Auto Modeler

📌 Project Overview

Auto Modeler is a machine learning automation tool that allows users to upload any dataset and get a predictive model instantly. The application, built with Streamlit, automatically preprocesses data, selects the best model, and provides predictions.

🚀 Features

• Upload Any Dataset – Supports CSV file uploads.
• Automated Data Preprocessing – Handles missing values, categorical encoding, and scaling.
• Model Selection & Training – Tests multiple ML models and selects the best one based on performance.
• Performance Metrics – Displays accuracy, precision, recall, and other evaluation metrics.
• Real-time Predictions – Users can input new data and get predictions instantly.

🛠️ Tech Stack

• Frontend: Streamlit
• Backend: Python
• Machine Learning: Scikit-Learn, Pandas, NumPy
• Deployment: Streamlit Cloud / Local Execution

📂 Installation & Usage

1️⃣ Clone the Repository

git clone [https://github.com/yourusername/auto-modeler.git](https://github.com/Knightkolla/Auto-modeller)
cd Auto-modeller

2️⃣ Install Dependencies

pip install -r requirements.txt

3️⃣ Run the Streamlit App

streamlit run app.py

📊 How It Works

1. Upload Dataset – The user uploads a CSV file.
2. Preprocessing – The system cleans and prepares the data automatically.
3. Model Training – The best model is selected and trained.
4. Predictions – The user can enter new values to get predictions.

🎯 Future Enhancements

• Add support for deep learning models.
• Improve model interpretability with SHAP and LIME.
• Provide hyperparameter tuning options.

🤝 Contributors

• Dhavala Kartikeya Somayaji – LinkedIn | GitHub
• Chetna
• Dipon mitra

📜 License

This project is licensed under the MIT License. Feel free to contribute and enhance it!