인프런 커뮤니티 질문&답변

안녕하세요!

1. 작업형 2 모의문제 2의 마지막에 csv파일을 제출할 때 에러가 발생하는데, 어떤 이유인지 찾아봐도 안보여 질문드립니다.

    

2. 그리고 작업형 2유형의 train과 test데이터 파일만 주어진 경우, 어떤 문제는 n_train, c_train, n_test, c_test로 나누는 문제가 있고 그렇지 않은 문제들이 있어서 헷갈립니다ㅠ 그래서 문제를 읽고 데이터를 분리해야하는 상황이 궁금합니다!

import pandas as pd
train = pd.read_csv('train.csv')
test = pd.read_csv('test.csv')

# print(train.head())
# print(test.head())
# print(train.info()) 1. 불필요한 컬럼제거 2. 결측치 대치 3. 인코딩 4. 스케일링
# print(test.info())

train = train.drop(columns ='id')
test_id = test.pop('id')

# print(test_id)

cols = ['host_id','neighbourhood_group','neighbourhood','name', 'host_name', 'last_review']
# print(train.shape)
train = train.drop(cols, axis =1)
test = test.drop(cols, axis =1)
# print(train.shape)

# train = train.drop(columns ='host_id')
# test = test.drop(columns ='host_id')
# train = train.drop(columns ='neighbourhood_group')
# test = test.drop(columns ='neighbourhood_group')
# train = train.drop(columns ='neighbourhood')
# test = test.drop(columns ='neighbourhood')
# train = train.drop(columns ='name')
# test = test.drop(columns ='name')
# train = train.drop(columns ='host_name')
# test = test.drop(columns ='host_name')
# train = train.drop(columns ='last_review')
# test = test.drop(columns ='last_review')

# print(train.info())
# print(test.info())
# print(train.isnull().sum())
# print(test.isnull().sum())

train['reviews_per_month'] = train['reviews_per_month'].fillna(0)
test['reviews_per_month'] = test['reviews_per_month'].fillna(0)

# print(train.isnull().sum())
# print(test.isnull().sum())

#라벨 인코딩
from sklearn.preprocessing import LabelEncoder

cols =train.select_dtypes(include = 'object').columns

for col in cols:
  encoder = LabelEncoder()
  train[col] = encoder.fit_transform(train[col])
  test[col] = encoder.transform(test[col])

# print(train.info())
# print(test.info())

# print(train.describe())
# print(test.describe())

# 스케일링
from sklearn.preprocessing import minmax_scale

cols2 = train.select_dtypes(exclude = 'object').columns

for col in cols2:
  train[col] = minmax_scale(train[col])

cols2 = test.select_dtypes(exclude = 'object').columns

for col in cols2:
  test[col] = minmax_scale(test[col])

# print(train.describe())
# print(test.describe())

from sklearn.model_selection import train_test_split

X_train, X_val, y_train, y_val = train_test_split(train.drop('price', axis=1), train['price'], test_size=0.2, random_state=20)

# print(X_train.head())

from sklearn.ensemble import RandomForestRegressor
rf = RandomForestRegressor()

rf.fit(X_train, y_train)
pred = rf.predict(X_val)
# print(pred)

#id,price

sumit = pd.DataFrame({'id': test_id, 'price' : pred})
submit.to_csv('10004.csv', index=False)