🤖 AutoML App

A powerful and user-friendly AutoML application built with Streamlit empowers users to perform automated machine-learning tasks easily.

📋 Table of Contents

• Features
• Screenshots
• Installation
• Usage
• Models
• Contributing

✨ Features

Our AutoML App offers a range of features to streamline your machine-learning workflow:

• 📤 Data Ingestion: Easily upload your CSV files
• 🔧 Data Transformation:
  • Remove unwanted features
  • Handle null values
  • Convert categorical data to numerical
• 🧠 Auto Train ML Models:
  • Decision Tree
  • AdaBoost
  • Random Forest
  • SVM
  • Linear Regression
• 💾 Model Export: Save and download trained models

📸 Screenshots

Here are some screenshots of the AutoML Streamlit App in action:

Data Ingestion Page

Data Transformation Page

Deleting Features in Data Transformation Page

Auto Train ML Models Page

Freeze the Learnings Page

Downloading SVM model in Freeze the Learnings Page

🚀 Installation

To get started with the AutoML Streamlit App, follow these steps:

1. Clone the repository:

   git clone https://github.com/yourusername/automl-streamlit-app.git
   

2. Navigate to the project directory:

   cd automl-streamlit-app
   

3. Create a virtual environment (optional but recommended):

   python -m venv venv
   source venv/bin/activate  # On Windows, use `venv\Scripts\activate`
   

4. Install the required packages:

   pip install streamlit pandas scikit-learn joblib
   

   Alternatively, you can create a requirements.txt file with the following content:

   streamlit
   pandas
   scikit-learn
   joblib
   

   Then install the requirements using:

   pip install -r requirements.txt
   

These packages are necessary for running the AutoML Streamlit App:

• streamlit: For creating the web application interface
• pandas: For data manipulation and analysis
• scikit-learn: For machine learning models and utilities
• joblib: For model serialization and deserialization

Make sure you have Python 3.6 or later installed on your system.

🖥️ Usage

To run the AutoML Streamlit App:

1. Ensure you're in the project directory and your virtual environment is activated (if you created one).

2. Run the Streamlit app:

   streamlit run app.py
   

3. Open your web browser and go to http://localhost:8501 to access the app.

4. Follow the intuitive interface to upload your data, perform transformations, train models, and export the results.

🤖 Models

The AutoML Streamlit App supports the following machine learning models:

• Decision Tree Classifier
• AdaBoost Classifier
• Random Forest Classifier
• Support Vector Machine (SVM)
• Linear Regression (for continuous target variables)

🤝 Contributing

We welcome contributions to improve the AutoML App! Here's how you can contribute:

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

Made with ❤️ by Amitesh Mishra.

If you find this project helpful, please give it a ⭐️!