ml-project-vault

An evolving production-grade library mapping classical statistical learning models and advanced deep learning architectures to real-world business optimization, asymmetric fiat risk matrices, and product deployment.

---

The Architecture Strategy

Most machine learning repositories optimize for academic metrics like raw Accuracy or clean F1-scores. This vault is built differently. Every system inside this architecture is engineered to:

1. Solve a Business-Fiat Reality: Convert mathematical classification/regression errors directly into operational overhead and cost liability.
2. Ensure Production Reproducibility: Utilize self-contained data ingestion loops so code runs flawlessly out-of-the-box.
3. Be Ready for Deployment: Serialize and freeze trained mathematical weights into compact artifact files (.joblib, models) for immediate backend API serving.

---

System Roadmap & Index

This vault is systematically categorized into two major developmental arcs: Classical Statistical Learning (Mastery of data geometry, pipelines, and cost matrices) and Deep Learning Systems (Neural networks, vector embeddings, and sequence modeling).

Part 1: Classical Machine Learning Systems

Index	Project Directory	Core Architecture	Key Algorithms	Status
01	01-credit-card-fraud-detection	Asymmetric Business Cost-Matrix Optimization	Logistic Regression, Robust Scaling	Complete
02	02-movielens-recommender-svd	Latent Feature Extraction & Sparse Matrices	Singular Value Decomposition (SVD), KNN	Next Up
03	To Be Determined	Network Intrusion Detection Pipeline	Anomaly Detection / Isolation Forests	Planned
04	To Be Determined	High-Dimensional E-Commerce Customer Segments	K-Means, Principal Component Analysis (PCA)	Planned
05	To Be Determined	B2B SaaS Churn Prediction Engine	Random Forests, Gradient Boosting (XGBoost)	Planned
06	To Be Determined	Algorithmic Supply Chain Demand Forecasting	Ridge/Lasso Regularization, Time Series	Planned
07	To Be Determined	Automated Healthcare Risk Stratification	Support Vector Machines (SVM), Naive Bayes	Planned
08	To Be Determined	Predictive Maintenance Asset Scheduler	Survival Analysis, Decision Trees	Planned
09	To Be Determined	Real Estate Valuation Engine	Advanced Ensemble Stack, StackingRegressor	Planned
10	To Be Determined	Text Sentiment Latent Intent Classifier	Naive Bayes, TF-IDF Text Vectorization	Planned

Part 2: Deep Learning & Frontier AI Systems

Index	Target System	Domain Arc	Core Architecture / Tech Stack	Status
11	To Be Decided	Deep Learning	Advanced Neural Networks	Yet To Be Decided
12	To Be Decided	Deep Learning	Sequence Modeling & NLP	Yet To Be Decided
13	To Be Decided	Deep Learning	Vector Embeddings & Semantics	Yet To Be Decided