# Plotting in Multiple Linear Regression in Python 3

So I'm working on linear regression. So far I've managed to plot in linear regression, but currently I'm on Multiple Linear Regression and I couldn't manage to plot it, I can get some results if I enter the values manually, but I couldn't manage to plot it. Below is my code block and dataset and error, what can i change to plot it?

Dataset:

deneyim maas    yas
0.5 2500    22
0   2250    21
1   2750    23
5   8000    25
8   9000    28
4   6900    23
15  20000   35
7   8500    29
3   6000    22
2   3500    23
12  15000   32
10  13000   30
14  18000   34
6   7500    27


Code block:

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from sklearn.linear_model import LinearRegression

x = dataset.iloc[:,[0,2]].values
y = dataset.maas.values.reshape(-1,1)

multiple_lr = LinearRegression()
multiple_lr.fit(x,y)

b0 = multiple_lr.intercept_
b1 = multiple_lr.coef_
b2 = b1

multiple_lr.predict(np.array([[10,35],[5,35]]))

array = np.array([0,1,2,3,4,5,6,7,8,9,10,11,12,13,14,15]).reshape(-1,1)

plt.scatter(x,y)
plt.show()


It says ValueError: shapes (16,1) and (2,1) not aligned: 1 (dim 1) != 2 (dim 0) when I try to compile it.

• What does this code actually give then? Can you post a screenshot of the result? Or is there a bug? The plotting part seems ok to me, but is hard to test without having that dataset. Aug 13, 2018 at 13:06
• It seems like X has two features (iloc[:, [0, 2]]). But then you try to use the regression on that [0, 1, ..., 15] array that has only one feature. And even after you get the predictions, the visualization will have to be 3D (because of the two Xs plus the Y). Aug 13, 2018 at 13:34
• I've tried a 3D plot using mplot3D for a similar problem. Check this out: medium.com/@anupriyaincbe/… Oct 11, 2018 at 19:21