🚀 AutoML Project

An End-to-End Automated Machine Learning Pipeline with Dynamic Feature Engineering

📌 Overview

This project implements a complete Automated Machine Learning (AutoML) system that simplifies the process of building, training, and evaluating machine learning models.

The system is designed to:

Automatically preprocess data

Perform intelligent feature engineering

Train multiple models

Optimize performance

Select the best model

It reduces manual effort and enables faster experimentation for real-world ML problems.

🎯 Key Features

✅ Automated Data Preprocessing

Handling missing values

Encoding categorical variables

Feature scaling

✅ Dynamic Feature Engineering

Automatic feature selection

Feature transformation

Handling noisy/anomalous data

✅ Model Training & Selection

Multiple ML algorithms support

Hyperparameter tuning

Best model selection

✅ Evaluation & Metrics

Accuracy, Precision, Recall, F1-score

Model comparison

✅ Modular Pipeline Design

Clean and reusable architecture

Easy to extend for new datasets/models

🛠️ Tech Stack

Language: Python

Libraries:

scikit-learn

pandas

numpy

matplotlib / seaborn

Tools:

Jupyter Notebook / VS Code

Git & GitHub

📂 Project Structure AutoML/ │── data/ # Dataset files │── notebooks/ # Experiments & analysis │── src/ │ ├── preprocessing.py # Data cleaning & transformation │ ├── feature_engineering.py │ ├── model_training.py │ ├── evaluation.py │── models/ # Saved trained models │── utils/ # Helper functions │── main.py # Entry point │── requirements.txt │── README.md ⚙️ Installation

Clone the repository

git clone https://github.com/Atharva-1512/AutoML.git

Navigate to project

cd AutoML/project

Create virtual environment

python -m venv venv source venv/bin/activate # Windows: venv\Scripts\activate

Install dependencies

pip install -r requirements.txt ▶️ Usage python main.py

OR (if notebook-based):

jupyter notebook 📊 Workflow

Load Dataset

Data Preprocessing

Feature Engineering

Model Training

Hyperparameter Optimization

Model Evaluation

Best Model Selection

📈 Example Output

Best Model: Random Forest / XGBoost (example)

Accuracy: ~85–95% (depends on dataset)

Feature Importance Visualization

🔥 Use Cases

Predictive analytics

Fraud detection

Customer churn prediction

Academic ML experiments

Rapid prototyping for startups

🚧 Future Improvements

Add deep learning models

Integrate AutoML libraries (AutoGluon / H2O)

Build a web dashboard (Streamlit)

Deploy as API (FastAPI)

Add experiment tracking (MLflow)

🤝 Contributing

Contributions are welcome!

Fork the repo

Create a new branch

git checkout -b feature-name

Commit changes

git commit -m "Added new feature"

Push

git push origin feature-name 📜 License

This project is licensed under the MIT License.

👨‍💻 Author

Atharva Gade

AI/ML Enthusiast

BE IT (SPPU)

Interested in AutoML, Data Science & AI Research