Polynomial fitting in SciPy - Mini Project: Build & Apply

Polynomial fitting

📖 Scenario: You have collected some data points from a small experiment measuring temperature over time. You want to find a smooth curve that fits these points well. This will help you understand the trend and make predictions.

🎯 Goal: Build a polynomial model that fits the given data points using numpy. You will create the data, set the polynomial degree, fit the polynomial, and then print the polynomial coefficients.

📋 What You'll Learn

Create a dictionary called data_points with exact keys time and temperature and their respective lists of values

Create a variable called degree and set it to the exact integer 2

Use numpy to fit a polynomial of degree degree to the data points

Store the polynomial coefficients in a variable called coefficients

Print the coefficients variable

💡 Why This Matters

🌍 Real World

Polynomial fitting is used in science and engineering to find smooth curves that describe data trends, such as temperature changes, stock prices, or sensor readings.

💼 Career

Data scientists and analysts use polynomial fitting to model relationships in data, make predictions, and communicate insights clearly.

1

DATA SETUP: Create the data points dictionary

Create a dictionary called data_points with two keys: 'time' and 'temperature'. Set data_points['time'] to the list [0, 1, 2, 3, 4] and data_points['temperature'] to the list [1, 3, 7, 13, 21].

SciPy

# Create the data_points dictionary with time and temperature lists
# Your code here

Need a hint?

Use a dictionary with keys 'time' and 'temperature'. Each key should have a list of numbers as its value.

2

CONFIGURATION: Set the polynomial degree

Create a variable called degree and set it to the integer 2.

SciPy

data_points = {'time': [0, 1, 2, 3, 4], 'temperature': [1, 3, 7, 13, 21]}
# Set the polynomial degree to 2
# Your code here

Need a hint?

Just assign the number 2 to a variable named degree.

3

CORE LOGIC: Fit the polynomial to the data

Import numpy as np and polyfit from numpy.polynomial.polynomial. Use polyfit with data_points['time'], data_points['temperature'], and degree to fit the polynomial. Store the result in a variable called coefficients.

SciPy

data_points = {'time': [0, 1, 2, 3, 4], 'temperature': [1, 3, 7, 13, 21]}
degree = 2
# Import numpy and polyfit, then fit the polynomial
# Your code here

Need a hint?

Use polyfit from numpy.polynomial.polynomial to fit the polynomial. The order of arguments is x values, y values, then degree.

4

OUTPUT: Print the polynomial coefficients

Print the variable coefficients to display the polynomial coefficients.

SciPy

import numpy as np
from numpy.polynomial.polynomial import polyfit

data_points = {'time': [0, 1, 2, 3, 4], 'temperature': [1, 3, 7, 13, 21]}
degree = 2
coefficients = polyfit(data_points['time'], data_points['temperature'], degree)
# Print the coefficients
# Your code here

Need a hint?

Use print(coefficients) to show the polynomial coefficients.