인프런 커뮤니티 질문&답변

원핫 인코딩시, train 과 test의 컬럼의수가 달라지는 경우가 있어서 데이터를 concat 했다가 다시 분리를 해주고 있는데요.

아래와 같이 코딩시 답안 제출까지는 가능하지만, 강사님께서 풀이한 것과 값차이가 많이 납니다.

이렇게 하면 안되나요?

import pandas as pd train = pd.read_csv('churn_train.csv') test = pd.read_csv('churn_test.csv') y=train.pop('TotalCharges') print (train.shape, test.shape, y.shape) total = pd.concat([train, test], axis=0) total=total.drop('customerID',axis=1) # print (total.shape) cols=total.select_dtypes(include = 'object').columns total = pd.get_dummies (total[cols]) train = total[:4116] test = total[4116:] print (train.shape, test.shape, y.shape) from sklearn.model_selection import train_test_split X_train, X_val, y_train, y_val = train_test_split(train, y, test_size=0.2, random_state=0) print(X_train.shape, X_val.shape, y_train.shape, y_val.shape) from sklearn.ensemble import RandomForestRegressor rf = RandomForestRegressor() rf.fit(X_train, y_train) pred=rf.predict(X_val) pred from sklearn.metrics import mean_absolute_error mae = mean_absolute_error (y_val, pred) print(mae) # 결과값 제출 result = rf.predict(test) # print(result.shape, test.shape) print(result) result = pd.DataFrame ({ 'total price':result }) result.to_csv("result.csv", index=False) print(result) # help(sklearn) # import sklearn # help(sklearn.metrics )