回歸算法-波士頓房價預測

from sklearn.datasets import load_boston import numpy as np import pandas as pd import matplotlib.pyplot as plt from sklearn.linear_model import LassoCV import seaborn as sns house = load_boston() print(house.DESCR)

x = house.data y = house.target df = pd.DataFrame(x, columns=house.feature_names) df['Target'] = pd.DataFrame(y, columns=['Target']) df.head()

plt.figure(figsize=(15,15)) # 畫熱力圖，數值為兩個變量之間的相關系數 p=sns.heatmap(df.corr(), annot=True, square=True) # 數據標準化 from sklearn.preprocessing import StandardScalerss = StandardScaler() x = ss.fit_transform(x) print(x[:5])

from sklearn.model_selection import train_test_split # 切分數據集 x_train,x_test,y_train,y_test = train_test_split(x,y,test_size=0.3) # 創建模型 model = LassoCV() model.fit(x_train, y_train)# lasso系數 print(model.alpha_) # 相關系數 print(model.coef_)

model.score(x_test, y_test)

回歸算法-葡萄酒質量和時間關系

import numpy as np import matplotlib.pyplot as plt from sklearn.linear_model import LinearRegression from sklearn.model_selection import train_test_split # 載入數據 data = np.genfromtxt('linear.csv', delimiter=',') # 畫圖 plt.scatter(data[1:,0],data[1:,1]) plt.title('Age Vs Quality (Test set)') plt.xlabel('Age') plt.ylabel('Quality') plt.show()

# 數據拆分 x_train, x_test, y_train, y_test = train_test_split(data[1:, 0], data[1:, 1], test_size = 0.3) # 1D->2D，給數據增加一個維度，主要是訓練模型的時候，函數要求傳入2維的數據 x_train = x_train[:, np.newaxis] x_test = x_test[:, np.newaxis] # 訓練模型 model = LinearRegression() model.fit(x_train, y_train)

# 訓練集的散點圖 plt.scatter(x_train, y_train, color = 'b') # 模型對訓練集的預測結果 plt.plot(x_train,model.predict(x_train), color ='r' , linewidth=5) # 畫表頭和xy坐標描述 plt.title('Age Vs Quality (Training set)') plt.xlabel('Age') plt.ylabel('Quality') plt.show()

# 測試集的散點圖 plt.scatter(x_test, y_test, color = 'b') # 模型對測試集的預測結果 plt.plot(x_test,model.predict(x_test), color ='r', linewidth=5) # 畫表頭和xy坐標描述 plt.title('Age Vs Quality (Test set)') plt.xlabel('Age') plt.ylabel('Quality') plt.show()

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