Simple Linear Regression: Sklearn vs. From Scratch
This repository contains two implementations of Simple Linear Regression:
Using Scikit-Learn – A straightforward implementation leveraging the LinearRegression class from sklearn.
From Scratch – A custom-built implementation of linear regression using NumPy, demonstrating how the model works under the hood.
Contents:
linear_regression_sklearn.ipynb → Implements simple linear regression using sklearn.
linear_regression_scratch.ipynb → Implements the same model manually using mathematical formulas.
placement.csv (optional) → Sample dataset used for training and testing.
Key Concepts Covered:
✅ Training a Linear Regression Model
✅ Understanding Model Parameters (Slope & Intercept)
✅ Comparing Predictions from Sklearn and Scratch Implementations