이상향

데이터 출처 : https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists HR Analytics: Job Change of Data Scientists Predict who will move to a new job www.kaggle.com import pandas as pd x_train = pd.read_csv("https://raw.githubusercontent.com/Datamanim/datarepo/main/HRdata/X_train.csv") y_train = pd.read_csv("https://raw.githubusercontent.com/Datamanim/datarepo/main/HRdata/y..

데이터 출처 : https://www.kaggle.com/datasets/shubh0799/churn-modelling Churn Modelling Deep Learning Artificial Neural Network Used. www.kaggle.com import pandas as pd x_train = pd.read_csv("https://raw.githubusercontent.com/Datamanim/datarepo/main/churnk/X_train.csv") y_train = pd.read_csv("https://raw.githubusercontent.com/Datamanim/datarepo/main/churnk/y_train.csv") x_test= pd.read_csv("https://r..

# pandas 문법으로 풀이 import pandas as pd a = pd.read_csv('data/mtcars.csv', index_col=0) # Min Max Scale은 (X-min) / (max-min) qsec_max = a['qsec'].max() qsec_min = a['qsec'].min() a['minmax_scaled_qsec'] = (a['qsec'] - qsec_min) / (qsec_max - qsec_min) over_0_5 = a[a['minmax_scaled_qsec'] > 0.5] nrows = over_0_5.shape[0] print(f'Pandas 문법 사용 했을 때 0.5보다 큰 레코드 수는 {nrows}') # sklearn의 MinMaxScaler 사용 i..