機器學習中運用python進行對房子價格的預測程式碼，資料庫直接使用sklearn自帶的boston，使用三種方法進行預測，分別是：線性迴歸直接預測、梯度下降預測、嶺迴歸預測

from sklearn.datasets import load_boston
from sklearn.linear_model import LinearRegression, SGDRegressor,Ridge
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.metrics import mean_squared_error


def mylinear():
    """
    線性迴歸直接預測房子價格
    :return: None
    """

    # 獲取資料
    lb = load_boston()

    # 分割資料集到訓練集和測試集
    x_train, x_test, y_train, y_test = train_test_split(lb.data, lb.target, test_size=0.25)

    # print(y_train, y_test)

    # 進行標準化處理(?)目標值處理？
    # 特徵值和目標值都必須進行標準化處理,例項化兩個標準化API
    std_x = StandardScaler()

    x_train = std_x.fit_transform(x_train)
    x_test = std_x.transform(x_test)

    # 目標值
    std_y = StandardScaler()

    y_train = std_y.fit_transform(y_train.reshape(-1, 1))
    y_test = std_y.transform(y_test.reshape(-1, 1))

    # estimator預測
    # 正規方程求解方式預測結果
    lr = LinearRegression()

    lr.fit(x_train, y_train)

    print(lr.coef_)



    # 預測測試集房子價格
    y_lr_predict = std_y.inverse_transform(lr.predict(x_test))

    print("測試集裡面每個房子的預測價格：", y_lr_predict)

    print("正規方程的均方誤差：",mean_squared_error(std_y.inverse_transform(y_test), y_lr_predict))
    # 梯度下降去預測房價
    sgd = SGDRegressor()

    sgd.fit(x_train, y_train)

    print(sgd.coef_)



    # 預測測試集房子價格
    y_sgd_predict = std_y.inverse_transform(sgd.predict(x_test))

    print("測試集裡面每個房子的預測價格：", y_sgd_predict)

    print("梯度下降方程的均方誤差：", mean_squared_error(std_y.inverse_transform(y_test), y_sgd_predict))

    # 嶺迴歸去預測房價
    rd = Ridge()

    rd.fit(x_train, y_train)

    print(rd.coef_)



    # 預測測試集房子價格
    y_rd_predict = std_y.inverse_transform(rd.predict(x_test))

    print("測試集裡面每個房子的預測價格：", y_rd_predict)

    print("嶺迴歸方程的均方誤差：", mean_squared_error(std_y.inverse_transform(y_test), y_rd_predict))



    return None




if __name__ == '__main__':
    mylinear()