Linear Regression in Python: A Step-by-Step Overview

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Linear Regression is one of the foundational algorithms in Data Science and Machine Learning. It models the scalar relationship between a dependent variable and one or more explanatory variables.

Key Takeaways:

Cost Function: Measures how far off the linear model's predictions are from actual data.
Gradient Descent: Optimization algorithm used to minimize the cost function.
Python Implementation: Using Scikit-Learn and NumPy to build predictive models.

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Check out the complete guide with source code and detailed explanations on AheadMint.