Auto-ML-Web-App

AutoML Web App using Streamlit

Overview

This project is an AutoML (Automated Machine Learning) web application built using Streamlit. It allows users to easily upload datasets, select target variables, and train machine learning models with minimal coding required.

Display

Features

• User-friendly Interface: Intuitive web interface built with Streamlit for seamless interaction.
• Dataset Upload: Users can upload their datasets in various formats (CSV, Excel, etc.).
• Automated Model Selection: The application automates the process of model selection based on the dataset characteristics.
• Model Training: Once a dataset is uploaded, users can choose target variables and initiate the model training process.
• Model Evaluation: After training, the application provides model evaluation metrics and visualizations for performance analysis.
• Model Deployment: Users can deploy trained models for inference directly from the web app.

Technologies Used

• Streamlit: Used for building the web application interface.

• Scikit-learn: Leveraged for machine learning model training and evaluation.

• Pandas: Utilized for data manipulation and preprocessing.

• Matplotlib and Seaborn: Used for data visualization within the application.

  Web App

  

Setup Instructions

Clone the repository: Copy code

• git clone https://github.com/ritikkumar55/Auto-Ml-Web-App.git

Install dependencies:

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• pip install -r requirements.txt

Run the Streamlit app:

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• streamlit run app.py
• Access the web application via the provided local URL.

Usage

• Upload your dataset using the provided interface.
• Select the target variable(s) for model training.
• Initiate the model training process.
• Evaluate model performance using the provided metrics and visualizations.
• Optionally, deploy the trained model for inference.