Linear regression with np.polyfit() in NumPy - Mini Project: Build & Apply

Linear regression with np.polyfit()

📖 Scenario: You are a data analyst at a small store. You want to understand how the number of hours spent advertising affects the number of sales. You have collected data for 7 days.

🎯 Goal: Build a simple linear regression model using np.polyfit() to find the relationship between advertising hours and sales. Then, display the slope and intercept of the line.

📋 What You'll Learn

Create two lists: hours and sales with given values

Create a variable degree to set the polynomial degree

Use np.polyfit() with hours, sales, and degree to find the slope and intercept

Print the slope and intercept values

💡 Why This Matters

🌍 Real World

Linear regression helps businesses understand relationships between variables, like advertising time and sales, to make better decisions.

💼 Career

Data analysts and scientists use linear regression to build predictive models and explain trends in data.

1

Create the data lists

Create a list called hours with these exact values: [1, 2, 3, 4, 5, 6, 7]. Create another list called sales with these exact values: [3, 4, 2, 5, 6, 7, 8].

NumPy

# Create the lists hours and sales with the given values
# Your code here

Need a hint?

Use square brackets to create lists. Separate numbers with commas.

2

Set the polynomial degree

Create a variable called degree and set it to 1 to indicate a linear fit.

NumPy

hours = [1, 2, 3, 4, 5, 6, 7]
sales = [3, 4, 2, 5, 6, 7, 8]
# Set degree to 1 for linear regression
# Your code here

Need a hint?

Set degree to 1 because linear regression fits a line.

3

Apply np.polyfit() to find slope and intercept

Import numpy as np. Use np.polyfit() with hours, sales, and degree to create a variable called coefficients. This will hold the slope and intercept.

NumPy

hours = [1, 2, 3, 4, 5, 6, 7]
sales = [3, 4, 2, 5, 6, 7, 8]
degree = 1
# Import numpy as np
# Use np.polyfit() to get coefficients
# Your code here

Need a hint?

Use import numpy as np first. Then call np.polyfit(hours, sales, degree).

4

Print the slope and intercept

Print the slope and intercept from coefficients. The slope is the first element, and the intercept is the second element. Use print() to show them with labels.

NumPy

import numpy as np

hours = [1, 2, 3, 4, 5, 6, 7]
sales = [3, 4, 2, 5, 6, 7, 8]
degree = 1

coefficients = np.polyfit(hours, sales, degree)
# Print slope and intercept with labels
# Your code here

Need a hint?

Use coefficients[0] for slope and coefficients[1] for intercept. Use print() with f-strings.