目录
- import numpy as np
- from sklearn import datasets
- from sklearn.linear_model import LinearRegression
-
- # 指定版本才有数据集
- # C:\Users\14817\PycharmProjects\pythonProject1\venv\Scripts\activate.bat
- # pip install scikit-learn==1.0
- # FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.
-
- boston = datasets.load_boston()
-
- X = boston['data'] # 数据
- y = boston['target'] # 房价
- feature_names = boston['feature_names'] # 具体指标
-
- # 切分数据
- index = np.array(range(506))
- np.random.shuffle(index)
-
- train_index = index[:405]
- test_index = index[405:]
-
- # 80%的训练数据
- X_train = X[train_index]
- y_train = y[train_index]
-
- X_test = X[test_index]
- y_test = y[test_index]
-
- # 数据建模
- np.set_printoptions(suppress=True)
- model = LinearRegression(fit_intercept=True)
- model.fit(X_train, y_train)
-
- # 模型应用
- y_pred = model.predict(X_test).round(2)
- print(y_pred)
- print(y_test)
-
- #模型评分
- #负数到1之间 ,1 最高分
- score = model.score(X_test,y_test)
- print(score)
