BlocklyML is a No Code training ground for python and Machine Learning. This tool is designed to simplify standard machine learning implementation. This tool can assist anyone who wants to start with Machine Learning or python. This is a forked project from Blockly and adapted for machine learning and Data analytics use-cases. 🧠
For a sample run go to sampleLayouts folder upload and try it out 😃
In the Example given below we will train a random forest for Iris Dataset
blocklyML_demo.mp4
First clone this repo
git clone https://github.com/chekoduadarsh/BlocklyML
After cloning the repo you can either follow the Flask Method
If you've cloned the project and want to build the image, follow these steps:
1.Open your terminal and navigate to the project directory.
2.Run the following command to build the Docker image:
docker build . -t blocklyml/demo
Once the image is built, you can launch the app by executing the following command:
docker run -ti -p5000:5000 blockly_ml/demo
This will start the app, and you'll be able to access it by opening your web browser and navigating to http://localhost:5000
Install the requirements from requirements.txt
with the following command
pip install -r requirements.txt
then you can run the application by
python app.py
Simple as that 🤷♂️
You can find these buttons in the top right corner of the application. Their functionality as follows
- Download XML Layout
- Upload XML layout
- Copy Code
- Launch Google Colab
- Delete
- Run (Not Supported Yet!!)
Blockly support complete html view of the DataFrame. This can be accessed by view option in the navigation bar
Blockly support both .py and .ipynb formats. You can download the code from the download option in the navigation bar
If you find any error or need support please raise a issue. If you think you can add a feature, or help solve a bug please raise a PR
Feel free to adapt it criticize it and support it the way you like!!
Read : CONTRIBUTING.md