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https://github.com/ayodimeji1/ai_classification-nn
https://github.com/ayodimeji1/ai_classification-nn
data-analysis feature-engineering jupyter-notebook keras machine-learning neural-networks python tensorflow
Last synced: 2 days ago
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- Host: GitHub
- URL: https://github.com/ayodimeji1/ai_classification-nn
- Owner: Ayodimeji1
- Created: 2024-10-28T20:17:00.000Z (19 days ago)
- Default Branch: main
- Last Pushed: 2024-11-04T04:50:39.000Z (12 days ago)
- Last Synced: 2024-11-14T00:37:24.955Z (2 days ago)
- Topics: data-analysis, feature-engineering, jupyter-notebook, keras, machine-learning, neural-networks, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 908 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# ML Classification - Neural Networks Project
## Overview
This project focuses on implementing classification tasks using neural networks. It is designed to provide an in-depth understanding of how to build, train, and evaluate neural network models for classification problems. The project is presented in a Jupyter Notebook format, which makes it interactive and suitable for demonstration and educational purposes.
## Table of Contents
- [Features](#features)
- [Project Structure](#project-structure)
- [Installation](#installation)
- [Usage](#usage)
- [Dependencies](#dependencies)
- [Configuration](#configuration)
- [Project Details](#project-details)
- [License](#license)
## Features
- **Neural Network Architecture**: Implementation of a neural network for classification.
- **Data Preprocessing**: Methods to clean and prepare data for training.
- **Training and Evaluation**: Includes training the model, validation, and performance metrics.
- **Interactive Notebook**: Step-by-step code explanations and outputs.
- **Visualization**: Visualizes training metrics and model performance.
## Project Structure
```
ML_Classification-NN-main/
│
├── Task_3_Classification_Neural_Networks.ipynb # Jupyter Notebook with neural network implementation
└── README.md # Project documentation
```
## Installation
### Prerequisites
- **Python 3.8+**
- **Jupyter Notebook** or **Jupyter Lab**
### Setup
1. **Clone the repository**:
```
git clone https://github.com/Ayodimeji1/ML_Classification-NN.git
cd ML_Classification-NN-main
```
3. **Install the required packages**:
```
pip install numpy pandas matplotlib scikit-learn tensorflow
```
## Usage
1. **Launch Jupyter Notebook**:
```
jupyter notebook
```
2. **Open `Classification_Neural_Networks.ipynb`** in the Jupyter interface and execute the cells step-by-step to explore the code and outputs.
## Dependencies
- **NumPy**: For numerical operations
- **Pandas**: For data manipulation
- **Matplotlib/Seaborn**: For visualizations
- **Scikit-learn**: For data splitting and evaluation
- **TensorFlow/Keras or PyTorch**: For building and training the neural network
- **Jupyter Notebook**: For interactive coding environment
## Configuration
- **Data File**: Ensure any dataset required is available and properly referenced in the notebook.
## Project Details
The notebook walks through the process of:
- **Data Loading and Preprocessing**: Cleaning and preparing the dataset.
- **Model Building**: Creating a neural network architecture tailored for classification.
- **Training**: Configuring training loops, defining loss functions, and using optimizers.
- **Evaluation**: Assessing model performance using metrics such as accuracy, precision, and recall.
- **Visualization**: Plotting learning curves and evaluation metrics for better insight.
## License
This project is licensed under the MIT License.