Python Coding challenge - Day 787| What is the output of the following Python Code?

 

Code Explanation:

Import the required library

from sklearn.linear_model import LinearRegression

Explanation:

You import the LinearRegression class from sklearn.linear_model, which is used to create and train a linear regression model.

Linear regression finds the best-fit line that describes the relationship between input (X) and output (y).

Import NumPy for numerical data

import numpy as np

Explanation:

NumPy is a library for handling arrays and performing mathematical operations efficiently.

We’ll use it to create arrays for our input features and target values.

Create the input (feature) array

X = np.array([[1], [2], [3]])

Explanation:

X is a 2D array (matrix) representing the input feature.

Each inner list ([1], [2], [3]) is one data point (feature value).

So here, we have 3 samples:

X = [[1],

     [2],

     [3]]

Shape of X = (3, 1) → 3 rows (samples), 1 column (feature).

Create the target (output) array

y = np.array([2, 4, 6])

Explanation:

y contains the target values (what we want the model to predict).

Here the relationship is clear: y = 2 * X.

X: 1 → y: 2  

X: 2 → y: 4  

X: 3 → y: 6

Create and train (fit) the Linear Regression model

model = LinearRegression().fit(X, y)

Explanation:

LinearRegression() creates an empty regression model.

.fit(X, y) trains the model using our data.

The model learns the best-fit line that minimizes prediction error.

In this simple case, it learns the formula:

y=2x+0

(slope = 2, intercept = 0)

Make a prediction for a new input

print(model.predict([[4]])[0])

Explanation:

model.predict([[4]]) asks the trained model to predict y when x = 4.

Since the model learned y = 2x,

y=2×4=8

The result of .predict() is an array (e.g., [8.]), so we use [0] to get the first value.

print() displays it.

Output:

8.0

500 Days Python Coding Challenges with Explanation