# Multimodal Alzheimer’s Disease Classification through Deep Ensemble Random Vector Functional Link Networks


## Overview
This repository contains the code and notebooks for the RVFL project, which aims to implement and analyze Random Vector Functional Link (RVFL) networks within different stages of data processing and machine learning. The project is structured into four main Jupyter notebooks detailing specific aspects of the process.

## Files Description
- `RVFL_PROJECT_preprocessing.ipynb`: Contains all preprocessing steps required to prepare the data for analysis and model training.
- `RVFL_PROJECT_EDA.ipynb`: Provides an exploratory data analysis of the dataset to uncover trends, patterns, and anomalies.
- `RVFL_PROJECT_ML.ipynb`: Includes machine learning models that use preprocessed data to predict outcomes based on the RVFL network.
- `RVFL_PROJECT_RVFL.ipynb`: Specific implementation of the Random Vector Functional Link network, detailing the architecture, training, and evaluation.

## Installation

To run these notebooks, you will need Python 3 and the following libraries:
- NumPy
- Pandas
- Matplotlib
- Scikit-learn

You can install these with pip:
```bash
pip install numpy pandas matplotlib scikit-learn
```

## Usage

1. Clone the repository to your local machine.
2. Ensure you have Jupyter Notebook installed, or use Google Colab to open the `.ipynb` files.
3. Start with the `RVFL_PROJECT_preprocessing.ipynb` to preprocess the data.
4. Proceed to `RVFL_PROJECT_EDA.ipynb` for exploratory data analysis.
5. Use the `RVFL_PROJECT_ML.ipynb` to run machine learning models.
6. Explore the RVFL implementation in `RVFL_PROJECT_RVFL.ipynb`.