P2.2.3 Supervised ML - Linear Regression | ML Foundations - Python to GenAI

How do platforms like MagicBricks and 99acres automatically estimate house prices? Machine Learning — and in this video you'll build exactly that from scratch. In this session (P2.2.3 — Supervised Learning: Linear Regression), we cover: → Why house price prediction is a perfect Regression problem → How to read your data before picking an algorithm → What Linear Regression actually does — in plain English → Building a real house price predictor in Python → Train-Test Split — why we split data and what it proves → R² Score and RMSE — what they mean in real terms → Overfitting vs Underfitting — how to detect and understand both → Pros and limitations of Linear Regression The 4-phase ML workflow in action: Phase 1 → Split | Phase 2 → Train | Phase 3 → Evaluate | Phase 4 → Inference No math overload. Clean code. Real intuition. 📂 Part of the GenAI Foundation Course 📁 GitHub https://github.com/MaighaInc/pycore/tree/main/Course-GenAIFoundation/P2_LegacyToGenAI/P2.2_ML%20Foundations 💬 Discord https://discord.com/invite/V35dKcApS6 ────────────────────────────── 🔔 Subscribe so you don't miss the next session 👍 Like if house prices finally make sense 💬 Drop your questions below — I read every one ──────────────────────────────