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In [6]:

    
from sklearn.datasets import load_diabetes
import numpy as np
import pandas as pd



In [7]:

    
diabetes = load_diabetes()
dfX_diabetes = pd.DataFrame(diabetes.data, columns=["X%d" % (i+1) for i in range(np.shape(diabetes.data)[1])])
dfy_diabetes = pd.DataFrame(diabetes.target, columns=["target"])
df_diabetes0 = pd.concat([dfX_diabetes, dfy_diabetes], axis=1)
df_diabetes0.tail()



In [9]:

    
from sklearn.linear_model import LinearRegression
model_diabetes = LinearRegression().fit(diabetes.data, diabetes.target)
print(model_diabetes.coef_)









    



[ -10.01219782 -239.81908937  519.83978679  324.39042769 -792.18416163
  476.74583782  101.04457032  177.06417623  751.27932109   67.62538639]



In [13]:

    
import matplotlib.pyplot as plt
predictions = model_diabetes.predict(diabetes.data)
plt.scatter(diabetes.target, predictions)
plt.show()



In [6]:

    
def preprocess(phonenumber):
    phonenumber_process_dict = {
        "공": 0,
        "영": 0,
        "일": 1,
        "이": 2,
        "삼": 3,
        "사": 4,
        "오": 5,
        "육": 6,
        "칠": 7,
        "팔": 8,
        "구": 9,
        "-" : "",
        " " : "",
    }
    for key, value in phonenumber_process_dict.items():
        phonenumber = phonenumber.replace(key, str(value))
        
    return phonenumber
                                          
preprocess("공일공2220-57삼육")









    Out[6]:





'01022205736'



In [35]:

    
class Student():
    
    def __init__(self, name, age):
        self.name = name
        self.age = age
        print("student {name}({age}) is born".format(name = self.name, age = self.age))
    def introduce():
        print("hi , i am {name} i am {age} years old.".format(name = self.name,age = self.age,))



In [25]:

    
a = Student()
a.name = "jy"
a.age = 26



In [38]:

    
a = Student("jy", 26)









    



student jy(26) is born



In [41]:

    
a.introduce









    Out[41]:





<bound method Student.introduce of <__main__.Student instance at 0x035B95F8>>

	X1	X2	X3	X4	X5	X6	X7	X8	X9	X10	target
437	0.041708	0.050680	0.019662	0.059744	-0.005697	-0.002566	-0.028674	-0.002592	0.031193	0.007207	178.0
438	-0.005515	0.050680	-0.015906	-0.067642	0.049341	0.079165	-0.028674	0.034309	-0.018118	0.044485	104.0
439	0.041708	0.050680	-0.015906	0.017282	-0.037344	-0.013840	-0.024993	-0.011080	-0.046879	0.015491	132.0
440	-0.045472	-0.044642	0.039062	0.001215	0.016318	0.015283	-0.028674	0.026560	0.044528	-0.025930	220.0
441	-0.045472	-0.044642	-0.073030	-0.081414	0.083740	0.027809	0.173816	-0.039493	-0.004220	0.003064	57.0