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@FinanceData
FinanceData / notebook.ipynb
Last active June 6, 2021 15:02
Keras 08 - LSTM - 다양한 구성.ipynb
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@FinanceData
FinanceData / README.MD
Last active July 26, 2020 09:52
Predictive Bank Marketing Analysis (UCI datasets)

Bank Marketing

  • bank.xlxs (train:7000 rows, test:3000 rows)
import pandas as pd  

train = pd.read_excel('http://bit.ly/2Oi4nq8', sheet_name='train')      
test = pd.read_excel('http://bit.ly/2Oi4nq8', sheet_name='test')        

len(train), len(test) # (7000, 3000)
@FinanceData
FinanceData / 공시검색_상세유형.md
Created January 23, 2020 15:52
공시검색 상세유형

공시검색 상세유형

@FinanceData
FinanceData / Tutorial_01_Basic_Classification.ipynb
Created July 13, 2019 23:54
Tutorial_01_Basic_Classification
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@FinanceData
FinanceData / readme.md
Last active October 7, 2018 12:18
requirements.txt 예시

사용예시 (주피터 노트북)

!curl -L -o requirements.txt https://goo.gl/cBcBje
!pip install -q -r requirements.txt
@FinanceData
FinanceData / readme.md
Created June 22, 2018 03:48
pandas-datareader 설치

pandas-datareader 설치

@FinanceData
FinanceData / README.md
Created April 2, 2018 08:38
윈도우 콘솔 인코딩

949 (유니코드)

chcp 65001

949 (한국어)

chcp 949 
@FinanceData
FinanceData / LG화학.csv
Last active March 30, 2018 04:47
예제 데이터
We can make this file beautiful and searchable if this error is corrected: Unclosed quoted field in line 10.
년/월/일,종가,대비,거래량(주),거래대금(원),시가,고가,저가,시가총액(백만),상장주식수(주)
2017/12/28,"405,000","10,500","169,517","68,358,962,775","396,500","407,000","395,000","28,589,899","70,592,343"
2017/12/27,"394,500","2,500","138,310","54,352,710,500","394,000","395,000","391,000","27,848,679","70,592,343"
2017/12/26,"392,000","-1,500","267,020","105,171,154,250","393,500","395,000","392,000","27,672,198","70,592,343"
2017/12/22,"393,500","2,500","134,256","52,831,746,500","391,500","395,000","389,500","27,778,087","70,592,343"
2017/12/21,"391,000","-7,000","123,480","48,508,597,000","398,500","398,500","391,000","27,601,606","70,592,343"
2017/12/20,"398,000","-500","142,948","56,797,800,000","396,500","398,500","395,000","28,095,753","70,592,343"
2017/12/19,"398,500","1,000","135,439","53,811,949,500","399,500","399,500","395,500","28,131,049","70,592,343"
2017/12/18,"397,500","3,500","158,523","62,751,474,500","396,000","398,000","393,000","28,060,456","70,592,343"
2017/12/15,"394,000","4,000","197,159","77,749,749,500","395,000","397,
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