Software Automation
Scikit - Polynomial Regression

Introduction

Sometimes, data is not perfectly straight — it's curved or follows a pattern that bends. Linear regression can't capture this curve well.

Polynomial regression extends linear regression by adding powers of the input features so that it can fit curves instead of just straight lines.

Part 1: Setup and Imports

Step 1: Install scikit-learn

Run this only once in your environment. 

pip install scikit-learn

Step 2: Import Libraries

import numpy as np

import matplotlib.pyplot as plt

from sklearn.linear_model import LinearRegression

from sklearn.preprocessing import PolynomialFeatures

Part 4: Apply Polynomial Regression

Let’s now apply polynomial regression with a degree of 2 (a parabola).

Part 6 - Lab Exercises

1. Try Different Values of k

Change n_neighbors to 1, 3, 5, etc.

• How does the prediction change?

• Which value of k seems most stable?

2.  Add New Training Points

Manually add more points to X and y. Try to:

2. • Add more class 0 and class 1 points in different areas

   • Make the classes overlap and observe what KNN does
     
     

3. Use Real Categories

Create a KNN classifier for:

3. • “Is this fruit citrus?”

     1. Features: has thick skin, tastes sour, round shape (1 or 0)

     2. Labels: 1 = citrus, 0 = not citrus

   • Invent 8 fruits with these properties and classify a new one