코딩걸음마

보스턴 집값예측 데이터로 DNN을 활용한 회귀식모델을 만들어봅시다. 1. 데이터 준비하기 import pandas as pd import seaborn as sns import matplotlib.pyplot as plt from sklearn.preprocessing import StandardScaler from sklearn.datasets import load_boston boston = load_boston() df = pd.DataFrame(boston.data, columns=boston.feature_names) df["TARGET"] = boston.target scaler = StandardScaler() scaler.fit(df.values[:, :-1]) df.values[:, :-..

1. Data 불러오기 !pip install matplotlib seaborn pandas sklearn import pandas as pd import seaborn as sns import matplotlib.pyplot as plt 선형회귀에 사용할 sample은 유명한 boston 주택가격 예측입니다. from sklearn.datasets import load_boston boston = load_boston() print(boston.DESCR) 2. Data 확인하기 .. _boston_dataset: Boston house prices dataset --------------------------- **Data Set Characteristics:** :Number of Instances..