github位址 github.com/yangjinghit…
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
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/anaconda3/envs/py35/lib/python3.5/importlib/_bootstrap.py:222: runtimewarning: numpy.dtype size changed, may indicate binary incompatibility. expected 96, got 88
return f(*args, **kwds)
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from sklearn.linear_model import linearregression
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
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plt.style.use('ggplot')
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data = pd.read_csv('advertising.csv')
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data.head()
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unnamed: 0
tvradio
news*****
sales01
230.1
37.8
69.2
22.112
44.5
39.3
45.1
10.423
17.2
45.9
69.3
9.33
4151.5
41.3
58.5
18.545
180.8
10.8
58.4
12.9
plt.scatter(data.tv, data.sales)
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複製**plt.scatter(data.radio, data.sales)
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複製**plt.scatter(data.news*****, data.sales)
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複製**x = data[['tv', 'radio', 'news*****']]
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y = data.sales
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x_train, x_test, y_train, y_test = train_test_split(x, y)
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len(x_train), len(y_train)
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(150, 150)
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len(x_test)
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50
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model = linearregression()
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model.fit(x_train, y_train)
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linearregression(copy_x=true, fit_intercept=true, n_jobs=1, normalize=false)
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model.coef_
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array([ 0.04466416, 0.19594144, -0.00469486])
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for i in zip(x_train.columns, model.coef_):
print(i)
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('tv', 0.04466415613441986)
('radio', 0.1959414384329583)
('news*****', -0.0046948632484331895)
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mean_squared_error(model.predict(x_test), y_test)
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3.927556655626268
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