AutoML Streamlit App

This repository contains a Streamlit application that allows users to train, evaluate, and apply machine learning models using PyCaret. The app supports both classification and regression tasks.

Features

• Model Training: Train machine learning models using PyCaret's autoML functionality.
• Model Evaluation: View performance metrics of the trained models.
• Model Inference: Apply the trained model to new data for predictions. Supports both single predictions and batch predictions.
• Download Predictions: Save the predictions to a CSV file for further analysis.

Installation

Prerequisites

• Python 3.7+
• Anaconda (recommended for managing environments)
• Git

Setup

1. Clone the repository:

   git clone https://github.com/yourusername/AutoML_Streamlit_App_POC.git
   cd AutoML_Streamlit_App_POC

2. Create a virtual environment:

   conda create -n tokyo python=3.8
   conda activate tokyo

3. Install the dependencies:

   pip install -r requirements.txt

4. Run the Streamlit app:

   streamlit run app.py

Usage

1. Home Page

• Start by selecting the type of task you want to perform: Classification or Regression.
• Upload your dataset, and the app will automatically train models using PyCaret.

2. Model Evaluation

• View the performance metrics of the trained models.
• Select the best model based on the evaluation metrics.

3. Apply Model

• Apply the trained model to new data.
• Choose between single prediction and batch prediction.
• Download the predictions as a CSV file.

4. Custom Settings

• Adjust settings like model hyperparameters, preprocessing options, and more.

Project Structure

• app.py: Main entry point for the Streamlit app.
• pages/: Contains separate pages for applying the model and model evaluation.
• requirements.txt: List of Python packages required to run the app.
• README.md: Documentation for the project.

Troubleshooting

Common Issues

1. NaN or Inf Values: Ensure that your data doesn't contain NaN or infinite values before applying the model. The app provides handling for such values but might need adjustments based on your dataset.
2. Duplicate Columns: The app automatically handles duplicate columns that may arise during the prediction phase.

Debugging

• Check the Streamlit app logs for any errors. Logs are usually displayed in the terminal where you ran the app.

Contributing

1. Fork the repository.
2. Create your feature branch (git checkout -b feature/yourFeature).
3. Commit your changes (git commit -am 'Add some feature').
4. Push to the branch (git push origin feature/yourFeature).
5. Create a new Pull Request.

License

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