notebook

%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns; sns.set()
import numpy as np

y=ax+b a disebut slope, dan b is dikenal sebagai intercept.

x=np.array([1,2,3,4,5,6])
y=np.array([6,4,3,5,4,2])
plt.xlim(0, 10)
plt.ylim(0, 16)
plt.scatter(x, y);


import pandas as pd 


df1 = df = pd.DataFrame({"x": x,"y":y})

print (df1)

   x  y
0  1  6
1  2  4
2  3  3
3  4  5
4  5  4
5  6  2


from sklearn.linear_model import LinearRegression
model = LinearRegression(fit_intercept=True)
import numpy as np

model.fit(x[:, np.newaxis], y)

xfit = np.linspace(0, 10, 1000)
yfit = model.predict(xfit[:, np.newaxis])

plt.scatter(x, y)
plt.plot(xfit, yfit);

png


print("Model slope (a):    ", model.coef_[0])
print("Model intercept (b):", model.intercept_)
print ("Persamaan adalah =",round(model.coef_[0],2),"X","+",round(model.intercept_,2))

Model slope (a):     -0.5142857142857145
Model intercept (b): 5.800000000000001
Persamaan adalah = -0.51 X + 5.8