[ML] Linear Regression (선형 회귀) 예제

dataset

Housing.csv

0.03MB

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

df=pd.read_csv('/content/Housing.csv')

X = df[["price"]]
y = df["lotsize"]

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=10, shuffle=True)
model = LinearRegression()
model.fit(X_train, y_train)

plt.scatter(X_test, y_test)
plt.plot(X_test, model.predict(X_test), color="red")
plt.show()

print(model.score(X_test, y_test))

print(model.score(X_train, y_train))

metrics.mean_absolute_error(X_test, model.predict(X_test))

https://github.com/erica00j/machinelearning/blob/main/housing_linear_regression.ipynb

GitHub - erica00j/machinelearning

---

import numpy as np
from sklearn.linear_model import LinearRegression

X=np.array([[1,1],[1,2],[2,2],[2,3]])

y=np.dot(X,np.array([1,2]))+3
reg = LinearRegression().fit(X,y)
reg.score(X,y)
reg.coef_
reg.intercept_
reg.predict(np.array([[3,5]]))

https://github.com/erica00j/machinelearning/blob/main/final_linear_regression.ipynb

GitHub - erica00j/machinelearning