CSVtoML is a cloud-based, mobile-ready, Streamlit-powered application to let you convert your csv file to ML model and tune the hyperparameters too.
- The app uses streamlit as frontend
- Scikit Learn in the Backend
https://csvtoml.azurewebsites.net/
- Import a CSV file and watch it magically convert to ML Model
- Choose the Best Model for your data
- Works for both classification and reression problems
- Select the hyperparameters search range and get the best hyperparamreters
- Use the generated model to predict the results
Any suggestions are welocme you can contact me [Kuljot Singh] on my email [[email protected]][df1]
There is one known issue causing the number of columns to change due to streamlit's behavior of running the entire app from starting upon interaction with buttons !Please press [R] on the keyboard or press button And it will resolve
CSVtoML uses a number of open source libraries to work :
- Streamlit - For frontend of the web apps!
- SciKit Learn - Make and train the ML models
- XGBOOST - A great ensemble type ML model as an option
- Numpy - for numerical processing
- Pandas - handeling the dataframes
And CSVtoML itself is open source with a [https://github.com/Kuljot/csv_hypertuner][dill] on GitHub. Built as an educational project. Please treat it for educational purpose only, PLEASE DONT UPLOAD ANY SENSITIVE/PERSONAL INFO.
CSVtoML requires Python 3.11+ to run.
Clone the repository on your device
git clone https://github.com/Kuljot/csv_hypertuner.git
cd csv_hypertuner
Create a virtual environment and activate it
sudo apt install python3.11-venv
python3.11 -m venv env
source env/bin/activate
Install the requirements
pip install -r requirements.txt
Run the application
streamlit run app.py
CSVtoML can be containerized via docker.
By default, the Docker will expose port 8080 but streamlit uses 8051, so in the Dockerfile I have exposed 8051 explicitly. Simply use the Dockerfile to build the image.
sudo docker build -t csvtoml .
Verify the app by navigating to your server address in your preferred browser.
http://localhost:8501/
CC OpenSource!