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# µû¶óÇÏ¸ç ¹è¿ì´Â ÆÄÀ̽ã°ú µ¥ÀÌÅͰúÇÐ(»ý´ÉÃâÆÇ»ç 2020)
# 14.9 »çÀÌŶ·±ÀÇ ´ç´¢º´ ¿¹Á¦¿Í ÇнÀ µ¥ÀÌÅÍ »ý¼º, 377ÂÊ
#
import matplotlib.pyplot as plt
import numpy as np
from sklearn.linear_model import LinearRegression
from sklearn import datasets
# ´ç´¢º´ µ¥ÀÌÅÍ ¼¼Æ®¸¦ sklearnÀÇ µ¥ÀÌÅÍÁýÇÕÀ¸·ÎºÎÅÍ ÀоîµéÀδÙ.
diabetes = datasets.load_diabetes()
print('shape of diabetes.data: ', diabetes.data.shape)
print(diabetes.data)
print('ÀԷµ¥ÀÌÅÍÀÇ Æ¯¼ºµé')
print(diabetes.feature_names)
print('target data y:', diabetes.target.shape)
print(diabetes.target)
X = diabetes.data[:, np.newaxis, 2] # ¹è¿ÀÇ Â÷¿øÀ» Áõ°¡½ÃÅ´
print(X) # (442, 1) ÇüÅÂÀÇ Çà·ÄÀÌ µÇ¾ú´Â°¡ È®ÀÎ