Python实现多元线性回归_多元线性回归数据集python

总目录：Python数据分析整理

本文基本是对文章的整理，修改了一些我这个版本跑不通的地方，多加了一个模型保存部分而已。整理后用于之后使用。

原作者大佬文章地址：Python实现多元线性回归

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数据集

TV	radio	newspaper	sales
230.1	37.8	69.2	22.1
44.5	39.3	45.1	10.4
17.2	45.9	69.3	9.3
151.5	41.3	58.5	18.5
180.8	10.8	58.4	12.9
8.7	48.9	75	7.2
57.5	32.8	23.5	11.8
120.2	19.6	11.6	13.2
8.6	2.1	1	4.8
199.8	2.6	21.2	10.6
66.1	5.8	24.2	8.6
214.7	24	4	17.4
23.8	35.1	65.9	9.2
97.5	7.6	7.2	9.7
204.1	32.9	46	19
195.4	47.7	52.9	22.4
67.8	36.6	114	12.5
281.4	39.6	55.8	24.4
69.2	20.5	18.3	11.3
147.3	23.9	19.1	14.6
218.4	27.7	53.4	18
237.4	5.1	23.5	12.5
13.2	15.9	49.6	5.6
228.3	16.9	26.2	15.5
62.3	12.6	18.3	9.7
262.9	3.5	19.5	12
142.9	29.3	12.6	15
240.1	16.7	22.9	15.9
248.8	27.1	22.9	18.9
70.6	16	40.8	10.5
292.9	28.3	43.2	21.4
112.9	17.4	38.6	11.9
97.2	1.5	30	9.6
265.6	20	0.3	17.4
95.7	1.4	7.4	9.5
290.7	4.1	8.5	12.8
266.9	43.8	5	25.4
74.7	49.4	45.7	14.7
43.1	26.7	35.1	10.1
228	37.7	32	21.5
202.5	22.3	31.6	16.6
177	33.4	38.7	17.1
293.6	27.7	1.8	20.7
206.9	8.4	26.4	12.9
25.1	25.7	43.3	8.5
175.1	22.5	31.5	14.9
89.7	9.9	35.7	10.6
239.9	41.5	18.5	23.2
227.2	15.8	49.9	14.8
66.9	11.7	36.8	9.7
199.8	3.1	34.6	11.4
100.4	9.6	3.6	10.7
216.4	41.7	39.6	22.6
182.6	46.2	58.7	21.2
262.7	28.8	15.9	20.2
198.9	49.4	60	23.7
7.3	28.1	41.4	5.5
136.2	19.2	16.6	13.2
210.8	49.6	37.7	23.8
210.7	29.5	9.3	18.4
53.5	2	21.4	8.1
261.3	42.7	54.7	24.2
239.3	15.5	27.3	15.7
102.7	29.6	8.4	14
131.1	42.8	28.9	18
69	9.3	0.9	9.3
31.5	24.6	2.2	9.5
139.3	14.5	10.2	13.4
237.4	27.5	11	18.9
216.8	43.9	27.2	22.3
199.1	30.6	38.7	18.3
109.8	14.3	31.7	12.4
26.8	33	19.3	8.8
129.4	5.7	31.3	11
213.4	24.6	13.1	17
16.9	43.7	89.4	8.7
27.5	1.6	20.7	6.9
120.5	28.5	14.2	14.2
5.4	29.9	9.4	5.3
116	7.7	23.1	11
76.4	26.7	22.3	11.8
239.8	4.1	36.9	12.3
75.3	20.3	32.5	11.3
68.4	44.5	35.6	13.6
213.5	43	33.8	21.7
193.2	18.4	65.7	15.2
76.3	27.5	16	12
110.7	40.6	63.2	16
88.3	25.5	73.4	12.9
109.8	47.8	51.4	16.7
134.3	4.9	9.3	11.2
28.6	1.5	33	7.3
217.7	33.5	59	19.4
250.9	36.5	72.3	22.2
107.4	14	10.9	11.5
163.3	31.6	52.9	16.9
197.6	3.5	5.9	11.7
184.9	21	22	15.5
289.7	42.3	51.2	25.4
135.2	41.7	45.9	17.2
222.4	4.3	49.8	11.7
296.4	36.3	100.9	23.8
280.2	10.1	21.4	14.8
187.9	17.2	17.9	14.7
238.2	34.3	5.3	20.7
137.9	46.4	59	19.2
25	11	29.7	7.2
90.4	0.3	23.2	8.7
13.1	0.4	25.6	5.3
255.4	26.9	5.5	19.8
225.8	8.2	56.5	13.4
241.7	38	23.2	21.8
175.7	15.4	2.4	14.1
209.6	20.6	10.7	15.9
78.2	46.8	34.5	14.6
75.1	35	52.7	12.6
139.2	14.3	25.6	12.2
76.4	0.8	14.8	9.4
125.7	36.9	79.2	15.9
19.4	16	22.3	6.6
141.3	26.8	46.2	15.5
18.8	21.7	50.4	7
224	2.4	15.6	11.6
123.1	34.6	12.4	15.2
229.5	32.3	74.2	19.7
87.2	11.8	25.9	10.6
7.8	38.9	50.6	6.6
80.2	0	9.2	8.8
220.3	49	3.2	24.7
59.6	12	43.1	9.7
0.7	39.6	8.7	1.6
265.2	2.9	43	12.7
8.4	27.2	2.1	5.7
219.8	33.5	45.1	19.6
36.9	38.6	65.6	10.8
48.3	47	8.5	11.6
25.6	39	9.3	9.5
273.7	28.9	59.7	20.8
43	25.9	20.5	9.6
184.9	43.9	1.7	20.7
73.4	17	12.9	10.9
193.7	35.4	75.6	19.2
220.5	33.2	37.9	20.1
104.6	5.7	34.4	10.4
96.2	14.8	38.9	11.4
140.3	1.9	9	10.3
240.1	7.3	8.7	13.2
243.2	49	44.3	25.4
38	40.3	11.9	10.9
44.7	25.8	20.6	10.1
280.7	13.9	37	16.1
121	8.4	48.7	11.6
197.6	23.3	14.2	16.6
171.3	39.7	37.7	19
187.8	21.1	9.5	15.6
4.1	11.6	5.7	3.2
93.9	43.5	50.5	15.3
149.8	1.3	24.3	10.1
11.7	36.9	45.2	7.3
131.7	18.4	34.6	12.9
172.5	18.1	30.7	14.4
85.7	35.8	49.3	13.3
188.4	18.1	25.6	14.9
163.5	36.8	7.4	18
117.2	14.7	5.4	11.9
234.5	3.4	84.8	11.9
17.9	37.6	21.6	8
206.8	5.2	19.4	12.2
215.4	23.6	57.6	17.1
284.3	10.6	6.4	15
50	11.6	18.4	8.4
164.5	20.9	47.4	14.5
19.6	20.1	17	7.6
168.4	7.1	12.8	11.7
222.4	3.4	13.1	11.5
276.9	48.9	41.8	27
248.4	30.2	20.3	20.2
170.2	7.8	35.2	11.7
276.7	2.3	23.7	11.8
165.6	10	17.6	12.6
156.6	2.6	8.3	10.5
218.5	5.4	27.4	12.2
56.2	5.7	29.7	8.7
287.6	43	71.8	26.2
253.8	21.3	30	17.6
205	45.1	19.6	22.6
139.5	2.1	26.6	10.3
191.1	28.7	18.2	17.3
286	13.9	3.7	15.9
18.7	12.1	23.4	6.7
39.5	41.1	5.8	10.8
75.5	10.8	6	9.9
17.2	4.1	31.6	5.9
166.8	42	3.6	19.6
149.7	35.6	6	17.3
38.2	3.7	13.8	7.6
94.2	4.9	8.1	9.7
177	9.3	6.4	12.8
283.6	42	66.2	25.5
232.1	8.6	8.7	13.4
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可以直接复制下来命名为Advertising.txt

数据分布

import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus']=False

#通过read_csv来读取我们的目的数据集
adv_data = pd.read_csv("Advertising.txt", sep='	')

# #得到我们所需要的数据集且查看其前几列以及数据形状
print(adv_data.head(10))

# 数据描述
print(adv_data.describe())
# 缺失值检验
print(adv_data[adv_data.isnull() == True].count())

adv_data.boxplot()
plt.savefig("箱图.jpg")
plt.show()

adv_data.iloc[:, :3].hist(bins=30,alpha = 0.5)
plt.savefig("直方图.jpg")
plt.show()

adv_data.iloc[:, :3].plot(kind='kde', secondary_y=True)
plt.savefig("密度图.jpg")
plt.show()
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数据分析

import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

plt.rcParams['font.sans-serif'] = ['SimHei']

#通过read_csv来读取我们的目的数据集
adv_data = pd.read_csv("Advertising.txt", sep='	')

# 通过加入一个参数kind='reg'，seaborn可以添加一条最佳拟合直线和95%的置信带。
sns.pairplot(adv_data, x_vars=['TV','radio','newspaper'], y_vars='sales', height=7, aspect=0.8,kind='reg')
plt.savefig("pairplot.jpg")
plt.show()

# 相关系数矩阵 r(相关系数) = x和y的协方差/(x的标准差*y的标准差) == cov（x,y）/σx*σy
# 相关系数0~0.3弱相关0.3~0.6中等程度相关0.6~1强相关
print(adv_data.corr())
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数据拆分

import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

plt.rcParams['font.sans-serif'] = ['SimHei']
plt.rcParams['axes.unicode_minus']=False

#通过read_csv来读取我们的目的数据集
adv_data = pd.read_csv("Advertising.txt", sep='	')
# print(adv_data)

X_train, X_test, Y_train, Y_test = train_test_split(adv_data.iloc[:, 0:3], adv_data.sales, train_size=0.8, random_state=2)

print("X：原始数据特征:", adv_data.iloc[:, 0:3].shape,
      ",训练数据特征:", X_train.shape,
      ",测试数据特征:", X_test.shape)

print("Y：原始数据标签:", adv_data.sales.shape,
      ",训练数据标签:", Y_train.shape,
      ",测试数据标签:", Y_test.shape)

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数据建模

import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

plt.rcParams['font.sans-serif'] = ['SimHei']

#通过read_csv来读取我们的目的数据集
adv_data = pd.read_csv("Advertising.txt", sep='	')
# print(adv_data)

X_train, X_test, Y_train, Y_test = train_test_split(adv_data.iloc[:, :3], adv_data.sales, train_size=0.8, random_state=2)

model = LinearRegression()
model.fit(X_train, Y_train)

a = model.intercept_  # 截距
b = model.coef_  # 回归系数

print("最佳拟合线:截距=", a, ",回归系数=", b)
for i in range(len(X_train.keys())):
    print(str(X_train.keys()[i])+':'+str(b[i]))
print('常数:'+str(a))
print()
score = model.score(X_test, Y_test)
print('模型测试得分：'+str(score))

Y_pred = model.predict(X_test)
# print(Y_pred)

df_empty = pd.DataFrame(Y_test)
df_empty['Y_pred'] = Y_pred
new_df_empty = df_empty.sort_values('sales')
print(df_empty)

plt.plot(range(len(new_df_empty['sales'])), new_df_empty['sales'], 'b', label="test data")
plt.plot(range(len(new_df_empty['Y_pred'])), new_df_empty['Y_pred'], 'r', label="pred data")
# 显示图像
plt.legend(loc=2)
plt.title("测试集预测")
plt.savefig("测试集预测.jpg")
plt.show()

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模型保存

import pandas as pd
import seaborn as sns
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
import pickle

plt.rcParams['font.sans-serif'] = ['SimHei']

#通过read_csv来读取我们的目的数据集
adv_data = pd.read_csv("Advertising.txt", sep='	')
# print(adv_data)

X_train, X_test, Y_train, Y_test = train_test_split(adv_data.iloc[:, :3], adv_data.sales, train_size=0.8, random_state=2)

model = LinearRegression()
model.fit(X_train, Y_train)

with open('dy_model.pickle','wb') as f:
    pickle.dump(model, f)
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模型调用

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from pandas import DataFrame, Series
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LinearRegression
import pandas as pd
import pickle

#显示所有列
pd.set_option('display.max_columns', None)
#显示所有行
pd.set_option('display.max_rows', None)
#设置value的显示长度为100，默认为50
pd.set_option('max_colwidth',100)

plt.rcParams['font.sans-serif'] = ['SimHei']

#通过read_csv来读取我们的目的数据集
adv_data = pd.read_csv("Advertising.txt", sep='	')

# 读取模型
pickle_out = open('dy_model.pickle','rb')
model = pickle.load(pickle_out)

adv_data['pred'] = model.predict(adv_data.iloc[:, :3])
new_adv_data = adv_data.sort_values('sales')
print(new_adv_data)

x_test = new_adv_data.iloc[:, :3]
y_test = new_adv_data['sales']
y_pred = new_adv_data['pred']

plt.plot(range(len(x_test)), y_test, 'b', label="test_y")
plt.plot(range(len(y_pred)), y_pred, 'r', label="pred_y")

plt.legend(loc=2)
plt.title("模型调用")
plt.savefig("模型调用.jpg")
plt.show()

score = model.score(x_test, y_test)
print(score)
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