CODEWITHSUNDEEP
pythonmatplotlibmplot3d
Sajjad Aemmi
Sajjad Aemmi

Reputation: 2625

How to plot 3D multiple Linear Regression with 2 features using matplotlib?

I need to plot a 3D plot with multiple Linear Regression with 2 features in matplotlib. How can I do that?

this is my code:

import pandas
from sklearn import linear_model

df = pandas.read_csv("cars.csv")

X = df[['Weight', 'Volume']]
y = df['CO2']

regr = linear_model.LinearRegression()

predictedCO2 = regr.predict([scaled[0]])
print(predictedCO2)

Upvotes: 5

Views: 3112

Answers (1)

rednafi
rednafi

Reputation: 1731

So you want to plot a 3d plot of your regression model's outcome. In your 3d plot, for each point you have (x, y, z) = (Weight, Volume, PredictedCO2).

Now you can plot it with:

from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
import numpy as np
import random

# dummy variables for demonstration
x = [random.random()*100 for _ in range(100)]
y = [random.random()*100 for _ in range(100)]
z = [random.random()*100 or _ in range(100)]

# build the figure instance
fig = plt.figure(figsize=(10,8))
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x, y, z, c='blue', marker='o')

# set your labels
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
ax.set_zlabel('Z Label')

plt.show()

This will give you a plot like this:

enter image description here

Upvotes: 3

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