Logistic Regression From Scratch in Python | No Sklearn

Want to understand what’s actually happening inside Logistic Regression instead of simply using `sklearn`? In this video, we build a simple Logistic Regression model from scratch using Python, NumPy, Pandas, and Matplotlib — without using the ready-made Logistic Regression class from Scikit-learn. We’ll build the model step by step and understand what each part is doing: 📌 What Logistic Regression does 📌 Preparing a simple dataset 📌 Standardizing the input data 📌 Creating weights and bias 📌 Building the sigmoid function 📌 Calculating predictions 📌 Binary Cross-Entropy / Log Loss 📌 Gradient Descent and parameter updates 📌 Making predictions using a 0.5 decision threshold 📌 Visualizing the sigmoid curve and decision boundary The goal isn't to create a production-ready machine learning library. Instead, we're going *under the hood* to understand the core ideas that a Logistic Regression implementation needs. If you're learning Machine Learning and often find ML code difficult to understand, I hope this walkthrough helps make the process a little less intimidating. If you spot something I could explain or visualize better, let me know in the comments — I'm learning too! 🎥 Previous video: Logistic Regression Explained Simply    • Logistic Regression Explained Simply |  Si...   If this video helped you, consider liking the video and subscribing for more beginner-friendly Machine Learning and AI content. #MachineLearning #Python #LogisticRegression