🗂️ 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.
🗂️ 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.
- 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.
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Pre-requisites:
- Python 3.6 or 3.7 or 3.8
- Dependencies from requirements.txt
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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
- First clone this repository onto your system.
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Directions to Execute
Run the following command in the terminal -
streamlit run app.py
