Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+metrics:
+- accuracy
+- f1
+- r_squared
+- mse
+tags:
+- knn
+- nearest-neighbors
+- tabular
+- classification
+- regression
+- cpu
+- low-latency
+- ann
+- distance-weighted
+- production-ready
+---
+---
+language:
+- en
+tags:
+- tabular
+- nearest-neighbors
+- knn
+- classification
+- regression
+- cpu
+- low-latency
+- interpretable
+library_name: smart-knn
+license: mit
+pipeline_tag: tabular-classification
+model_name: SmartKNN v2
+---
+
+#  SmartKNN v2
+
+**SmartKNN v2** is a high-performance, CPU-first nearest-neighbors model designed for **low-latency production inference** on real-world tabular data.
+
+It delivers **competitive accuracy with gradient-boosted models** while maintaining **sub-millisecond single-prediction latency (p95)** on CPU-only systems.
+
+SmartKNN v2 is part of the **SmartEco** ecosystem.
+
+---
+
+## Model Details
+
+- **Model type:** Distance-weighted K-Nearest Neighbors  
+- **Tasks:** Classification, Regression  
+- **Backend:** Adaptive (Brute-force + ANN)  
+- **Hardware:** CPU-only (GPU not required)  
+- **Focus:** Low latency, interpretability, production readiness  
+
+Unlike classical KNN, SmartKNN v2 learns feature importance, adapts execution strategy based on data size, and uses optimized distance kernels for fast inference.
+
+---
+
+## What’s New in v2
+
+- Full classification support restored
+- ANN backend introduced for scalable neighbor search
+- Automatic backend selection (small → brute, large → ANN)
+- Distance-weighted voting for improved accuracy
+- Interpretable neighbor influence statistics
+- Foundation for adaptive-K strategies
+
+---
+
+## Architecture Overview
+
+- Feature Weighting
+- Backend Selector
+- Brute Backend (small datasets)
+- ANN Backend (large datasets)
+- Distance Kernel
+- Weighted Voting
+- Prediction
+
+
+This hybrid architecture ensures consistent low latency across dataset sizes.
+
+---
+
+## Performance (Internal Evaluation)
+
+> Public benchmarks will be released soon.
+
+From internal testing on real-world tabular datasets:
+
+- Accuracy comparable to XGBoost / LightGBM / CatBoost
+- Single-prediction latency:
+  - Median: sub-millisecond
+  - p95: consistently low on CPU
+- Predictable batch inference scaling
+
+SmartKNN v2 has **not yet reached its performance ceiling**. Future releases will further optimize speed and accuracy.
+
+---
+
+## Limitations
+
+- Not designed for unstructured data (text, images)
+- ANN backend focuses on CPU efficiency, not GPU acceleration
+- Best suited for tabular datasets
+
+---
+
+## Future Work
+
+- Adaptive-K accuracy optimization
+- Kernel-level speed improvements
+- Custom ANN backend
+
+## Links
+
+- Website: https://thatipamula-jashwanth.github.io/SmartEco/
+- Source Code: https://github.com/thatipamula-jashwanth/smart-knn