https://github.com/shaadclt/no-code-ml-model-training
This project aims to provide a user-friendly interface for training machine learning models without the need for coding. It allows users to select a dataset, preprocess it, choose a model, train the model, and evaluate its performance, all through an intuitive web interface built with Streamlit.
https://github.com/shaadclt/no-code-ml-model-training
Last synced: 6 months ago
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This project aims to provide a user-friendly interface for training machine learning models without the need for coding. It allows users to select a dataset, preprocess it, choose a model, train the model, and evaluate its performance, all through an intuitive web interface built with Streamlit.
- Host: GitHub
- URL: https://github.com/shaadclt/no-code-ml-model-training
- Owner: shaadclt
- Created: 2024-03-25T18:21:30.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2024-03-25T18:33:50.000Z (over 1 year ago)
- Last Synced: 2025-02-02T09:41:21.394Z (8 months ago)
- Language: Python
- Homepage: https://no-code-ml-model-training.streamlit.app/
- Size: 16.6 KB
- Stars: 2
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# No-code ML Model Training
This project aims to provide a user-friendly interface for training machine learning models without the need for coding. It allows users to select a dataset, preprocess it, choose a model, train the model, and evaluate its performance, all through an intuitive web interface built with Streamlit.
## Features
- **Dataset Selection**: Users can choose from a list of available datasets to train their models on.
- **Data Preview**: Provides a preview of the selected dataset, displaying the first few rows.
- **Target Column Selection**: Allows users to specify the target column for prediction.
- **Scalability**: Supports both standard and min-max scaling for numerical features.
- **Model Selection**: Offers a variety of models to choose from, including Logistic Regression, Support Vector Classifier, Random Forest Classifier, and XGBoost Classifier.
- **Model Training**: Trains the selected model on the preprocessed data and saves the trained model for future use.
- **Model Evaluation**: Evaluates the trained model's performance using accuracy score on the test data.
## Usage
1. Clone the repository:
```bash
git clone https://github.com/shaadclt/No-Code-ML-Model-Training.git
cd No-Code-ML-Model-Training
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. Run the Streamlit app:
```bash
streamlit run main.py
```
4. Access the app through your browser at **http://localhost:8501**.
## Dependencies
1. Streamlit
2. scikit-learn
3. xgboost
4. pandas
## Author
This project was developed by Mohamed Shaad. You can connect with me on LinkedIn and check out my other projects on GitHub.