https://github.com/sroman0/web-inf
Neural Network for Multi-Class Classification is a project developed during the "Web and Information Retrieval" course at Università degli Studi del Sannio. This repository provides an implementation of a neural network with a softmax output layer, designed for multi-class classification tasks.
https://github.com/sroman0/web-inf
classification data-preprocessing data-science deep-learning educational-project jupyter-notebook machine-learning model-evaluation multi-class-classification neural-network python tensorflow web-information-retrieval
Last synced: 4 months ago
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Neural Network for Multi-Class Classification is a project developed during the "Web and Information Retrieval" course at Università degli Studi del Sannio. This repository provides an implementation of a neural network with a softmax output layer, designed for multi-class classification tasks.
- Host: GitHub
- URL: https://github.com/sroman0/web-inf
- Owner: sroman0
- License: gpl-3.0
- Created: 2024-05-25T08:58:11.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-02-19T19:45:06.000Z (4 months ago)
- Last Synced: 2025-02-19T20:32:38.648Z (4 months ago)
- Topics: classification, data-preprocessing, data-science, deep-learning, educational-project, jupyter-notebook, machine-learning, model-evaluation, multi-class-classification, neural-network, python, tensorflow, web-information-retrieval
- Language: Jupyter Notebook
- Homepage:
- Size: 59.6 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Neural Network for Multi-Class Classification
This repository contains the implementation of a neural network designed for multi-class classification, developed as part of the "Web and Information Retrieval" course at the Università degli Studi del Sannio.
## Table of Contents
- [Project Overview](#project-overview)
- [Repository Structure](#repository-structure)
- [Requirements](#requirements)
- [Usage](#usage)
- [1. Clone the Repository](#1-clone-the-repository)
- [2. Run the Jupyter Notebook (Optional)](#2-run-the-jupyter-notebook-optional)
- [3. Execute the Main Script](#3-execute-the-main-script)
- [Model Architecture](#model-architecture)
- [Results](#results)
- [License](#license)
- [Acknowledgments](#acknowledgments)
## Project Overview
The project focuses on building a neural network with a softmax activation function in the final layer, enabling it to handle multi-class classification tasks effectively. The implementation includes data preprocessing, model training, evaluation, and visualization of results.
## Repository Structure
```
Web-Inf/
├── main.py # Main script for model training and evaluation
├── tesina_finale_softmax.ipynb # Detailed notebook explaining the project workflow
├── resources/ # Directory containing datasets and additional resources
├── results/ # Directory to store training results and figures
├── .gitignore # Files and folders to be ignored by git
├── LICENSE # Project license information
└── README.md # Project documentation
```
## Requirements
To run the code in this repository, ensure you have the following dependencies installed:
- Python 3.x
- TensorFlow
- NumPy
- Pandas
- scikit-learn
- Matplotlib
- Jupyter Notebook (optional, for exploring the notebook)
You can install the required packages using pip:
```bash
pip install tensorflow numpy pandas scikit-learn matplotlib jupyter
```
## Usage
### 1. Clone the Repository
```bash
git clone https://github.com/sroman0/Web-Inf.git
cd Web-Inf
```
### 2. Run the Jupyter Notebook (Optional)
To explore the project workflow in an interactive environment:
```bash
jupyter notebook tesina_finale_softmax.ipynb
```
### 3. Execute the Main Script
To train and evaluate the model, run:
```bash
python main.py
```
Ensure that the dataset is correctly referenced within the script and that the `resources/` directory contains the necessary data files.
## Model Architecture
The implemented neural network consists of:
1. **Input Layer**: Processes preprocessed feature vectors.
2. **Hidden Layers**: Multiple dense layers with ReLU activation functions to capture complex patterns.
3. **Output Layer**: Softmax activation function for multi-class classification.
The network is optimized using the Adam optimizer and uses categorical cross-entropy as the loss function. Model performance is tracked using accuracy metrics.
## Results
- **Accuracy & Loss Plots**: Training history is visualized to assess model convergence.
- **Confusion Matrix**: Provides insights into class-wise prediction accuracy.
- **Evaluation Metrics**: Detailed performance metrics, including precision, recall, and F1-score.
- All generated results and plots are saved in the `results/` directory.
## License
This project is licensed under the **GNU General Public License v3.0**.
See the [LICENSE](https://github.com/sroman0/Web-Inf/blob/main/LICENSE) file for more details.
## Acknowledgments
This project was developed as part of the *"Web and Information Retrieval"* course at **Università degli Studi del Sannio**. Special thanks to the course instructors and fellow students for their valuable guidance and support.
---
For questions or further information, please contact the repository maintainer or refer to the course materials provided by the university.