Python Coding challenge - Day 675| What is the output of the following Python Code?

 

Code Explanation:

1. Import Required Libraries

from sklearn.linear_model import LinearRegression

import numpy as np

LinearRegression → scikit-learn class for fitting linear models.

numpy → used to create numerical arrays.

2. Define Input Features

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

Creates a 2D array of shape (3,1).

This represents our input values (independent variable).

Value of X:

array([[1],

       [2],

       [3]])

3. Define Target Values

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

Creates a 1D array of outputs (dependent variable).

This is the value we want the model to predict.

Value of y:

array([2, 4, 6])

4. Train the Linear Regression Model

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

Fits a straight line through the data using ordinary least squares (OLS).

The model learns two things:

Coefficient (slope): [2.]

Intercept: 0.0

So the learned equation is:

𝑦=2𝑥+0

5. Make Prediction

print(round(model.predict([[4]])[0], 2))

model.predict([[4]]) → asks model: “What is y when x = 4?”

Expected mathematically:

y=2⋅4=8

scikit-learn may give something like 7.999999999999998 (floating-point issue).

The round(..., 2) fixes it to 2 decimal places.

Final Output

8.0

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