---
title: "ML Prep and Train: Streamlining Machine Learning Workflows"
description: "An all-in-one Python toolkit for efficient data preprocessing and binary classification model training."
date: "2024-03-17T07:00:00Z"
last_updated: "2026-08-28"
build_time: "2026-08-31T12:31:21Z"
project_name: "Machine Learning Prep and Train Toolkit"
project_description: "The ML Prep and Train Toolkit is an integrated suite of Python scripts that automates the data analysis, preprocessing, and training phases for binary classification models — hyperparameter search and calibration per algorithm, a stacking ensemble over the base classifiers, an evaluation table that reports overfitting probability alongside the usual metrics, and a feature-importance or LIME explanation for every model. It simplifies and accelerates the machine learning pipeline for data scientists and ML engineers."
status: "Not Active"
project_group: "open-source-experiments"
period_from: "2023-05-01"
period_to: "2023-06-30"
repository: "https://github.com/alex-feel/ml-prep-and-train"
related_skills: ["artificial-intelligence","python","quality-assurance"]
related_projects: ["cxr-draft-auditor"]
related_experience: ["project-manager-at-spalvalo"]
keywords: ["Machine Learning","Data Preprocessing","Binary Classification","Ensemble Learning","Model Interpretability","LIME","Python","Automation"]
license: "https://creativecommons.org/licenses/by/4.0/"
canonical: "https://www.alexfeel.info/projects/ml-prep-and-train-toolkit/"
---

The ML Prep and Train project originated to address the need for a standardized approach to preparing datasets and training machine learning models. The toolkit provides functionality for cleaning and structuring data, feature extraction, and utilizing various machine learning algorithms for binary classification. With an emphasis on efficiency and best practices, this project serves as a cornerstone for data scientists needing to quickly iterate and test hypotheses in their binary classification problems.

Training does not stop at fitting one algorithm. Each candidate gets its own hyperparameter search, optional probability calibration, and an evaluation table reporting accuracy, precision, recall, F1, AUC and Gini beside an overfitting probability computed both by accuracy and by F1 — so a model that only looks strong on the data it was trained on is flagged rather than quietly shipped. The base classifiers then feed a stacking model, with a logistic regression, a gradient-boosted tree, or a small neural network sitting on top of their outputs.

Because a binary classifier is nearly always asked to justify itself, every model produces an explanation alongside its score: a feature-importance plot where the algorithm exposes one, and a LIME explanation where it does not.

Feel free to explore the project's repository on GitHub and contribute or adapt the toolkit to your specific needs.

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[alex-feel/ml-prep-and-train](https://github.com/alex-feel/ml-prep-and-train) — Collection of Python scripts designed to streamline data analysis, preprocessing, and binary classification modeling. (2 stars · Python · GPL-3.0)

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