lazytrainer 0.1.0

lazyml: Because training ML models shouldn’t need more energy than your morning coffee.

LazyTrainer

Train your regression models with zero drama.
LazyTrainer helps you get started with machine learning model training in the simplest way possible — no unnecessary complications.

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Why LazyTrainer?

This project was created by MrFayntom with a clear goal in mind:

"I made LazyTrainer for beginners who want to train machine learning models but feel overwhelmed by all the complexity.
LazyTrainer wraps it up into a clean, lazy workflow so you can focus on what matters — learning and iterating."

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What it Does

• Automatically preprocesses your data
• Handles categorical encoding and scaling
• Trains a regression model (SGD for now)
• Evaluates performance (MAE, R², Accuracy)
• Plots predictions
• Saves the model, scaler, and encoders

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Installation

pip install lazyml

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LazyML – Quick Start Guide

For full details, visit the official GitHub repository:
: https://github.com/mrfayntom/LazyML

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Minimal Workflow Example

from lazyml.trainer import LazyTrainer

# Initialize the trainer
trainer = LazyTrainer(
    data_path="path/to/your/data.csv",          # Path to your CSV file
    target_col="TargetColumn",                  # Column name you want to predict
    features=["Feature1", "Feature2", "Feature3"],  # List of input feature column names
    categorical_cols=["Feature3"]               # List of categorical feature names (if any)
)

# Train the model
trainer.train()

# Evaluate the model
trainer.evaluate()

# Plot prediction summary
trainer.plot_summary()

# Save model, scaler and encoders
trainer.save()
trainer.save_scaler()
trainer.save_encoders()

— Mr. Phantom