AI ENGINEERING
Linear Regression from Scratch
Linear Regression from Scratch is a machine learning project focused on implementing one of the most fundamental supervised learning algorithms without relying on machine learning libraries such as Scikit-learn or TensorFlow.
YEAR
2026
ROLE
Designed • Developed • Shipped
STACK
01/PROBLEM → SOLUTION
PROBLEM
Choosing a learning rate: too small converges slowly, too large diverges; convergence must stop at the right moment without terminating early.
SOLUTION
Feature normalization stabilizes gradient descent; a cost function drives parameter updates until convergence, then theta is persisted for prediction.
02/SYSTEM ARCHITECTURE
How the system fits together
- Raw Dataset
- →Data Validation
- →Feature Normalizer
- →Gradient Descent Optimizer
- →Model Exporter
- →Prediction Script
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03/KEY ENGINEERING DECISIONS
Zero ML Libraries
Normalization, gradient descent, and inference are hand-written in NumPy no scikit-learn or TensorFlow.
Feature Normalization
Scaling inputs prevents wide-range features from dominating the optimization.
Parameter Persistence
Training and prediction are separated; trained theta is saved to disk and reused without retraining.
04/IMPACT & TAKEAWAYS
Standalone Pipeline. CSV to validate to normalize to train to export to predict, all from scratch.
Reusable Model. Train once, persist, and predict later without re-running optimization.
Convergence Control. Stops when extra iterations yield minimal improvement.