Multiple Linear Regression in C — Part 3 Final Part

The finale of our from-scratch Machine Learning in C series — no libraries, no shortcuts, just plain C and math explained from zero. In Part 1, we covered regression theory, R² & RMSE, reading the dataset, matrix multiplication, and transpose. In Part 2, we implemented the determinant using recursion. In this final part, we finish the code, compile it, and see it actually predict something. In Part 3 (Final): 🔄 Matrix inverse — using the determinant and cofactors to invert a matrix 🧩 Putting it all together in main() — the Normal Equation, start to finish ⚙️ Compiling with gcc (and why this code needs MinGW, not MSVC) ▶️ Running the program and reading the output line by line 📊 Interpreting the coefficients, R², and RMSE in plain language 🎯 Making a real prediction — and checking it by hand By the end of this video, you'll have a complete, working Multiple Linear Regression model — built entirely by hand in C, with nothing hidden inside a library call. Full series: ▶️ Part 1 — Theory, R² & RMSE, reading data, matrix multiplication & transpose ▶️ Part 2 — Determinant and recursion ▶️ Part 3 — Matrix inverse, compiling, running, and interpreting results (this video) Tools used: VS Code, gcc (MinGW / C99) 👍 If this series helped you understand ML at a deeper level, hit like and subscribe — more from-scratch tutorials coming. 💬 Questions or stuck on your own version? Drop a comment, happy to help. #MachineLearning #CProgramming #LinearRegression #FromScratch #LearnToCode #NormalEquation