Regression analysis python practice

Regression analysis practice

0X00 Foreword

Using regression analysis and what factors related to car sales.

0X01 Code

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.rcParams['font.sans-serif'] = ['stxiHei']from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import seaborn as sns
from sklearn.metrics import r2_score, mean_squared_error

data = pd.read_csv(r'C:\Users\jxx\Desktop\13\汽车销售数据.csv',encoding = 'utf-8')
data = data[data['公路里程数'].notna()]#只有1行缺失值，直接舍弃
data = data[['传统汽车销量','国内生产总值当季值(亿元)x1', '汽油价格（元/吨）x2',
       '人民币贷款基准利率%x3', '汽车总产量（万辆）x4', '公路里程数', '汽车整车股票指数', '消费者信心指数']]
data.head()

# 利用pandas分析其相关关系
cormatrix = data.corr() 
cormatrix *= np.tri(*cormatrix.values.shape,k=-1).T
cormatrix
# 绘制相关关系的heatmap
#计算相关系数
corr_all = data.corr() 

#创建分布
mask = np.zeros_like(corr_all,dtype = np.bool) #
mask[np.triu_indices_from(mask)] = True#创建firue,画出heatmap
g,ax = plt.subplots(figsize = (9,7))
sns.heatmap(corr_all,mask = mask,square = True, linewidths = .5,ax = ax ,cmap = 'BuPu')
plt.title('Correlation of Feactures')
plt.show()