吴恩达《机器学习》版 第一节第二周 多元线性回归 房价预测简单实现
以下以下共两个实验，都是通过调用sklearn函数，分别实现了 一元线性回归和多元线性回归的房价预测。
一、一元线性回归
import numpy as npnp.set_printoptions(precision=2)from sklearn.linear_model import LinearRegression#输入数据X_train = np.array([1.0, 2.0]) y_train = np.array([300, 500]) #调用线性回归模型linear_model = LinearRegression()linear_model.fit(X_train.reshape(-1, 1), y_train) #计算系数和截距b = linear_model.intercept_w = linear_model.coef_print(f"w = {w:}, b = {b:0.2f}")print(f"'manual' prediction: f_wb = wx+b : {1200*w + b}")y_pred = linear_model.predict(X_train.reshape(-1, 1))print("Prediction on training set:", y_pred)#预测X_test = np.array([[1200]])print(f"Prediction for 1200 sqft house: ${linear_model.predict(X_test)[0]:0.2f}")
实验结果
#输出结果w = [200.], b = 100.00'manual' prediction: f_wb = wx+b : [240100.]Prediction on training set: [300. 500.]Prediction for 1200 sqft house: $240100.00
二、多元线性归回
import numpy as npnp.set_printoptions(precision=2)from sklearn.linear_model import LinearRegressiondef load_house_data():data = np.loadtxt("houses.txt", delimiter=',', skiprows=1)X = data[:,:4]y = data[:,4]return X, y#输入数据X_train, y_train = load_house_data()X_features = ['size(sqft)','bedrooms','floors','age']#调用线性回归模型linear_model = LinearRegression()linear_model.fit(X_train, y_train) #计算参数b = linear_model.intercept_w = linear_model.coef_print(f"w = {w:}, b = {b:0.2f}")#输出模型参数print(f"Prediction on training set:\n {linear_model.predict(X_train)[:4]}" )print(f"prediction using w,b:\n {(X_train @ w + b)[:4]}")print(f"Target values \n {y_train[:4]}")#预测x_house = np.array([1200, 3,1, 40]).reshape(-1,4)x_house_predict = linear_model.predict(x_house)[0]print(f" predicted price of a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old = ${x_house_predict*1000:0.2f}")
实验结果
#输出结果w = [ 0.27 -32.62 -67.25 -1.47], b = 220.42Prediction on training set:[295.18 485.98 389.52 492.15]prediction using w,b:[295.18 485.98 389.52 492.15]Target values [300. 509.8 394. 540. ]predicted price of a house with 1200 sqft, 3 bedrooms, 1 floor, 40 years old = $318709.09
houses.txt 数据
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