https://github.com/nitin-bommi/machine-learning-interface
Need not code to compare different models. Drop the .csv file and build the model with amazing UI.
https://github.com/nitin-bommi/machine-learning-interface
ai algorithms artificial-intelligence machine-learning ml model neural-networks python sklearn streamlit ui
Last synced: about 1 year ago
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Need not code to compare different models. Drop the .csv file and build the model with amazing UI.
- Host: GitHub
- URL: https://github.com/nitin-bommi/machine-learning-interface
- Owner: nitin-bommi
- License: gpl-3.0
- Created: 2020-07-13T05:39:27.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2024-02-01T21:46:18.000Z (over 2 years ago)
- Last Synced: 2025-03-23T20:04:14.278Z (about 1 year ago)
- Topics: ai, algorithms, artificial-intelligence, machine-learning, ml, model, neural-networks, python, sklearn, streamlit, ui
- Language: Python
- Homepage:
- Size: 30.3 KB
- Stars: 7
- Watchers: 2
- Forks: 4
- Open Issues: 3
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# 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](https://docs.streamlit.io/en/stable/)
### 🦾 Usage
* The application can be found [here.](https://machine-learning-interface.herokuapp.com/)
* 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:
```console
$ git clone https://github.com/Nitin1901/machine-learning-interface.git
```
* Change you current working directory.
```console
$ cd machine-learning-interface
```
* Create a virtual environment(recommended) and activate.
```console
$ 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:
```console
$ pip install -r requirements.txt
```
* Open `app.py` in a text editor and start making changes.
* Run the app locally
```console
$ streamlit run app.py
```
*If you wish to contribute, `fork` the repository, develop and create a `pull request`.*