머신러닝 기본적인 LinearRegression Code

1. Data 만들기 - "data2.csv" 

 

 

2. LinearRegression Code 

import pandas as pd
import numpy as np
 
df_data = pd.read_csv("data2.csv")
x_data = df_data.drop(['y'],axis=1)
y_data = df_data['y']
 
print(x_data.head(5))
print(y_data.head(5))
 
from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
 
lin_reg = LinearRegression()
 
scores = cross_val_score(lin_reg, x_data, y_data, cv=10, n_jobs=-1, scoring = "neg_mean_squared_error")
scores_RFR_scores = np.sqrt(-scores)
RF_result = scores_RFR_scores.mean()
print("RF_result",RF_result )

- "data2.csv" 파일 불러오기

- x_data, y_data 생성

- LinearRegression과 cross_val_score사용

- cv = 10번 나눠서 , "neg_mean_squared_error" 사용