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机器学习_丰田卡罗拉价格回归分析案例

青葱年少 • • 技术文章 • 阅读 662

使用丰田卡罗拉数据集构建了4个回归模型。这些是线性回归、多项式回归、岭回归、套索回归，然后衡量并可视化模型的性能。借鉴黄海广老师的课件资料。

1. 概述

数据栏：
Age: 车龄
KM: 累计里程
FuelType: 燃油类型 (Petrol, Diesel, CNG)
HP: 功率
MetColor: 是否金属漆 (Yes=1, No=0)
Automatic: 是否自动挡( (Yes=1, No=0)
CC: 排量
Doors: 车门数量
Weight: 整车重量
Price: 售价（欧元）

2、导入库并导入数据集

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score
from collections import Counter
from IPython.core.display import display, HTML
sns.set_style('white')
import warnings
warnings.filterwarnings("ignore")#忽略警告
dataset = pd.read_csv('data/ToyotaCorolla.csv')
dataset.head()

机器学习_丰田卡罗拉价格回归分析案例

dataset.count()

机器学习_丰田卡罗拉价格回归分析案例

dataset.describe()

机器学习_丰田卡罗拉价格回归分析案例

3、数据处理和可视化

dataset.isnull().sum()

机器学习_丰田卡罗拉价格回归分析案例

corr = dataset.corr()
#Plot figsize
fig, ax = plt.subplots(figsize=(8, 8))
#Generate Heat Map, allow annotations and place floats in map
sns.heatmap(corr, cmap='magma', annot=True, fmt=".2f")
#Apply xticks
plt.xticks(range(len(corr.columns)), corr.columns);
#Apply yticks
plt.yticks(range(len(corr.columns)), corr.columns)
#show plot
plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(2, 2, figsize=(12, 8))

sns.regplot(x='Price',
            y='Age',
            data=dataset,
            scatter_kws={'alpha': 0.6},
            ax=axes[0, 0])
axes[0, 0].set_xlabel('Price', fontsize=14)
axes[0, 0].set_ylabel('Age', fontsize=14)
axes[0, 0].yaxis.tick_left()

sns.regplot(x='Price',
            y='KM',
            data=dataset,
            scatter_kws={'alpha': 0.6},
            ax=axes[0, 1])
axes[0, 1].set_xlabel('Price', fontsize=14)
axes[0, 1].set_ylabel('KM', fontsize=14)
axes[0, 1].yaxis.set_label_position("right")
axes[0, 1].yaxis.tick_right()

sns.regplot(x='Price',
            y='Weight',
            data=dataset,
            scatter_kws={'alpha': 0.6},
            ax=axes[1, 0])
axes[1, 0].set_xlabel('Price', fontsize=14)
axes[1, 0].set_ylabel('Weight', fontsize=14)

sns.regplot(x='Price',
            y='HP',
            data=dataset,
            scatter_kws={'alpha': 0.6},
            ax=axes[1, 1])
axes[1, 1].set_xlabel('Price', fontsize=14)
axes[1, 1].set_ylabel('HP', fontsize=14)
axes[1, 1].yaxis.set_label_position("right")
axes[1, 1].yaxis.tick_right()
axes[1, 1].set(ylim=(40, 160))

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(1,2,figsize=(14,4))

sns.distplot(dataset['KM'], ax = axes[0])
axes[0].set_xlabel('KM', fontsize=14)
axes[0].set_ylabel('Count', fontsize=14)
axes[0].yaxis.tick_left()

sns.scatterplot(x = 'Price', y = 'KM', data = dataset, ax = axes[1])
axes[1].set_xlabel('Price', fontsize=14)
axes[1].set_ylabel('KM', fontsize=14)
axes[1].yaxis.set_label_position("right")
axes[1].yaxis.tick_right()

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

fuel_list= Counter(dataset['FuelType'])
labels = fuel_list.keys()
sizes = fuel_list.values()

f, axes = plt.subplots(1,2,figsize=(14,4))

sns.countplot(dataset['FuelType'], ax = axes[0], palette="Set1")
axes[0].set_xlabel('Fuel Type', fontsize=14)
axes[0].set_ylabel('Count', fontsize=14)
axes[0].yaxis.tick_left()

sns.violinplot(x = 'FuelType', y = 'Price', data = dataset, ax = axes[1])
axes[1].set_xlabel('Fuel Type', fontsize=14)
axes[1].set_ylabel('Price', fontsize=14)
axes[1].yaxis.set_label_position("right")
axes[1].yaxis.tick_right()

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(1,2,figsize=(14,4))

sns.distplot(dataset['HP'], ax = axes[0])
axes[0].set_xlabel('HP', fontsize=14)
axes[0].set_ylabel('Count', fontsize=14)
axes[0].yaxis.tick_left()

sns.scatterplot(x = 'HP', y = 'Price', data = dataset, ax = axes[1])
axes[1].set_xlabel('HP', fontsize=14)
axes[1].set_ylabel('Price', fontsize=14)
axes[1].yaxis.set_label_position("right")
axes[1].yaxis.tick_right()

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(1, 2, figsize=(14, 4))

sns.distplot(dataset['MetColor'], ax=axes[0])
axes[0].set_xlabel('MetColor', fontsize=14)
axes[0].set_ylabel('Count', fontsize=14)
axes[0].yaxis.tick_left()

sns.boxplot(x='MetColor', y='Price', data=dataset, ax=axes[1])
axes[1].set_xlabel('MetColor', fontsize=14)
axes[1].set_ylabel('Price', fontsize=14)
axes[1].yaxis.set_label_position("right")
axes[1].yaxis.tick_right()

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(1, 2, figsize=(14, 4))

sns.distplot(dataset['Automatic'], ax=axes[0])
axes[0].set_xlabel('Automatic', fontsize=14)
axes[0].set_ylabel('Count', fontsize=14)
axes[0].yaxis.tick_left()

sns.boxenplot(x='Automatic', y='Price', data=dataset, ax=axes[1])
axes[1].set_xlabel('Automatic', fontsize=14)
axes[1].set_ylabel('Price', fontsize=14)
axes[1].yaxis.set_label_position("right")
axes[1].yaxis.tick_right()

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(1, 2, figsize=(14, 4))

sns.distplot(dataset['CC'], ax=axes[0])
axes[0].set_xlabel('CC', fontsize=14)
axes[0].set_ylabel('Count', fontsize=14)
axes[0].yaxis.tick_left()

sns.boxplot(x='CC', y='Price', data=dataset, ax=axes[1])
axes[1].set_xlabel('CC', fontsize=14)
axes[1].set_ylabel('Price', fontsize=14)
axes[1].yaxis.set_label_position("right")
axes[1].yaxis.tick_right()

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(1, 2, figsize=(14, 4))

sns.distplot(dataset['Doors'], ax=axes[0])
axes[0].set_xlabel('Doors', fontsize=14)
axes[0].set_ylabel('Count', fontsize=14)
axes[0].yaxis.tick_left()

sns.boxenplot(x='Doors', y='Price', data=dataset, ax=axes[1])
axes[1].set_xlabel('Doors', fontsize=14)
axes[1].set_ylabel('Price', fontsize=14)
axes[1].yaxis.set_label_position("right")
axes[1].yaxis.tick_right()

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

dataset = pd.get_dummies(dataset)
dataset.head()

机器学习_丰田卡罗拉价格回归分析案例

X = dataset.drop('Price', axis = 1).values
y = dataset.iloc[:, 0].values.reshape(-1,1)
#Splitting the dataset into the Training set and Test set
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test = train_test_split(X,
                                                    y,
                                                    test_size=0.25,
                                                    random_state=42)
print("Shape of X_train: ",X_train.shape)
print("Shape of X_test: ", X_test.shape)
print("Shape of y_train: ",y_train.shape)
print("Shape of y_test",y_test.shape)

Shape of X_train: (1077, 11)
Shape of X_test: (359, 11)
Shape of y_train: (1077, 1)
Shape of y_test (359, 1)

4.回归模型

线性回归

from sklearn.linear_model import LinearRegression
regressor_linear = LinearRegression()
regressor_linear.fit(X_train, y_train)

LinearRegression()

from sklearn.metrics import r2_score

#Predicting Cross Validation Score the Test set results
cv_linear = cross_val_score(estimator=regressor_linear,
                            X=X_train,
                            y=y_train,
                            cv=10)

#Predicting R2 Score the Train set results
y_pred_linear_train = regressor_linear.predict(X_train)
r2_score_linear_train = r2_score(y_train, y_pred_linear_train)

#Predicting R2 Score the Test set results
y_pred_linear_test = regressor_linear.predict(X_test)
r2_score_linear_test = r2_score(y_test, y_pred_linear_test)

#Predicting RMSE the Test set results
rmse_linear = (np.sqrt(mean_squared_error(y_test, y_pred_linear_test)))
print("CV: ", cv_linear.mean())
print('R2_score (train): ', r2_score_linear_train)
print('R2_score (test): ', r2_score_linear_test)
print("RMSE: ", rmse_linear)

CV: 0.8480754345159047
R2_score (train): 0.8702260786694702
R2_score (test): 0.8621869690956068
RMSE: 1398.4596051422188

二阶多项式回归

from sklearn.preprocessing import PolynomialFeatures
poly_reg = PolynomialFeatures(degree = 2)
X_poly = poly_reg.fit_transform(X_train)
poly_reg.fit(X_poly, y_train)
regressor_poly2 = LinearRegression()
regressor_poly2.fit(X_poly, y_train)
LinearRegression()
from sklearn.metrics import r2_score

#Predicting Cross Validation Score the Test set results
cv_poly2 = cross_val_score(estimator = regressor_poly2, X = X_train, y = y_train, cv = 10)

#Predicting R2 Score the Train set results
y_pred_poly2_train = regressor_poly2.predict(poly_reg.fit_transform(X_train))
r2_score_poly2_train = r2_score(y_train, y_pred_poly2_train)

#Predicting R2 Score the Test set results
y_pred_poly2_test = regressor_poly2.predict(poly_reg.fit_transform(X_test))
r2_score_poly2_test = r2_score(y_test, y_pred_poly2_test)

#Predicting RMSE the Test set results
rmse_poly2 = (np.sqrt(mean_squared_error(y_test, y_pred_poly2_test)))
print('CV: ', cv_poly2.mean())
print('R2_score (train): ', r2_score_poly2_train)
print('R2_score (test): ', r2_score_poly2_test)
print("RMSE: ", rmse_poly2)

CV: 0.8480754345159047
R2_score (train): 0.9157086185553889
R2_score (test): 0.7619825755103794
RMSE: 1837.8461795439769

岭回归

from sklearn.linear_model import Ridge
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures

steps = [
    ('scalar', StandardScaler()),
    ('poly', PolynomialFeatures(degree=3)),
    ('model', Ridge(alpha=1777, fit_intercept=True))
]

ridge_pipe = Pipeline(steps)
ridge_pipe.fit(X_train, y_train)
Pipeline(steps=[('scalar', StandardScaler()),
                ('poly', PolynomialFeatures(degree=3)),
                ('model', Ridge(alpha=1777))])
from sklearn.metrics import r2_score

#Predicting Cross Validation Score the Test set results
cv_ridge = cross_val_score(estimator = ridge_pipe, X = X_train, y = y_train.ravel(), cv = 10)

#Predicting R2 Score the Test set results
y_pred_ridge_train = ridge_pipe.predict(X_train)
r2_score_ridge_train = r2_score(y_train, y_pred_ridge_train)

#Predicting R2 Score the Test set results
y_pred_ridge_test = ridge_pipe.predict(X_test)
r2_score_ridge_test = r2_score(y_test, y_pred_ridge_test)

#Predicting RMSE the Test set results
rmse_ridge = (np.sqrt(mean_squared_error(y_test, y_pred_ridge_test)))
print('CV: ', cv_ridge.mean())
print('R2_score (train): ', r2_score_ridge_train)
print('R2_score (test): ', r2_score_ridge_test)
print("RMSE: ", rmse_ridge)

CV: 0.7785178588873436
R2_score (train): 0.87000985560043
R2_score (test): 0.8697806448706517
RMSE: 1359.3852529159908

套索回归

from sklearn.linear_model import Lasso
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import PolynomialFeatures

steps = [('scalar', StandardScaler()), ('poly', PolynomialFeatures(degree=3)),
         ('model',
          Lasso(alpha=2.36, fit_intercept=True, tol=0.0199, max_iter=2000))]

lasso_pipe = Pipeline(steps)
lasso_pipe.fit(X_train, y_train)
Pipeline(steps=[('scalar', StandardScaler()),
                ('poly', PolynomialFeatures(degree=3)),
                ('model', Lasso(alpha=2.36, max_iter=2000, tol=0.0199))])
from sklearn.metrics import r2_score

# Predicting Cross Validation Score
cv_lasso = cross_val_score(estimator = lasso_pipe, X = X_train, y = y_train, cv = 10)

# Predicting R2 Score the Test set results
y_pred_lasso_train = lasso_pipe.predict(X_train)
r2_score_lasso_train = r2_score(y_train, y_pred_lasso_train)

# Predicting R2 Score the Test set results
y_pred_lasso_test = lasso_pipe.predict(X_test)
r2_score_lasso_test = r2_score(y_test, y_pred_lasso_test)

# Predicting RMSE the Test set results
rmse_lasso = (np.sqrt(mean_squared_error(y_test, y_pred_lasso_test)))
print('CV: ', cv_lasso.mean())
print('R2_score (train): ', r2_score_lasso_train)
print('R2_score (test): ', r2_score_lasso_test)
print("RMSE: ", rmse_lasso)

CV: 0.7427712620107894
R2_score (train): 0.9273633923675705
R2_score (test): 0.9022945020939632
RMSE: 1177.509135460343
5.衡量误差

models = [
    ('Linear Regression', rmse_linear, r2_score_linear_train,
     r2_score_linear_test, cv_linear.mean()),
    ('Polynomial Regression (2nd)', rmse_poly2, r2_score_poly2_train,
     r2_score_poly2_test, cv_poly2.mean()),
    ('Ridge Regression', rmse_ridge, r2_score_ridge_train, r2_score_ridge_test,
     cv_ridge.mean()),
    ('Lasso Regression', rmse_lasso, r2_score_lasso_train, r2_score_lasso_test,
     cv_lasso.mean()),

]
predict = pd.DataFrame(data = models, columns=['Model', 'RMSE', 'R2_Score(training)', 'R2_Score(test)', 'Cross-Validation'])
predict

机器学习_丰田卡罗拉价格回归分析案例

5、模型性能可视化

f, axe = plt.subplots(1,1, figsize=(18,6))

predict.sort_values(by=['Cross-Validation'], ascending=False, inplace=True)

sns.barplot(x='Cross-Validation', y='Model', data = predict, ax = axe)
#axes[0].set(xlabel='Region', ylabel='Charges')
axe.set_xlabel('Cross-Validaton Score', size=16)
axe.set_ylabel('Model',size=16)
axe.set_xlim(0,1.0)
axe.set_xticks(np.arange(0, 1.1, 0.1))
plt.show()

机器学习_丰田卡罗拉价格回归分析案例

f, axes = plt.subplots(2, 1, figsize=(14, 10))

predict.sort_values(by=['R2_Score(training)'], ascending=False, inplace=True)

sns.barplot(x='R2_Score(training)',
            y='Model',
            data=predict,
            palette='Blues_d',
            ax=axes[0])
#axes[0].set(xlabel='Region', ylabel='Charges')
axes[0].set_xlabel('R2 Score (Training)', size=16)
axes[0].set_ylabel('Model', size=16)
axes[0].set_xlim(0, 1.0)
axes[0].set_xticks(np.arange(0, 1.1, 0.1))

predict.sort_values(by=['R2_Score(test)'], ascending=False, inplace=True)

sns.barplot(x='R2_Score(test)',
            y='Model',
            data=predict,
            palette='Reds_d',
            ax=axes[1])
#axes[0].set(xlabel='Region', ylabel='Charges')
axes[1].set_xlabel('R2 Score (Test)', size=16)
axes[1].set_ylabel('Model', size=16)
axes[1].set_xlim(0, 1.0)
axes[1].set_xticks(np.arange(0, 1.1, 0.1))

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

predict.sort_values(by=['RMSE'], ascending=False, inplace=True)

f, axe = plt.subplots(1, 1, figsize=(10, 6))
sns.barplot(x='Model', y='RMSE', data=predict, ax=axe)
axe.set_xlabel('Model', size=16)
axe.set_ylabel('RMSE', size=16)

plt.show()

机器学习_丰田卡罗拉价格回归分析案例

6.结论

在这段代码中，我使用丰田卡罗拉数据集构建了4个回归模型。这些是线性回归、多项式回归、岭回归、套索回归，然后衡量并可视化模型的性能。

版权声明：本文为博主凌贤文原创文章，版权归属原作者，如果侵权，请联系我们删除！

原文链接：https://blog.csdn.net/lxwssjszsdnr_/article/details/122990902

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