The AutoML Framework is an intelligent Machine Learning application that automates the complete ML workflow—from data preprocessing to model selection.
Instead of manually cleaning datasets and experimenting with multiple algorithms, users simply upload a CSV dataset, choose the target column, and let the framework handle the remaining tasks automatically.
The project is built using Python, Scikit-learn, and Streamlit, making machine learning accessible to beginners while providing rapid experimentation for developers.
- 📂 Upload any CSV dataset
- 🧹 Automatic Data Preprocessing
- 🔍 Missing Value Handling
- 🔤 Automatic Label Encoding
- 🤖 Multi-Model Training
- 📊 Model Performance Comparison
- 🏆 Automatic Best Model Selection
- 💾 Download Trained Model
- 📈 Interactive Accuracy Charts
- 🎨 Professional Streamlit Dashboard
- ✅ End-to-End AutoML Pipeline
- ✅ Clean and Modular Code Structure
- ✅ Multiple Machine Learning Algorithms
- ✅ Automatic Best Model Selection
- ✅ Download Trained Model
- ✅ Interactive Web Dashboard
- ✅ Beginner Friendly
- ✅ Resume Ready Project
| Category | Technologies |
|---|---|
| Language | Python |
| Machine Learning | Scikit-learn |
| Data Processing | Pandas, NumPy |
| Visualization | Plotly |
| Model Saving | Joblib |
| Web Framework | Streamlit |
AutoML-Framework/
│
├── assets/
├── data/
├── models/
├── notebooks/
├── outputs/
├── src/
│ ├── automl.py
│ ├── preprocessing.py
│ ├── model_training.py
│ ├── evaluation.py
│ └── data_loader.py
│
├── streamlit_app/
│ └── app.py
│
├── README.md
├── requirements.txt
└── LICENSE
flowchart TD
A[Upload CSV Dataset]
B[Data Preprocessing]
C[Missing Value Handling]
D[Label Encoding]
E[Train Multiple ML Models]
F[Compare Performance]
G[Select Best Model]
H[Download Model]
A --> B
B --> C
C --> D
D --> E
E --> F
F --> G
G --> H
The framework automatically trains and compares:
| Model | Status |
|---|---|
| Logistic Regression | ✅ |
| Decision Tree | ✅ |
| Random Forest | ✅ |
| K-Nearest Neighbors | ✅ |
| Support Vector Machine | ✅ |
The model with the highest accuracy is automatically selected as the best model.
The framework works with any structured CSV dataset.
Examples include:
- Titanic Dataset
- Iris Dataset
- Heart Disease Dataset
- Telecom Customer Churn Dataset
- Bank Marketing Dataset
- Student Performance Dataset
Clone the repository
git clone https://github.com/HarshitKumarModi/AutoML-Framework.git
cd AutoML-Framework
pip install -r requirements.txt
streamlit run streamlit_app/app.py- Regression Support
- Hyperparameter Optimization
- Cross Validation
- Automatic Feature Engineering
- SHAP Explainability
- Feature Importance Visualization
- One-Click Deployment
- Model Versioning
Harshit Kumar Modi
🎓 B.Tech Computer Science Engineering
🏛 VIT Bhopal University
🔗 GitHub: https://github.com/HarshitKumarModi
If you found this project useful,
please consider giving it a ⭐ on GitHub.
It motivates me to build more open-source Machine Learning projects.
This project is licensed under the MIT License.