner State                 79344 non-null  object 
 25  Owner ZIP                   78547 non-null  object 
 26  Disposal Name               31861 non-null  object 
 27  Disposal Address1           8037 non-null   object 
 28  Disposal Address2           1354 non-null   object 
 29  Disposal City               6534 non-null   object 
 30  Disposal ZIP                5516 non-null   object 
 31  Disposal State              31927 non-null  object 
 32  Operator Name               55801 non-null  object 
 33  Operator Address1           53337 non-null  object 
 34  Operator Address2           2915 non-null   object 
 35  Operator City               53489 non-null  object 
 36  Operator State              55859 non-null  object 
 37  Operator Zip                51438 non-null  object 
 38  Georeference                82610 non-null  object 
 39  NYS Municipal Boundaries 2  82610 non-null  int64  
 40  New York Zip Codes 2        82056 non-null  float64
 41  Counties 2                  82517 non-null  float64
dtypes: float64(4), int64(3), object(35)
memory usage: 26.5+ MB

null_columns = data.loc[:, data.isna().sum() > 0.25 * data.shape[0]]

null_columns
Address2 Waste Name Contaminants Owner Address2 Disposal Name Disposal Address1 Disposal Address2 Disposal City Disposal ZIP Disposal State Operator Name Operator Address1 Operator Address2 Operator City Operator State Operator Zip
0 NaN NaN tetrachloroethene (PCE) NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
1 NaN NaN benzo(a)anthracene NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2 NaN NaN mercury NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
3 NaN NaN benzo(a)pyrene NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
4 NaN NaN tetrachloroethene (PCE) NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
82605 NaN DICHLOROETHYLENE, XYLENE, STYRENE, styrene NaN TRI-CITY BARREL COMPANY, INC. NaN NaN NaN NaN ZZ WARNER GARY R.D. 1, PO BOX 88 NaN PORT CRANE NY NaN
82606 NaN NaN methylene chloride NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
82607 NaN TETRACHLOROETHYLENE tetrachloroethene (PCE) NaN NEST EQUITIES, INC. NaN NaN NaN NaN ZZ Audie Kranz and Wilbur Kranz 700-712 Main Street NaN Westbury NY 11590
82608 NaN DICHLOROETHYLENE, XYLENE, STYRENE, styrene NaN TRI-CITY BARREL COMPANY, INC. NaN NaN NaN NaN ZZ WARNER GARY R.D. 1, PO BOX 88 NaN PORT CRANE NY NaN
82609 NaN NaN trans-1,2-dichloroethene NaN bendix corporation NaN NaN NaN NaN NY joseph m bianchi 40-60 Delaware Avenue NaN Sidney NY 13838

82610 rows × 16 columns

data = data.drop(null_columns, axis=1)

unneeded_columns = [
    'Program Number', 
    'Project Name', 
    'Program Facility Name', 
    'Address1', 
    'Locality', 
    'ZIPCode', 
    'SWIS Code', 
    'Owner Name',
    'Owner Address1', 
    'Owner City', 
    'Owner State', 
    'Owner ZIP',
    'Georeference',
]

data = data.drop(unneeded_columns, axis=1)

data
Program Type Site Class County DEC Region Latitude Longitude Control Code Control Type OU Project Completion Date NYS Municipal Boundaries 2 New York Zip Codes 2 Counties 2
0 BCP C Queens 2 40.768000 -73.935028 Environmental Easement Environmental Easement 01 2016-09-12T00:00:00.000 196 878.0 2137.0
1 ERP A Oneida 6 43.204978 -75.434194 Environmental Easement Environmental Easement 01 2018-03-01T00:00:00.000 989 1366.0 625.0
2 BCP C Erie 9 42.943614 -78.885092 Environmental Easement Environmental Easement 01 2018-12-12T00:00:00.000 300 63.0 2041.0
3 BCP A Monroe 8 43.084683 -77.610928 Environmental Easement Environmental Easement 01 2020-12-31T00:00:00.000 546 1682.0 2093.0
4 VCP C Kings 2 40.683078 -73.922924 Deed Restriction Deed Restriction 01A 2008-06-11T00:00:00.000 894 816.0 2090.0
... ... ... ... ... ... ... ... ... ... ... ... ... ...
82605 HW 04 Broome 7 42.158504 -75.811645 Other Controls Other Controls 01 2003-01-16T00:00:00.000 945 453.0 2033.0
82606 BCP C Kings 2 40.671538 -74.011473 Environmental Easement Environmental Easement 01 2006-03-31T00:00:00.000 894 814.0 2090.0
82607 HW 04 Nassau 1 40.756999 -73.556776 Decision Document Decision Document 01 2003-03-19T00:00:00.000 60 997.0 2094.0
82608 HW 04 Broome 7 42.158504 -75.811645 Deed Restriction Deed Restriction 01 2000-03-31T00:00:00.000 945 453.0 2033.0
82609 HW 02 Delaware 4 42.307482 -75.399926 Deed Restriction Deed Restriction 01 1987-11-01T00:00:00.000 558 456.0 2039.0

82610 rows × 13 columns

def get_uniques(df, columns):
    return {column: list(df[column].unique()) for column in columns}

def get_categorical_columns(df):
    return [column for column in df.columns if df.dtypes[column] == 'object']

get_uniques(data, get_categorical_columns(data))

{'Control Code': ['Environmental Easement',
  'Deed Restriction',
  'Environmental Notice',
  'Other Controls',
  'Decision Document'],
 'Control Type': ['Environmental Easement',
  'Deed Restriction',
  'Environmental Notice',
  'Other Controls',
  'Decision Document'],
 'County': ['Queens',
  'Oneida',
  'Erie',
  'Monroe',
  'Kings',
  'Bronx',
  'Cattaraugus',
  'Schenectady',
  'Broome',
  'Franklin',
  'Onondaga',
  'Delaware',
  'Richmond',
  'Nassau',
  'Dutchess',
  'Westchester',
  'Chenango',
  'Niagara',
  'New York',
  'Ulster',
  'Orleans',
  'Chemung',
  'Lewis',
  'Ontario',
  'Montgomery',
  'Herkimer',
  'Wayne',
  'Saratoga',
  'Rockland',
  'Jefferson',
  'Suffolk',
  'Washington',
  'Orange',
  'Rensselaer',
  'Oswego',
  'Fulton',
  'Albany',
  'Clinton',
  'St Lawrence',
  'Seneca',
  'Cayuga',
  'Chautauqua',
  'Tompkins',
  'Genesee',
  'Tioga',
  'Putnam',
  'Madison',
  'Greene',
  'Yates',
  'Sullivan',
  'Allegany',
  'Warren',
  'Schoharie',
  'Livingston',
  'Steuben',
  'Cortland',
  'Wyoming',
  'Columbia',
  'Schuyler',
  'Otsego',
  'Essex'],
 'OU': ['01',
  '01A',
  '02',
  '03',
  '04B',
  '00',
  '05',
  '09',
  '12',
  '21',
  '11',
  '16',
  '17',
  '04',
  '06',
  '01B',
  '02A',
  '01C',
  '01E',
  '05A',
  '03A',
  '06A',
  '04A',
  '03B',
  '02B',
  '04C',
  '07',
  '04D',
  '03C',
  '01H',
  '01J',
  '01F',
  '01D',
  '01K',
  '08',
  '01a',
  '02C',
  '18',
  '20',
  '14',
  '25',
  '15',
  '13',
  '19',
  '42'],
 'Program Type': ['BCP', 'ERP', 'VCP', 'HW', 'RCRA'],
 'Project Completion Date': ['2016-09-12T00:00:00.000',
  '2018-03-01T00:00:00.000',
  '2018-12-12T00:00:00.000',
  '2020-12-31T00:00:00.000',
  '2008-06-11T00:00:00.000',
  '2011-08-16T00:00:00.000',
  '2016-11-30T00:00:00.000',
  '2014-03-19T00:00:00.000',
  '2012-04-10T00:00:00.000',
  '2021-07-31T00:00:00.000',
  '2010-03-17T00:00:00.000',
  '2000-03-27T00:00:00.000',
  '1991-08-01T00:00:00.000',
  nan,
  '1999-03-01T00:00:00.000',
  '2000-12-22T00:00:00.000',
  '2007-07-30T00:00:00.000',
  '2001-05-04T00:00:00.000',
  '2014-10-30T00:00:00.000',
  '2012-11-16T00:00:00.000',
  '2008-01-10T00:00:00.000',
  '1995-09-01T00:00:00.000',
  '2014-05-14T00:00:00.000',
  '2020-05-29T00:00:00.000',
  '1990-10-01T00:00:00.000',
  '2005-10-27T00:00:00.000',
  '2022-06-30T00:00:00.000',
  '2010-09-30T00:00:00.000',
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  '1993-03-01T00:00:00.000',
  '2003-09-04T00:00:00.000',
  '2019-12-31T00:00:00.000',
  '2016-12-21T00:00:00.000',
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  '1994-09-01T00:00:00.000',
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  '1987-01-01T00:00:00.000',
  '2021-08-30T00:00:00.000',
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  '2019-05-08T00:00:00.000',
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  '2021-10-31T00:00:00.000',
  '1989-10-01T00:00:00.000',
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  '1991-12-01T00:00:00.000',
  '2017-07-12T00:00:00.000',
  '1995-02-01T00:00:00.000',
  '1998-06-17T00:00:00.000',
  '1991-11-01T00:00:00.000',
  '2007-03-30T00:00:00.000',
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  '1992-09-01T00:00:00.000',
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  '1997-07-01T00:00:00.000',
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  '2007-05-03T00:00:00.000',
  '2000-05-30T00:00:00.000',
  '2016-03-31T00:00:00.000',
  '1998-06-01T00:00:00.000',
  '2015-11-20T00:00:00.000',
  '2019-09-27T00:00:00.000',
  '2008-03-21T00:00:00.000',
  '2015-09-30T00:00:00.000',
  '1996-08-01T00:00:00.000',
  '1994-03-01T00:00:00.000',
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  '2020-09-25T00:00:00.000',
  '2020-08-04T00:00:00.000',
  '2011-12-30T00:00:00.000',
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  '2020-05-21T00:00:00.000',
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  '2006-03-28T00:00:00.000',
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  '2018-12-18T00:00:00.000',
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  '2018-09-14T00:00:00.000',
  '2013-04-25T00:00:00.000',
  '2005-03-24T00:00:00.000',
  '2008-02-28T00:00:00.000',
  '2015-12-29T00:00:00.000',
  '2017-08-23T00:00:00.000',
  '2015-10-01T00:00:00.000',
  '2019-09-11T00:00:00.000',
  '2013-01-30T00:00:00.000',
  '1998-01-01T00:00:00.000',
  '2015-01-22T00:00:00.000',
  '2015-11-30T00:00:00.000',
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  '2004-03-31T00:00:00.000',
  '2000-11-27T00:00:00.000',
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  '2019-12-18T00:00:00.000',
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  '2014-12-23T00:00:00.000',
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  '2017-08-09T00:00:00.000',
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  '2004-03-16T00:00:00.000',
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  '2019-08-19T00:00:00.000',
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  '2015-07-06T00:00:00.000',
  '2014-03-04T00:00:00.000',
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  '2015-11-12T00:00:00.000',
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  '2011-08-24T00:00:00.000',
  '2018-12-03T00:00:00.000',
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  '2011-09-20T00:00:00.000',
  '2019-05-20T00:00:00.000',
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  '2018-05-29T00:00:00.000',
  '2019-12-24T00:00:00.000',
  '2020-11-30T00:00:00.000',
  '2013-12-31T00:00:00.000',
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  '2013-11-25T00:00:00.000',
  '2015-05-15T00:00:00.000',
  '1991-07-01T00:00:00.000',
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  '2011-10-13T00:00:00.000',
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  '2012-02-28T00:00:00.000',
  '2017-08-24T00:00:00.000',
  '1999-08-30T00:00:00.000',
  '2016-12-28T00:00:00.000',
  '1999-10-01T00:00:00.000',
  '2015-01-06T00:00:00.000',
  '2015-06-01T00:00:00.000',
  '2017-10-31T00:00:00.000',
  '2015-02-03T00:00:00.000',
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  '2019-04-26T00:00:00.000',
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  '2008-08-12T00:00:00.000',
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  '2074-04-02T00:00:00.000',
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  '2013-12-19T00:00:00.000',
  '2010-06-08T00:00:00.000',
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  '2018-06-11T00:00:00.000',
  '2019-08-16T00:00:00.000',
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  '2005-05-10T00:00:00.000',
  '1992-10-01T00:00:00.000',
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  '2005-03-18T00:00:00.000',
  '2015-03-16T00:00:00.000',
  '2018-03-19T00:00:00.000',
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  '1999-09-01T00:00:00.000',
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  '1996-07-01T00:00:00.000',
  '2017-12-27T00:00:00.000',
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  '2003-10-20T00:00:00.000',
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  '1993-02-01T00:00:00.000',
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  '2007-12-12T00:00:00.000',
  '2007-03-27T00:00:00.000',
  '2009-01-07T00:00:00.000',
  '2014-09-19T00:00:00.000',
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  '2007-02-13T00:00:00.000',
  '2003-06-23T00:00:00.000',
  '2010-12-28T00:00:00.000',
  '2016-11-04T00:00:00.000',
  '2001-06-26T00:00:00.000',
  '2018-12-28T00:00:00.000',
  '2018-10-03T00:00:00.000',
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  '2018-03-21T00:00:00.000',
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  '2018-02-15T00:00:00.000',
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  '2016-01-26T00:00:00.000',
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  '1995-11-01T00:00:00.000',
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  '2018-06-25T00:00:00.000',
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  '2014-12-18T00:00:00.000',
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  '2014-05-08T00:00:00.000',
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  '2006-05-29T00:00:00.000',
  '2003-09-15T00:00:00.000',
  '2018-03-08T00:00:00.000',
  '2012-04-06T00:00:00.000',
  '2017-09-06T00:00:00.000',
  '2017-11-14T00:00:00.000',
  '2008-04-10T00:00:00.000',
  '2014-06-25T00:00:00.000',
  '2011-01-21T00:00:00.000',
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  '2018-06-28T00:00:00.000',
  '2016-05-19T00:00:00.000',
  '2018-06-01T00:00:00.000',
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  '2002-11-29T00:00:00.000',
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  '2014-11-19T00:00:00.000',
  '2014-08-14T00:00:00.000',
  '2017-04-30T00:00:00.000',
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  '2019-03-27T00:00:00.000',
  '2000-06-08T00:00:00.000',
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  '2013-10-11T00:00:00.000',
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  '2015-03-19T00:00:00.000',
  '2015-08-15T00:00:00.000',
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  '2019-11-15T00:00:00.000',
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  '2004-11-19T00:00:00.000',
  '2013-05-16T00:00:00.000',
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  '2000-02-29T00:00:00.000',
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  '2004-09-27T00:00:00.000',
  '2002-01-31T00:00:00.000',
  '1997-09-01T00:00:00.000',
  '2022-12-01T00:00:00.000',
  '2013-02-05T00:00:00.000',
  '2015-05-31T00:00:00.000',
  '2004-03-01T00:00:00.000',
  '1993-04-01T00:00:00.000',
  '2011-09-30T00:00:00.000',
  '2018-01-24T00:00:00.000',
  '2018-08-10T00:00:00.000',
  '2011-09-09T00:00:00.000',
  '2015-05-27T00:00:00.000',
  '2013-02-25T00:00:00.000',
  '2000-11-29T00:00:00.000',
  '2011-02-14T00:00:00.000',
  '1994-05-01T00:00:00.000',
  '2012-03-30T00:00:00.000',
  '2004-04-14T00:00:00.000',
  '2022-09-16T00:00:00.000',
  '2016-12-13T00:00:00.000',
  '2006-09-05T00:00:00.000',
  '1988-07-01T00:00:00.000',
  '2018-11-15T00:00:00.000',
  '2010-12-22T00:00:00.000',
  '2018-09-25T00:00:00.000',
  '2004-04-04T00:00:00.000',
  '2014-09-18T00:00:00.000',
  '1998-05-01T00:00:00.000',
  '2013-10-18T00:00:00.000',
  '2000-02-25T00:00:00.000',
  '2017-03-01T00:00:00.000',
  '2016-01-07T00:00:00.000',
  '2015-09-15T00:00:00.000',
  '2003-09-12T00:00:00.000',
  '2006-07-27T00:00:00.000',
  '2019-07-26T00:00:00.000',
  '2015-08-03T00:00:00.000',
  '2000-11-09T00:00:00.000',
  '2011-03-30T00:00:00.000',
  '2002-12-30T00:00:00.000',
  '1981-01-01T00:00:00.000',
  '2004-12-31T00:00:00.000',
  '2008-02-13T00:00:00.000',
  '2007-05-01T00:00:00.000',
  '2017-11-16T00:00:00.000',
  '2016-05-24T00:00:00.000',
  '2000-10-02T00:00:00.000',
  '2014-07-24T00:00:00.000',
  '2003-04-09T00:00:00.000',
  '2009-06-02T00:00:00.000',
  '2009-10-27T00:00:00.000',
  '2014-06-03T00:00:00.000',
  '2019-06-12T00:00:00.000',
  '2007-04-27T00:00:00.000',
  '2004-06-28T00:00:00.000',
  '2002-02-05T00:00:00.000',
  '2008-03-05T00:00:00.000',
  '1997-10-01T00:00:00.000',
  '2004-12-20T00:00:00.000',
  '2019-09-19T00:00:00.000',
  '2008-12-11T00:00:00.000',
  '2002-03-25T00:00:00.000',
  '2004-07-30T00:00:00.000',
  '2008-12-31T00:00:00.000',
  '2008-09-25T00:00:00.000',
  '2011-07-06T00:00:00.000',
  '1999-03-15T00:00:00.000',
  '2010-12-13T00:00:00.000',
  '2010-07-14T00:00:00.000',
  '2021-09-02T00:00:00.000',
  '2011-11-18T00:00:00.000',
  '2020-07-27T00:00:00.000',
  '2002-05-22T00:00:00.000',
  '2009-01-16T00:00:00.000',
  '2018-12-14T00:00:00.000',
  '2003-07-03T00:00:00.000',
  '2010-05-24T00:00:00.000',
  '1999-07-21T00:00:00.000',
  '2017-05-25T00:00:00.000',
  '1998-06-30T00:00:00.000',
  '2004-06-01T00:00:00.000',
  '2005-12-22T00:00:00.000',
  '2002-09-24T00:00:00.000',
  '2003-06-30T00:00:00.000',
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  '2007-08-27T00:00:00.000',
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  '2020-08-06T00:00:00.000',
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  '2006-01-17T00:00:00.000',
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  '2014-05-20T00:00:00.000',
  '2001-08-15T00:00:00.000',
  '2010-05-03T00:00:00.000',
  '2014-03-06T00:00:00.000',
  '2012-04-04T00:00:00.000',
  '2013-03-12T00:00:00.000',
  '2006-04-19T00:00:00.000',
  '2016-09-23T00:00:00.000',
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  '1992-12-01T00:00:00.000',
  '2017-03-06T00:00:00.000',
  '2013-03-22T00:00:00.000',
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  '2014-01-27T00:00:00.000',
  '2007-11-27T00:00:00.000',
  '2004-01-30T00:00:00.000',
  '2012-11-21T00:00:00.000',
  '2010-03-08T00:00:00.000',
  '1986-09-01T00:00:00.000',
  '2003-12-23T00:00:00.000',
  '2016-10-31T00:00:00.000',
  '2012-07-13T00:00:00.000',
  '2012-03-21T00:00:00.000',
  '2000-02-02T00:00:00.000',
  '2011-11-22T00:00:00.000',
  '2018-07-18T00:00:00.000',
  '1998-05-20T00:00:00.000',
  '1998-04-01T00:00:00.000',
  '2006-10-25T00:00:00.000',
  '2012-03-02T00:00:00.000',
  '2008-01-23T00:00:00.000',
  '2002-03-04T00:00:00.000',
  '2011-06-27T00:00:00.000',
  '2007-03-28T00:00:00.000',
  '2011-09-27T00:00:00.000',
  '1999-11-01T00:00:00.000',
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  '2005-10-25T00:00:00.000',
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  '2003-08-05T00:00:00.000',
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  '2018-07-13T00:00:00.000',
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  '2017-03-09T00:00:00.000',
  '2019-04-02T00:00:00.000',
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  '2018-07-02T00:00:00.000',
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  '2006-12-07T00:00:00.000',
  '2017-10-06T00:00:00.000',
  '1990-01-01T00:00:00.000',
  '1998-07-01T00:00:00.000',
  '1996-06-01T00:00:00.000',
  '2008-02-27T00:00:00.000',
  '2007-10-03T00:00:00.000',
  '2019-09-06T00:00:00.000',
  '2017-09-29T00:00:00.000',
  '2012-09-28T00:00:00.000',
  '2015-07-31T00:00:00.000',
  '2021-01-01T00:00:00.000',
  '2010-02-24T00:00:00.000',
  '2015-10-31T00:00:00.000',
  '2010-08-30T00:00:00.000',
  '2006-11-28T00:00:00.000',
  '2001-09-17T00:00:00.000',
  '2014-04-08T00:00:00.000',
  '2025-03-31T00:00:00.000',
  ...],
 'Site Class': ['C', 'A', '04', '02', 'N', 'P', '03']}

data['Project Completion Date'] = data['Project Completion Date'].apply(lambda x: x[0:7] if str(x) != 'nan' else x)

data['Year'] = data['Project Completion Date'].apply(lambda x: np.float(x[0:4]) if str(x) != 'nan' else x)
data['Month'] = data['Project Completion Date'].apply(lambda x: np.float(x[5:7]) if str(x) != 'nan' else x)

data = data.drop('Project Completion Date', axis=1)

for column in ['New York Zip Codes 2', 'Year', 'Month', 'Counties 2']:
    data[column] = data[column].fillna(data[column].mean())

data.isna().sum()

Program Type                  0
Site Class                    0
County                        0
DEC Region                    0
Latitude                      0
Longitude                     0
Control Code                  0
Control Type                  0
OU                            0
NYS Municipal Boundaries 2    0
New York Zip Codes 2          0
Counties 2                    0
Year                          0
Month                         0
dtype: int64

エンコード

def onehot_encode(df, column):
    df = df.copy()
    dummies = pd.get_dummies(df[column])
    df = pd.concat([df, dummies], axis=1)
    df = df.drop(column, axis=1)
    return df

get_categorical_columns(data)

['Program Type', 'Site Class', 'County', 'Control Code', 'Control Type', 'OU']

nominal_features = get_categorical_columns(data)
nominal_features.remove('Program Type')

for feature in nominal_features:
    data = onehot_encode(data, feature)

label_encoder = sp.LabelEncoder()
data['Program Type'] = label_encoder.fit_transform(data['Program Type'])
target_mapppings = {index: column for index, column in enumerate(label_encoder.classes_)}
target_mapppings

{0: 'BCP', 1: 'ERP', 2: 'HW', 3: 'RCRA', 4: 'VCP'}

データの分割とスケーリング

X
DEC Region Latitude Longitude NYS Municipal Boundaries 2 New York Zip Codes 2 Counties 2 Year Month 02 03 04 A C N P Albany Allegany Bronx Broome Cattaraugus Cayuga Chautauqua Chemung Chenango Clinton Columbia Cortland Delaware Dutchess Erie Essex Franklin Fulton Genesee Greene Herkimer Jefferson Kings Lewis Livingston ... 01D 01E 01F 01H 01J 01K 01a 02 02A 02B 02C 03 03A 03B 03C 04 04A 04B 04C 04D 05 05A 06 06A 07 08 09 11 12 13 14 15 16 17 18 19 20 21 25 42
0 -0.748714 -0.963589 0.637439 -0.689064 0.093366 0.314497 1.002552 0.535128 -0.625374 -0.020588 -0.492479 -0.233580 1.819035 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
1 0.533197 1.277122 -0.140194 1.777746 1.188601 -4.472172 1.219252 -1.042891 -0.625374 -0.020588 -0.492479 4.281179 -0.549742 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
2 1.494630 1.036807 -1.930211 -0.365548 -1.735766 0.010582 1.219252 1.324138 -0.625374 -0.020588 -0.492479 -0.233580 1.819035 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 3.978476 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
3 1.174152 1.166515 -1.269289 0.399692 1.897811 0.175202 1.435952 1.324138 -0.625374 -0.020588 -0.492479 4.281179 -0.549742 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
4 -0.748714 -1.041672 0.643717 1.482227 -0.045783 0.165705 0.135751 -0.253882 -0.625374 -0.020588 -0.492479 -0.233580 1.819035 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 5.700842 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
82605 0.853675 0.314928 -0.335982 1.640874 -0.860476 -0.014745 -0.406000 -1.568898 -0.625374 -0.020588 2.030542 -0.233580 -0.549742 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 7.145222 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
82606 -0.748714 -1.052282 0.597786 1.482227 -0.050271 0.165705 -0.080949 -1.042891 -0.625374 -0.020588 -0.492479 -0.233580 1.819035 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 5.700842 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
82607 -1.069191 -0.973704 0.833642 -1.112123 0.360442 0.178368 -0.406000 -1.042891 -0.625374 -0.020588 2.030542 -0.233580 -0.549742 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
82608 0.853675 0.314928 -0.335982 1.640874 -0.860476 -0.014745 -0.731050 -1.042891 -0.625374 -0.020588 2.030542 -0.233580 -0.549742 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 7.145222 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 -0.062163 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561
82609 -0.107758 0.451908 -0.122419 0.437021 -0.853743 0.004250 -2.139601 1.061135 1.599044 -0.020588 -0.492479 -0.233580 -0.549742 -0.094942 -0.547296 -0.097821 -0.067979 -0.156868 -0.139954 -0.081267 -0.049016 -0.07772 -0.079897 -0.042793 -0.047504 -0.075805 -0.072589 16.086637 -0.166678 -0.251353 -0.017047 -0.015167 -0.060473 -0.025337 -0.038141 -0.082093 -0.061967 -0.175413 -0.013918 -0.059045 ... -0.015167 -0.012546 -0.006959 -0.009841 -0.009841 -0.006959 -0.00492 -0.186021 -0.024609 -0.012053 -0.00492 -0.138008 -0.020291 -0.008523 -0.00492 -0.087171 -0.006026 -0.00492 -0.00492 -0.00492 -0.055972 -0.006026 -0.072673 -0.006026 -0.047504 -0.058318 -0.065041 -0.093767 -0.038141 -0.04673 -0.058318 -0.044052 -0.064286 -0.144079 -0.04673 -0.02696 -0.044052 -0.115094 -0.031134 -0.015561

82610 rows × 131 columns

y = data['Program Type']
X = data.drop(['Program Type'], axis=1)

scaler = sp.StandardScaler()
X = pd.DataFrame(scaler.fit_transform(X), index=X.index, columns=X.columns)

X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)

トレーニング

X.shape

(82610, 131)

y.value_counts()

2    61673
0    14403
1     3455
4     3022
3       57
Name: Program Type, dtype: int64

model = tf.keras.Sequential([
    tf.keras.layers.Dense(64, activation='relu', input_shape=(131,)),
    tf.keras.layers.Dense(64, activation='relu'),
    tf.keras.layers.Dense(5, activation='softmax'),
])

model.summary()

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy'],
)

batch_size=64
epochs=100

history = model.fit(
    X_train,
    y_train,
    validation_split=0.2,
    batch_size=batch_size,
    epochs=epochs,
    callbacks=[tf.keras.callbacks.ReduceLROnPlateau()],
    verbose=0,
)

Model: "sequential_2"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
dense_6 (Dense)              (None, 64)                8448      
_________________________________________________________________
dense_7 (Dense)              (None, 64)                4160      
_________________________________________________________________
dense_8 (Dense)              (None, 5)                 325       
=================================================================
Total params: 12,933
Trainable params: 12,933
Non-trainable params: 0
_________________________________________________________________

plt.figure(figsize=(14, 10))

epochs_range = range(1, epochs+1)
train_loss = history.history['loss']
val_loss = history.history['val_loss']

plt.plot(epochs_range, train_loss, label='Training Loss')
plt.plot(epochs_range, val_loss, label='Validation Loss')

plt.title('Training and Validation Loss')
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.legend()

plt.show()

png

model.evaluate(X_test, y_test)

775/775 [==============================] - 1s 884us/step - loss: 0.0262 - accuracy: 0.9958





[0.026238199323415756, 0.9958440661430359]

for label in range(5):
    label_indices = y_test[y_test == label].index
    label_acc = model.evaluate(X_test.loc[label_indices, :], y_test.loc[label_indices], verbose=0)
    print(f'Class {label} Accuracy: {label_acc[1]}')

Class 0 Accuracy: 0.9960345029830933
Class 1 Accuracy: 0.9609895348548889
Class 2 Accuracy: 0.9981631636619568
Class 3 Accuracy: 0.9230769276618958
Class 4 Accuracy: 0.9891657829284668

すべてのタイプで良い精度が出ていることを確認できました。

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