ML interface

This application is capable of building the model, save it for future purpose, without writing a single piece of code!
A special library, Streamlit is used to develop the application's interface. The documentation can be found here

🦾 Usage

• The application can be found here.
• Upload the .csv file you wanted to build the model on.
• Select the features/columns form the drop-down menu.
• Handle the missing data(NaN) using different strategies. (A warning is displayed if it cannot be added. Try another strategy)
• Enccode the columns for training. (One-Hot encoder)
• Split the data into training and dev/test sets. (The max. split is set to 0.3 i.e., the dataset is split in the ratio, 70/30)
• Normalise the data.
• Select the algorithm for predicting.
• Modify the hyperparameters on the sidebar for better results.
• Click save button to save the model for later use.

⛏️ Develop

• Clone the repository from above or in the commad line use:

$ git clone https://github.com/Nitin1901/machine-learning-interface.git

• Change you current working directory.

$ cd machine-learning-interface

• Create a virtual environment(recommended) and activate.

$ python -m venv ml-intreface
$ ml-interface\Scripts\activate.bat

• Install the required packages from requirements.txt. You can manually install each package or use:

$ pip install -r requirements.txt

• Open app.py in a text editor and start making changes.
• Run the app locally

$ streamlit run app.py

If you wish to contribute, fork the repository, develop and create a pull request.