Python数据分析及可视化实例之房屋制热、制冷功率预测

发布时间：2021-12-04 付费文章：2.0元

Base

基础知识

Civil

土木分类资料

Python

Python编程学习

Tools

自媒体效率工具

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加载数据

import os
import pandas as pd
df = pd.read_csv("CYKEZR.csv",encoding='gb18030' )
df.head()

relative compactness surface area wall area roof area overall height orientation glazing area glazing area distribution heating load cooling load 0 0.98 514.5 294.0 110.25 7.0 2 0.0 0 15.55 21.33 1 0.98 514.5 294.0 110.25 7.0 3 0.0 0 15.55 21.33 2 0.98 514.5 294.0 110.25 7.0 4 0.0 0 15.55 21.33 3 0.98 514.5 294.0 110.25 7.0 5 0.0 0 15.55 21.33 4 0.90 563.5 318.5 122.50 7.0 2 0.0 0 20.84 28.28

是否有空值

df.isnull().any()
relative compactness         False
surface area                 False
wall area                    False
roof area                    False
overall height               False
orientation                  False
glazing area                 False
glazing area distribution    False
heating load                 False
cooling load                 False
dtype: bool

数据分割

X = df.iloc[:,:-2]
y_heating = df['heating load']
y_cooling = df['cooling load']
# 解决中文显示问题
# from yellowbrick.style.rcmod import set_aesthetic
# set_aesthetic(font = 'SimHei')
import matplotlib
matplotlib.rcParams['font.sans-serif']=['SimHei']
matplotlib.rcParams['axes.unicode_minus']=False  # 用来正常显示负号

回归分析

制热

from sklearn.model_selection import train_test_split
from sklearn.linear_model import Lasso
from yellowbrick.regressor import PredictionError
X_train, X_test, y_train, y_test = train_test_split(X, y_heating, test_size=0.2, random_state=42)
# 可视化及验证
model = Lasso()
visualizer = PredictionError(model) # line_color='r'
visualizer.fit(X_train, y_train)
print(visualizer.score(X_test, y_test))
visualizer.poof()
0.7940649566807682

 

制冷

X_train, X_test, y_train, y_test = train_test_split(X, y_cooling, test_size=0.2, random_state=42)
# 可视化及验证
model = Lasso()
visualizer = PredictionError(model) # line_color='r'
visualizer.fit(X_train, y_train)
print(visualizer.score(X_test, y_test))
visualizer.poof()
0.7722075069396912

 

学习曲线

制热

from yellowbrick.model_selection import LearningCurve
model = Ridge()
visualizer = LearningCurve(model, cv=5)
visualizer.fit(X, y_heating)
visualizer.poof()

 

制冷

model = Ridge()
visualizer = LearningCurve(model, cv=5)
visualizer.fit(X, y_cooling)
visualizer.poof()

 

残差图

制热

from sklearn.model_selection import train_test_split
from sklearn.linear_model import Ridge
from yellowbrick.regressor import ResidualsPlot
# 创建训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y_heating, test_size=0.2, random_state=42)
# 可视化及验证
model = Ridge()
visualizer = ResidualsPlot(model)
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.poof()

 

制冷

# 创建训练集和测试集
X_train, X_test, y_train, y_test = train_test_split(X, y_cooling, test_size=0.2, random_state=42)
# 可视化及验证
model = Ridge()
visualizer = ResidualsPlot(model)
visualizer.fit(X_train, y_train)
visualizer.score(X_test, y_test)
visualizer.poof()

 

 

 


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