Simple Linear Regression for Machine Learning

First, import the standard library

In [36]:

# Importing the libraries import library 
import  numpy  as  np 
import  matplotlib.pyplot  as  plt 
import  pandas  as  pd 
# Enable image adjustment 
% matplotlib notebook 
 #Chinese font display   
plt . rc ( 'font' ,  family = 'SimHei' ,  size = 8 )

2. Import data

In [3]:

dataset = pd.read_csv('Salary_Data.csv')
dataset

Out[3]:

	YearsExperience	Salary
0	1.1	39343.0
1	1.3	46205.0
2	1.5	37731.0
3	2.0	43525.0
4	2.2	39891.0
5	2.9	56642.0
6	3.0	60150.0
7	3.2	54445.0
8	3.2	64445.0
9	3.7	57189.0
10	3.9	63218.0
11	4.0	55794.0
12	4.0	56957.0
13	4.1	57081.0
14	4.5	61111.0
15	4.9	67938.0
16	5.1	66029.0
17	5.3	83088.0
18	5.9	81363.0
19	6.0	93940.0
20	6.8	91738.0
21	7.1	98273.0
22	7.9	101302.0
23	8.2	113812.0
24	8.7	109431.0
25	9.0	105582.0
26	9.5	116969.0
27	9.6	112635.0
28	10.3	122391.0
29	10.5	121872.0

In [5]:

X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, 1].values
X

Out[5]:

array([[  1.1],
       [  1.3],
       [  1.5],
       [  2. ],
       [  2.2],
       [  2.9],
       [  3. ],
       [  3.2],
       [  3.2],
       [  3.7],
       [  3.9],
       [  4. ],
       [  4. ],
       [  4.1],
       [  4.5],
       [  4.9],
       [  5.1],
       [  5.3],
       [  5.9],
       [  6. ],
       [  6.8],
       [  7.1],
       [  7.9],
       [  8.2],
       [  8.7],
       [  9. ],
       [  9.5],
       [  9.6],
       [ 10.3],
       [ 10.5]])

In [6]:

y 

Out[6]:

array([  39343.,   46205.,   37731.,   43525.,   39891.,   56642.,
         60150.,   54445.,   64445.,   57189.,   63218.,   55794.,
         56957.,   57081.,   61111.,   67938.,   66029.,   83088.,
         81363.,   93940.,   91738.,   98273.,  101302.,  113812.,
        109431.,  105582.,  116969.,  112635.,  122391.,  121872.])

三、区分训练集和测试集

In [28]:

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)

四、用简单线性回归训练

In [29]:

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

Out[29]:

LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)

In [30]:

y_pred = regressor.predict(X_test)

五、画图比较

In [31]:

# Visualising the Training set results
plt.scatter(X_train, y_train, color = 'red') # 训练的点
plt.plot(X_train, regressor.predict(X_train), color = 'blue') # 训练和训练的结果所画的线
plt.title(u'薪水和工作经验（训练集）')
plt.xlabel(u'经验')
plt.ylabel(u'薪水')
plt.show()

In [32]:

plt.scatter(X_test, y_test, color = 'red')# 测试的点
plt.plot(X_train, regressor.predict(X_train), color = 'blue') # 训练和训练的结果所画的线
plt.title(u'薪水和工作经验（测试集）')
plt.xlabel(u'经验')
plt.ylabel(u'薪水')
plt.show()

六、项目地址

https://coding.net/u/RuoYun/p/Python-of-machine-learning/git/tree/master/00%E6%9C%BA%E5%99%A8%E5%AD%A6%E4%B9%A0/2.%E5%9B%9E%E5%BD%92%E7%AE%97%E6%B3%95/1.%E7%AE%80%E5%8D%95%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92?public=true