import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pickle
with open("datasets/perch_length.pickle","rb")as f:
perch_length=pickle.load(f)
with open("datasets/perch_weight.pickle","rb")as d:
perch_weight=pickle.load(d)
len(perch_weight)==len(perch_length),len(perch_weight) #두 파일의 길이 비교
from sklearn.model_selection import train_test_split
train_input, test_input, train_target, test_target=\
train_test_split(perch_input,perch_weight,random_state=42)
from sklearn.linear_model import LinearRegression
lr=LinearRegression()
lr.fit(train_input,train_target)
lr.score(train_input,train_target)
import pandas as pd import numpy as np import matplotlib.pyplot as plt import pickle with open("datasets/perch_length.pickle","rb")as f: perch_length=pickle.load(f)
with open("datasets/perch_weight.pickle","rb")as d: perch_weight=pickle.load(d)
len(perch_weight)==len(perch_length),len(perch_weight) #두 파일의 길이 비교
from sklearn.model_selection import train_test_split train_input, test_input, train_target, test_target=\ train_test_split(perch_input,perch_weight,random_state=42)
from sklearn.linear_model import LinearRegression lr=LinearRegression() lr.fit(train_input,train_target) lr.score(train_input,train_target)
print(perch_weight.mean()) perch_length_mean=perch_length.mean() print(perch_length_mean)
lr.predict(perch_length_mean.reshape(1,1))
print(lr.coef,lr.intercept)
plt.scatter(train_input, traintarget) plt.scatter(27.892857142857142,379.28100564,marker="^") plt.scatter(27.892857142857142,382.23928571428576,marker="o") plt.plot([1,50],[1*lr.coef+lr.intercept,50*lr.coef+lr.intercept_]) #직선