Notebook

Multivariate Linear Regression Demo¶

Source: 🤖Homemade Machine Learning repository

☝Before moving on with this demo you might want to take a look at:

📗Math behind the Linear Regression

⚙️Linear Regression Source Code

Linear regression is a linear model, e.g. a model that assumes a linear relationship between the input variables (x) and the single output variable (y). More specifically, that output variable (y) can be calculated from a linear combination of the input variables (x).

Multivariate Linear Regression is a linear regression that has more than one input parameter and one output label.

Demo Project: In this demo we will build a model that will predict Happiness.Score for the countries based on Economy.GDP.per.Capita and Freedom parameters.

In [1]:

# To make debugging of linear_regression module easier we enable imported modules autoreloading feature.
# By doing this you may change the code of linear_regression library and all these changes will be available here.
%load_ext autoreload
%autoreload 2

# Add project root folder to module loading paths.
import sys
sys.path.append('../..')

Import Dependencies¶

pandas - library that we will use for loading and displaying the data in a table
numpy - library that we will use for linear algebra operations
matplotlib - library that we will use for plotting the data
plotly - library that we will use for plotting interactive 3D scatters
linear_regression - custom implementation of linear regression

In [2]:

# Import 3rd party dependencies.
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly
import plotly.graph_objs as go

# Configure Plotly to be rendered inline in the notebook.
plotly.offline.init_notebook_mode()

# Import custom linear regression implementation.
from homemade.linear_regression import LinearRegression