README.md

metadata

title: ML Workbench
emoji: 📊
colorFrom: green
colorTo: blue
sdk: streamlit
app_file: app.py
pinned: false

ML Workbench

ML Workbench is a powerful and intuitive Streamlit application designed to streamline your machine learning workflow. From data upload to model deployment, ML Workbench provides a unified interface for all your data analysis needs.

Features

• Easy Data Upload: Support for CSV file uploads.
• Comprehensive Preprocessing:
  • Handle missing values.
  • Normalize numeric columns.
  • Automatic identification of numeric and categorical columns.
• Model Training:
  • Support for multiple algorithms including:
    • Linear Regression
    • Random Forest (Regressor & Classifier)
    • K-Nearest Neighbors (Regressor & Classifier)
    • Support Vector Machines (SVR & SVC)
    • Logistic Regression
    • Decision Tree
    • Naive Bayes
• Performance Evaluation:
  • Accuracy score and classification report for classifiers.
  • Mean Squared Error (MSE) for regressors.
• Interactive Prediction: Test your trained model with custom inputs directly in the app.
• Model Export: Save your trained model and metadata as a ZIP file for deployment.

Installation

1. Clone the repository:

   git clone https://github.com/sowmiyan-s/ML-WorkBench.git
   

2. Navigate to the project directory:

   cd ML-WorkBench
   

3. Install the required dependencies:

   pip install -r requirements.txt
   

Usage

Run the Streamlit app:

streamlit run Main.py

Credits

Created by Sowmiyan S.

License: MIT