Problem 4: Check for Multicollinearity Before Modeling

Problem Description

Write a Python program that computes the correlation between two candidate input features, and prints a warning message if their correlation exceeds 0.9, before proceeding to fit a Multiple Linear Regression model.

Input

A dataset with two candidate features and one target.

Output

A correlation value, a warning if multicollinearity is detected, and the fitted model's coefficients regardless.

Constraints

• None specific.

Example Input

text
feature1=[10,15,20,25], feature2=[12,17,22,27], target=[100,150,200,250]

Example Output

text
Correlation between feature1 and feature2: 0.99
Warning: High multicollinearity detected.
Coefficients: [...]

Concepts Covered

df.corr(), conditional warnings, LinearRegression.