Logistic Regression from Scratch using Gradient Descent

Implemented Logistic Regression from scratch using Gradient Descent and compared it with sklearn's LogisticRegression. 🚀

📌 Key Features:

✔ Sigmoid Activation for probability estimation
✔ Gradient Descent Optimization for parameter updates
✔ Decision Boundary Visualization comparing manual vs sklearn implementation
✔ NumPy-based implementation with step-by-step optimization

📊 Results:

Both models successfully converge, and their decision boundaries closely match! 🎯

📌 Check out the full notebook for implementation details!