Ever felt tired after preprocessing the dataset, and not wanting to write any code further to train your model? Ever encountered a situation where you wanted to record the hyperparameters of the trained model and able to retrieve it afterward? Models Playground is here to help you do that. Models playground allows you to train your models right from the browser.

Models Playground

 🗂️ Upload a Preprocessed Dataset

 🌠 Choose whether to perform Classification or Regression

 🦹 Enter the Dependent Variable

 🪓 Type in the split ratio

 ⚖️ Select the Scaling method
  
 ⚙️ Pick a Model and set its Hyper-Parameters

 📉 Train it and check its Performance Metrics on Train and Test data

 🩺 Diagnose possible overitting and experiment with other settings

 📠 Copy the code snippet to run in jupyter or colab

 🚀 If you find the model to be performing well, save the model's hyperparameters with a single click

 🎑 View all the saved models.

What is the idea?

• Ever felt tired after preprocessing the dataset, and not wanting to write any code further to train your model? Ever encountered a situation where you wanted to record the hyperparameters of the trained model and able to retrieve it afterward
• Models Playground is here to help you do that. Models playground allows you to train your models right from the browser.
• Just upload the preprocessed dataset, choose the option to perform classification or regression. Enter the dependent variable, type in the split ratio and choose a scaling method(MinMaxScaler or StandardScaler), and enter the columns to be scaled.
• Select a model and adjust its hyperparameters to get the best result.
• Copy the automatically generated code snippet to train in Jupyter or Colab.
• If you find the model to be performing well click the save hyperparameters button to save the results to a data.txt file.
• To view all the hyperparameters saved till now, display them on the screen with a single click.

Built Using:

Instructions to run:

• Pre-requisites:

  • Python 3.6 or 3.7 or 3.8
  • Dependencies from requirements.txt

• Directions to Install

  • First clone this repository onto your system.
    
  • Then, create a Virtual Environment and install the packages from requirements.txt:
    
  • Navigate to this repository, create a Virtual Environment and activate it:
    

  cd path/to/cloned/repo
  
  ##(for Mac and Linux)
  python3 -m venv env 
  source env/bin/activate
  ##(for Windows)
  python3 -m venv env
  .\env\Scripts\activate

  Install the python dependencies from requirements.txt:

  pip install -r requirements.txt

• Directions to Execute

  Run the following command in the terminal -

  streamlit run app.py

Different Screens