ModelLab

A modular machine learning toolkit for training and comparing classification and regression models.

ModelLab is a Python-based machine learning project designed to simplify the process of loading datasets, preparing data, training multiple machine learning models, and evaluating their performance.

It supports both classification and regression tasks through a modular architecture built with Scikit-learn.

Features

• Load datasets from CSV files.
• Separate features and target variables.
• Validate datasets and detect common data issues.
• Handle missing values through data cleaning and imputation.
• Split datasets into training and testing sets.
• Train multiple machine learning models.
• Evaluate model performance using task-specific metrics.
• Compare different models using their evaluation results.

Supported Models

Classification

• Logistic Regression
• Decision Tree Classifier
• Random Forest Classifier

Regression

• Linear Regression
• Decision Tree Regressor
• Random Forest Regressor

Evaluation Metrics

Classification

• Accuracy
• Precision
• Recall
• F1 Score

Regression

• Mean Absolute Error (MAE)
• Mean Squared Error (MSE)
• Root Mean Squared Error (RMSE)
• R² Score

Installation

Install ModelLab using pip:

pip install modellab-ml-toolkit

Install dependencies manually if needed:

pip install pandas numpy scikit-learn

Usage

ModelLab accepts a CSV dataset and allows you to specify:

1. The dataset file path.
2. The CSV separator.
3. The target column.
4. The machine learning task type:
   • classification
   • regression

The application then processes the dataset, trains the selected models, and evaluates their performance.

Project Architecture

ModelLab follows a modular structure, separating different responsibilities into individual Python modules.

Dependencies

• Python
• Pandas
• NumPy
• Scikit-learn

Project Status

ModelLab currently supports basic classification and regression workflows.

The project is under active development, and additional functionality may be introduced in future releases.

License

See the LICENSE file for licensing information (MIT License).

Developed as a modular machine learning project using Python and Scikit-learn.