Training and running a linear model using Scikit-Learn | by Dr. Gurinderjeet Kaur (Phd, CSE) | Jun, 2024

Let’s break down the code step-by-step.

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

• matplotlib.pyplot is used for plotting graphs.
• numpy is a library for numerical computations.
• pandas is used for knowledge manipulation and evaluation.
• sklearn.linear_model accommodates machine studying algorithms, together with LinearRegression.

data_root = "https://github.com/ageron/knowledge/uncooked/primary/"
lifesat = pd.read_csv(data_root + "lifesat/lifesat.csv")
X = lifesat[["GDP per capita (USD)"]].values
y = lifesat[["Life satisfaction"]].values

• data_root holds the bottom URL for the dataset.
• lifesat = pd.read_csv(data_root + "lifesat/lifesat.csv") reads the CSV file from the URL right into a pandas DataFrame.
• X = lifesat[["GDP per capita (USD)"]].values extracts the GDP per capita column and converts it right into a numpy array.
• y = lifesat[["Life satisfaction"]].values extracts the Life satisfaction column and converts it right into a numpy array.

lifesat.plot(type='scatter', grid=True, x="GDP per capita (USD)", y="Life satisfaction")
plt.axis([23_500, 62_500, 4, 9])
plt.present()

• lifesat.plot(type='scatter', grid=True, x="GDP per capita (USD)", y="Life satisfaction") creates a scatter plot with GDP per capita on the x-axis and Life satisfaction on the y-axis. The grid=True argument provides a grid to the plot.
• plt.axis([23_500, 62_500, 4, 9]) units the x-axis limits from 23,500 to 62,500 and the y-axis limits from 4 to 9.
• plt.present() shows the plot.

mannequin = LinearRegression()

• mannequin = LinearRegression() initializes a linear regression mannequin.

mannequin.match(X, y)

• mannequin.match(X, y) trains the linear regression mannequin utilizing the GDP per capita (X) because the enter and Life satisfaction (y) because the output.

X_new = [[37_655.2]] # Cyprus' GDP per capita in 2020
print(mannequin.predict(X_new)) # output: [[6.30165767]]

• X_new = [[37_655.2]] creates a brand new knowledge level representing Cyprus’ GDP per capita in 2020.
• print(mannequin.predict(X_new)) predicts the Life satisfaction for Cyprus utilizing the skilled mannequin and prints the end result. The output [[6.30165767]] signifies the expected Life satisfaction rating for Cyprus.

Every step corresponds to a key facet of the method: knowledge loading, knowledge visualization, mannequin choice, mannequin coaching, and making predictions.

Reference:

E-book — Arms-On Machine Studying with Scikit-Be taught, Keras & TensorFlow Ideas, Instruments, and Methods to Construct Clever Methods — By Aurélien Géron