https://github.com/radanpro/data-science
https://github.com/radanpro/data-science
Last synced: 23 days ago
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- Host: GitHub
- URL: https://github.com/radanpro/data-science
- Owner: radanpro
- Created: 2024-12-16T14:16:14.000Z (5 months ago)
- Default Branch: main
- Last Pushed: 2025-02-05T16:46:57.000Z (3 months ago)
- Last Synced: 2025-02-15T16:57:49.966Z (3 months ago)
- Language: Python
- Size: 6.35 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Data Processing and Visualization App
This is a **Python-based application** designed for data processing, visualization, and machine learning predictions. It provides a user-friendly interface built with **streamlit** for loading, processing, and analyzing datasets. The application also supports training and testing machine learning models, as well as making predictions based on user input.
**Advanced Data Processing & Predictive Modeling Toolkit**
[](https://www.python.org/)
[](https://streamlit.io/)
[](LICENSE)
---
## Features
- **Data Loading**: Load datasets in CSV or Excel formats.
- **Data Processing**:
- Handle missing values by dropping rows with null values.
- Encode categorical and target columns using Label Encoding.
- Balance datasets using Random Over-Sampling.
- Perform feature selection to identify the most relevant features.
**Algorithm Choices**:
- Random Forest (Customizable trees/depth)
- Neural Networks (Flexible architecture builder)
- **Performance Metrics**:
- Accuracy, Precision, Recall, F1-Score
- Interactive Confusion Matrix
- Classification Report
- **Data Visualization**:
- Plot histograms, scatter plots, and 3D scatter plots for up to 3 features.
- **Machine Learning**:
- Train and test machine learning models (Random Forest or Deep Learning).
- Evaluate model performance using accuracy, precision, and recall metrics.
- **Prediction**
- **Single Prediction**: Interactive input form with real-time results
- **Batch Prediction**: CSV upload with downloadable results
- **Confidence Scores**: Probability visualization for predictions
---
## Requirements
To run this application, you need the following:
- **Python 3.x** (Download and install Python from [here](https://www.python.org/downloads/)).
- **Libraries**:
- `pandas`
- `scikit-learn`
- `tensorflow`
- `matplotlib`
- `seaborn`
- `tkinter`
- `imblearn`
- `numpy`
---
## Installation
Follow these steps to set up and run the application:
### 1. Clone the Repository
First, clone the repository to your local machine:
```bash
git clone https://github.com/radanpro/data-science.git
```
### 2. Navigate to the Project Directory
Move into the project folder:
```bash
cd data-Science
```
### 3. Set Up a Virtual Environment (Optional but Recommended)
To avoid conflicts with other Python projects, create a virtual environment:
```bash
python -m venv venv
```
Activate the virtual environment:
- On Windows:
```bash
venv\Scripts\activate
```
- On macOS/Linux:
```bash
source venv/bin/activate
```
### 4. Install Required Libraries
Install the required libraries using the `requirements.txt` file:
```bash
pip install -r requirements.txt
```
or
Alternatively, you can install the libraries manually:
```bash
pip install pandas scikit-learn tensorflow matplotlib seaborn imblearn numpy
```
### 5. Run the Application
Once the libraries are installed, run the application:
```bash
streamlit run datap.py
```
---
## Usage
1. **Load Data**
Click on the "Load Data" button to upload your dataset (CSV or Excel).
2. **Process Data**
Select the target column from the dropdown menu.
Click "Start Processing" to clean, encode, and balance the dataset.
3. **Visualize Data**
Select up to 3 features from the list.
Click "Plot Data" to generate visualizations.
4. **Train Model**
Choose a model (Random Forest or Deep Learning).
Click "Train Model" to train the selected model.
5. **Test Model**
Click "Test Model" to evaluate the model's performance.
6. **Make Predictions**
Enter feature values in the prediction tab.
Click "Predict" to see the prediction result.
Click "New Prediction" to clear the input fields and reset the result.
---
## File Structure
Here’s an overview of the project structure:
```
repository-name/
│
├── datap.py # Main application script
├── requirements.txt # List of required libraries
├── README.md # Project documentation
├── LICENSE # License file
├── .gitignore # Files to ignore in Git
├── data/ # Folder for sample datasets (optional)
└── images/ # Folder for screenshots (optional)
```
---
## License
This project is licensed under the MIT License. See the `LICENSE` file for details.
---
## Contributing
Contributions are welcome! If you'd like to contribute, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bugfix.
3. Commit your changes.
4. Push your changes to the branch.
5. Submit a pull request.