{"repo":"AmirhosseinHonardoust/ML-Playground-Autodetect","free":true,"listed":false,"github":"https://github.com/AmirhosseinHonardoust/ML-Playground-Autodetect","clone":"git clone https://github.com/AmirhosseinHonardoust/ML-Playground-Autodetect.git","description":"A Streamlit-powered machine learning playground that automatically detects classification or regression tasks, builds pipelines with preprocessing, trains models interactively, and visualizes metrics using Plotly. Backward-compatible, fully responsive, and deployable on Streamlit Cloud or Docker.","language":"Python","stars":21,"topics":["ai","auto-detect","classification","data-science","data-visualization","docker","huggingface","machine-learning","model-training","plotly"],"license":"MIT","category":"deployment-docker-iac","readme_excerpt":"ML Playground, Auto‑Detect, Clean & Future‑Proof An interactive Streamlit + Scikit‑Learn web application that allows anyone to upload a dataset, automatically identify whether the problem is classification or regression , train multiple ML models interactively, visualize the results, and export the trained model, all from a single browser interface. --- Table of Contents 1. Overview 2. Architecture 3. Key Features 4. Data Flow Diagram 5. Design Decisions 6. Preprocessing Pipeline 7. Supported Models 8. Metrics Explained 9. User Guide 10. Deployment Guide 11. Troubleshooting 12. Performance Tips 13. Roadmap --- Overview ML Playground provides a practical and educational environment for machine learning experimentation. It bridges the gap between Jupyter notebooks and full production pipelines, ideal for data analysts, ML students, educators, and developers who want to quickly prototype or demonstrate machine learning behavior on real data. Goals: - Automate tedious parts of ML workflow (data cleaning, encoding, scaling) - Detect task type and prevent invalid configurations (classifier on continuous targets) - Provide instant, intuitive visual feedback - Ensure full forward and backward compatibility with future Streamlit and Scikit‑Learn versions --- Architecture System Flow Component Breakdown Component Purpose ------------ ---------- Frontend (Streamlit) Renders user interface, handles file uploads, parameter selection, and visualization. Task Detection Engine Analyzes targe","default_branch":null,"files":null,"tree":[],"storefront":"/r/AmirhosseinHonardoust","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/AmirhosseinHonardoust/ML-Playground-Autodetect/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}