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https://github.com/jayadavv/dynamic-ml-model-selector
An interactive web application that allows users to upload their datasets and dynamically select, train, and evaluate various machine learning models. The app provides comprehensive performance metrics and visualizations, making it easy for users to analyze their data effectively.
https://github.com/jayadavv/dynamic-ml-model-selector
decision-trees linear-regression logistic-regression matplotlib-pyplot plotly python random-forest scikit-learn streamlit
Last synced: 19 days ago
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An interactive web application that allows users to upload their datasets and dynamically select, train, and evaluate various machine learning models. The app provides comprehensive performance metrics and visualizations, making it easy for users to analyze their data effectively.
- Host: GitHub
- URL: https://github.com/jayadavv/dynamic-ml-model-selector
- Owner: Jayadavv
- Created: 2024-10-18T06:52:32.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2024-10-18T20:02:45.000Z (3 months ago)
- Last Synced: 2024-12-18T17:13:29.660Z (19 days ago)
- Topics: decision-trees, linear-regression, logistic-regression, matplotlib-pyplot, plotly, python, random-forest, scikit-learn, streamlit
- Language: Python
- Homepage: https://dynamic-ml-model-selector.streamlit.app/
- Size: 23.4 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Dynamic Machine Learning Model Selector
## Overview
The **Dynamic Machine Learning Model Selector** is an interactive web application designed to facilitate the selection and evaluation of various machine learning models. This tool allows users to easily upload datasets, select features and target variables, and dynamically apply regression and classification models. It aims to streamline the model selection process by providing essential performance metrics and visualizations.
## Features
- **Dataset Upload**: Users can upload their own CSV datasets for analysis.
- **Model Selection**: Choose from multiple machine learning models, including:
- Linear Regression
- Logistic Regression
- Decision Tree Classifier/Regressor
- Random Forest Classifier/Regressor (with hyperparameter tuning)
- **Visualization**: Visualize model performance through confusion matrices, bar graphs of Mean Squared Error, and other metrics.
- **User-Friendly Interface**: Built with Streamlit for an intuitive user experience.
## Getting Started
### Prerequisites
- Python 3.x installed on your system. Download it from [python.org](https://www.python.org/downloads/).
### Installation
Follow the steps below to set up the project:
1. Open a terminal and navigate to your project folder:
```bash
cd myproject
```
2. Create a virtual environment:
```bash
python -m venv venv
```
3. Activate the virtual environment:
- **Windows PowerShell**:
```bash
.\venv\Scripts\Activate.ps1
```
- **macOS/Linux**:
```bash
source venv/bin/activate
```
4. Install the required libraries:
```bash
pip install streamlit pandas scikit-learn matplotlib seaborn plotly
```
5. Launch the application:
```bash
streamlit run app.py
```
## Requirements
The following Python libraries are required for this application:
- `streamlit`
- `pandas`
- `scikit-learn`
- `matplotlib`
- `seaborn`
- `plotly`
These can be installed using:
```bash
pip install -r requirements.txt