SmplML is a user-friendly Python library for streamlined machine learning classification. It offers intuitive modules for data preprocessing, feature engineering, model training, and evaluation. Ideal for beginners and experts alike, SmplML simplifies classification tasks, enabling you to gain valuable insights from your data with ease.
License: MIT License
Python 29.63%Jupyter Notebook 70.37%
smplml's Introduction
SmplML / SimpleML: Simplified Machine Learning for Classification and Regression
SmplML is a user-friendly Python module for streamlined machine learning classification and regression. It offers intuitive functionality for data preprocessing, model training, and evaluation. Ideal for beginners and experts alike, SmplML simplifies ML tasks, enabling you to gain valuable insights from your data with ease.
Features
Data preprocessing: Easily handle encoding categorical variables and data partitioning.
Model training: Train various classification and regression models with just a few lines of code.
Model evaluation: Evaluate model performance using common metrics.
This module is designed to seamlessly handle various scikit-learn models, making it flexible for handling sklearn-like model formats.
Added training feature for training multiple models for experimentation.
Installation
You can install SmpML using pip:
pip install SimpleML
Usage
The TrainModel class is designed to handle both classification and regression tasks. It determines the task type based on the target parameter. If the target has a float data type, the class automatically redirects the procedures to regression; otherwise, it assumes a classification task.
Data Preparation
Data preparation like data spliting and converting categorical data into numerical data is also automatically executed when calling the fit() method.