smkml 0.0.9

A unified machine learning toolkit for classification, clustering, distance metrics, and model analysis—optimized for both supervised and unsupervised tasks.

SMKML - Hybrid Machine Learning Algorithm

🚀 SMK: Supervised + Unsupervised Machine Learning Toolkit SMK (smkml) is a unified Python library for seamlessly performing classification and clustering, with built-in support for SVM, KMeans, custom distance metrics, visualization, and limitations analysis. Ideal for learners, researchers, and rapid prototyping.

Features

-> ✅ SVM Classification with optional Grid Search (hyperparameter tuning)

-> 🔄 KMeans Clustering with PCA visualization

-> 📐 Built-in Distance Metrics:

=> Euclidean

=> Manhattan

=> Minkowski (configurable p)

-> 📊 Visualization of clusters (2D PCA)

-> ⚠️ Insights: Understand limitations of SVM and k-NN

-> 💡 Unified API: Same interface for supervised & unsupervised learning

Installation

pip install smkml

"Quick Start"

from smkml import SMK
from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

# Create synthetic classification data
X, y = make_classification(n_samples=300, n_features=10, n_informative=8, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)

# Initialize SMK with Grid Search enabled
model = SMK(enable_grid_search=True)

# Train model
model.fit(X_train, y_train)

# Evaluate
acc = model.score(X_test, y_test)
print("✅ Accuracy:", acc)

# Predict
preds = model.predict(X_test)