selectml 0.1.1

A streamlined pipeline for model selection, data preprocessing, training, and evaluation

SelectML

SelectML is an all‑in‑one pipeline for model selection, data preprocessing, training, and evaluation tailored for classification tasks. Designed for data scientists, it streamlines the process of choosing the most suitable model without requiring additional imports.

Features

• Data Preprocessing: Automatic scaling and one‑hot encoding.
• Model Selection: Choose between logistic regression, random forest, or SVM.
• Integrated Pipeline: Automatically split data, train models, and evaluate performance.

Installation

Install via pip (after publishing):

pip install selectml

Quick Start

import pandas as pd
from selectml import ModelSelectionPipeline

# Sample dataset
data = {
    'age': [25, 32, 47, 51, 23, 45, 36, 29],
    'income': [50000, 60000, 80000, 90000, 40000, 75000, 65000, 55000],
    'city': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Phoenix', 'Philadelphia', 'San Antonio', 'San Diego'],
    'purchased': [0, 1, 0, 1, 0, 1, 1, 0]
}
df = pd.DataFrame(data)

numerical_features = ['age', 'income']
categorical_features = ['city']

# Initialize pipeline with desired model type
pipeline = ModelSelectionPipeline(model_type='random_forest')
result = pipeline.run_pipeline(df, target='purchased', 
                               numerical_features=numerical_features, 
                               categorical_features=categorical_features)

print("Accuracy:", result['accuracy'])
print("Classification Report:\n", result['report'])

License

This project is licensed under the MIT License.