🌟 Universal ML Framework
A complete, automated machine learning pipeline framework that works with any dataset.
Build, compare, and deploy ML models with minimal code.
🚀 Key Features
🤖 Automated Pipeline - Complete ML workflow from data to deployment
🔍 Auto Feature Detection - Automatically identifies numeric, categorical, and binary features
📊 Model Comparison - Tests multiple algorithms with cross-validation
⚙️ Hyperparameter Tuning - Automatic parameter optimization
🎯 Multi-Problem Support - Classification and regression tasks
📦 Production Ready - Model persistence and metadata tracking
📦 Quick Install
pip install universal-ml-framework
🎯 Quick Start
from universal_ml_framework import UniversalMLPipeline
# Create and run pipeline
pipeline = UniversalMLPipeline(problem_type='classification')
pipeline.run_pipeline(
train_path='data.csv',
target_column='target',
test_path='test.csv'
)
print(f"Best model: {pipeline.best_model_name}")
print(f"Best score: {pipeline.best_score:.4f}")
📚 Documentation
🔧 Supported Algorithms
Classification:
Random Forest Classifier
Logistic Regression
Support Vector Machine
Regression:
Random Forest Regressor
Linear Regression
Support Vector Regression
📈 What It Does
Data Loading - Reads CSV files automatically
Feature Detection - Identifies feature types (numeric/categorical/binary)
Preprocessing - Handles missing values, encoding, scaling
Model Training - Trains multiple algorithms with cross-validation
Hyperparameter Tuning - Optimizes best performing model
Prediction - Generates predictions on test data
Model Saving - Persists trained model and metadata
🎯 Use Cases
Business Analytics - Customer churn, sales forecasting
Finance - Credit risk, fraud detection
Healthcare - Medical diagnosis, treatment prediction
Marketing - Campaign response, customer segmentation
Real Estate - Price prediction, market analysis
HR - Employee performance, retention prediction