AutoML Studio Pro

A no-code, end-to-end automated machine learning platform for training, evaluating, and deploying ML models—right from your browser.

       

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Short Description: AutoML Studio Pro is an interactive, browser-based platform that automates the complete machine learning workflow—from data upload and exploratory analysis to model training, evaluation, and prediction—without writing a single line of code.

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Table of Contents

• Overview
• Key Features
• Tech Stack
• Getting Started
• CI & Security Checks
• Project Structure
• Usage
• Architecture
• Project Governance
• Suggested Repository Topics
• Contributing
• Support This Project
• License
• Contact

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Overview

AutoML Studio Pro eliminates the complexity of building machine learning models. Upload a CSV dataset, select a target column, and let the platform handle the rest—preprocessing, model selection, training, evaluation, and export.

Built with Streamlit and Scikit-Learn, the application is designed for:

• Beginners who want to explore ML without writing code.
• Students looking for an educational tool with exportable Python scripts.
• Practitioners who need quick baseline models and batch-prediction capabilities.

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Key Features

🚀 AutoML Engine

Feature	Description
Automatic Task Detection	Determines whether the problem is classification or regression based on the target column.
Automated Preprocessing	Handles missing values, encoding, and feature scaling via Scikit-Learn pipelines.
Intelligent Model Selection	Trains multiple models (RandomForest, GradientBoosting, XGBoost, etc.) and selects the best.
Imbalanced Data Handling	Applies SMOTE oversampling for skewed classification datasets.
Hyperparameter Tuning	Lightweight tuning with Optuna integration for advanced optimization.
Ensemble Models	Combine multiple models using voting or stacking ensembles.

📊 Data Insights

Feature	Description
Exploratory Data Analysis	Generates descriptive statistics, outlier detection, and correlation heatmaps.
Advanced EDA Analytics	Six comprehensive tabs: Statistics, Target Analysis, Correlations, Distributions, Data Quality, Variance.
Feature Importance (XAI)	Uses permutation importance and SHAP values to explain model predictions.
Performance Metrics	Displays confusion matrices, accuracy scores, R² scores, and prediction plots.
Cross-Validation Visualization	Bar charts and histograms showing CV scores across models.

🔧 Feature Engineering

Feature	Description
Polynomial Features	Automatically creates polynomial features to capture non-linear relationships.
Interaction Features	Generates feature interactions to discover combined effects.
Statistical Aggregations	Creates row-wise statistics (mean, std, min, max) for numeric features.
Missing Value Strategies	Choose from median, mean, most_frequent, or constant imputation.
Preprocessing Preview	Visual preview of all preprocessing steps before training.

🧠 Advanced ML

Feature	Description
SHAP Explainable AI	Model interpretability with SHAP values and feature explanations.
Optuna Optimization	Advanced hyperparameter optimization with configurable trials.
NLP/Text Classification	TF-IDF text preprocessing with configurable n-gram ranges.
Time Series Forecasting	ARIMA and Exponential Smoothing models for temporal data.
Data Versioning	Track and compare datasets across versions with MD5 hashing.

📁 Sample Datasets

Feature	Description
Built-in Datasets	Iris, Wine, Breast Cancer, and Diabetes datasets for quick demos.
One-Click Loading	Load sample datasets instantly without uploading files.

💾 Export & Deploy

Feature	Description
Model Export	Download trained models as .zip archives for reuse.
Python Code Export	Export a ready-to-run train_model.py script for learning and customization.
PDF Report Export	Generate HTML reports with model details and metrics.
Batch Predictions	Upload CSV files to generate predictions at scale.
Dynamic Prediction Form	Auto-generated input form based on dataset schema for single predictions.
Model History	Track and compare previously trained models with visualizations.

🎨 User Experience

Feature	Description
Dark/Light Theme	Toggle between dark and light themes via sidebar.
Responsive Design	Optimized for desktop, tablet, and mobile devices.
Real-time Feedback	Live status updates during training with progress indicators.

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Tech Stack

Layer	Technology
Frontend / UI	Streamlit
Machine Learning	Scikit-Learn — RandomForest, GradientBoosting, XGBoost, Pipelines
Hyperparameter Optimization	Optuna
Explainable AI	SHAP
Time Series	Statsmodels — ARIMA, Exponential Smoothing
Data Processing	Pandas, NumPy
Visualization	Matplotlib, Seaborn
Model Serialization	Joblib
Imbalance Handling	Imbalanced-Learn (SMOTE)

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Getting Started

Prerequisites

• Python 3.9 or higher
• pip (Python package manager)

Installation

# 1. Clone the repository
git clone https://github.com/himanshu231204/AutoML-Studio-Pro-.git
cd AutoML-Studio-Pro-

# 2. (Recommended) Create and activate a virtual environment
python -m venv venv
# Windows
venv\Scripts\activate
# macOS / Linux
source venv/bin/activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Launch the application
streamlit run app.py

The application will open automatically in your default browser at http://localhost:8501.

Run with Docker

If you prefer running the app in a container, use one of the following options.

Prerequisites:

• Docker Desktop installed and running

Option 1: Docker Compose (recommended)

# Build and run in detached mode
docker compose up -d --build

# Open the app
# http://localhost:8501

# Stop containers
docker compose down

Option 2: Docker CLI

# Build image
docker build -t automl-studio-pro .

# Run container
docker run -d -p 8501:8501 --name automl_studio_pro automl-studio-pro

# Stop and remove container
docker stop automl_studio_pro
docker rm automl_studio_pro

Notes:

• The app is exposed on port 8501.
• requirements.txt is UTF-8 encoded for Linux container compatibility.

Troubleshooting:

• Docker command not found: Install Docker Desktop and restart terminal, then run docker --version.
• Docker daemon is not running: Start Docker Desktop, wait until status is "Engine running", then retry.
• Port 8501 already in use: Run with a different host port, for example docker run -d -p 8502:8501 --name automl_studio_pro automl-studio-pro.
• Container exits immediately: Check logs with docker logs automl_studio_pro.
• Dependency changes are not reflected: Rebuild image with docker compose up -d --build or docker build --no-cache -t automl-studio-pro ..

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CI & Security Checks

This repository uses GitHub Actions for linting, tests, and dependency security scanning.

Security Scan Behavior

• CI uses Safety to scan dependency files directly:
  • requirements.txt
  • requirements-dev.txt
• This avoids false failures from transient environment/toolchain packages and keeps scans focused on declared project dependencies.

Run the same checks locally

ruff check .
python -m compileall app.py automl_app tests
pytest -q
safety check -r requirements.txt
safety check -r requirements-dev.txt

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Project Structure

├── .github/                    # CI/CD and community files
│   ├── ISSUE_TEMPLATE/         # Bug report and feature request templates
│   └── workflows/              # GitHub Actions pipelines (ci.yml, cd.yml, tests.yml)
├── .streamlit/                 # Streamlit Cloud configuration
│   └── config.toml             # Server and theme settings
├── artifacts/                  # Auto-generated models & schema files
├── assets/
│   └── images/
│       ├── badges/             # Local badge assets (optional)
│       └── screenshots/        # UI screenshots for README/docs
├── automl_app/
│   ├── core/                   # Shared config and helper utilities
│   │   ├── config.py           # Page setup, theming, CSS
│   │   └── helpers.py          # ML utilities (preprocessing, model selection, etc.)
│   └── ui/                     # Reusable UI components and tab modules
│       ├── tabs/               # Streamlit tab modules
│       │   ├── train.py        # Training tab with all Phase 1 & 2 features
│       │   ├── analysis.py     # Advanced EDA with 6 sub-tabs
│       │   ├── prediction.py   # Single and batch predictions
│       │   ├── manual.py       # User guide
│       │   └── developer.py    # Developer info
│       └── footer.py           # Shared footer component
├── docs/
│   ├── api/                    # API/exported interface docs
│   └── guides/                 # User and developer guides
├── tests/                      # Unit tests
│   ├── test_phase1_features.py # Phase 1 feature tests
│   ├── test_helpers.py         # Helper function tests
│   └── test_train_utils.py     # Training utility tests
├── app.py                      # Main Streamlit application
├── Dockerfile                  # Multi-stage Docker build
├── docker-compose.yml          # Docker Compose configuration
├── requirements.txt            # Production dependencies
├── requirements-dev.txt        # Development dependencies
├── FEATURES_ROADMAP.md         # Feature roadmap and status
├── CHANGELOG.md                # Version history
└── README.md                   # Project documentation

App Flow

• app.py initializes the page and routes each Streamlit tab to its dedicated module.
• automl_app/core holds reusable configuration and ML utility functions.
• automl_app/ui/tabs keeps each product area isolated for easier maintenance.
• automl_app/ui contains shared UI components used across the app.

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Usage

1. Train & Learn

Upload a CSV file or load a sample dataset, then configure your training:

• Select Target Column — Choose the column to predict
• Training Mode — Fast or High Accuracy mode
• Missing Value Strategy — Choose imputation method (median/mean/most_frequent/constant)
• Feature Engineering — Enable polynomial features, interactions, or aggregations
• Advanced AutoML — Enable Optuna for hyperparameter optimization
• Ensemble Models — Combine multiple models with voting/stacking
• NLP/Text — Enable TF-IDF for text classification
• Time Series — Enable ARIMA/ETS for temporal forecasting

Click Start Training to run the AutoML pipeline. View results including:

• Model leaderboard with cross-validation scores
• Feature importance and SHAP explanations
• Confusion matrices and prediction plots
• Model history and comparison dashboard

Download the trained model or export the equivalent Python code.

2. Data Analysis

Explore the uploaded dataset through six comprehensive analysis tabs:

• Statistics — Skewness, kurtosis, and detailed statistical summaries
• Target Analysis — Class balance detection and distribution visualization
• Correlations — Feature correlations with heatmap visualization
• Distributions — Histograms, KDE, and Q-Q plots
• Data Quality — Completeness, uniqueness, and quality scores
• Variance Analysis — Feature variance contribution analysis

3. Production Engine

Load a previously trained model or use the current session model:

• Single Predictions — Dynamic form based on dataset schema
• Batch Predictions — Upload CSV for bulk inference
• Model Export — Download as .zip archive
• Report Export — Generate HTML report with metrics

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Architecture

For technical design details, see ARCHITECTURE.md.

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Project Governance

• Contribution workflow: CONTRIBUTING.md
• Community standards: CODE_OF_CONDUCT.md

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Suggested Repository Topics

automl · machine-learning · no-code · streamlit · scikit-learn · data-science · python · automated-machine-learning · eda · classification · regression · model-training · feature-importance · smote · gradient-boosting · shap · optuna · time-series · nlp · feature-engineering · explainable-ai · hyperparameter-optimization

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Contributing

Contributions are welcome. Please read CONTRIBUTING.md before opening a PR.

Quick start:

1. Fork the repository.
2. Create a feature branch (git checkout -b feature/your-feature).
3. Commit your changes (git commit -m "Add your feature").
4. Push to the branch (git push origin feature/your-feature).
5. Open a Pull Request.

When you open a PR, GitHub will auto-load the pull request template to keep reviews consistent.

Please use the GitHub issue templates for bug reports and feature requests:

• Bug report template for reproducible defects.
• Feature request template for enhancements and roadmap ideas.

You can open a new issue here: Issues.

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💖 Support This Project

If this project helped you, consider supporting my work!

Every contribution helps me:

• ⏰ Spend more time on open-source
• 🆓 Keep all tools free for everyone
• 📚 Create more tutorials and guides
• 🚀 Build new developer tools

⭐ Star this repo if you find it useful — it means a lot!

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License

This project is licensed under the MIT License. See the LICENSE file for details.

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Contact

Platform	Link
GitHub	himanshu231204
LinkedIn	himanshu231204
X (Twitter)	himanshu231204