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https://github.com/nurulashraf/rnn-sequence-number-prediction

This analysis demonstrates building and training a simple RNN model to predict the next number in a sequence, covering data preparation, model architecture, training, and prediction.
https://github.com/nurulashraf/rnn-sequence-number-prediction

data-science deep-learning keras machine-learning python recurrent-neural-networks rnn sequence-prediction tensorflow time-series

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This analysis demonstrates building and training a simple RNN model to predict the next number in a sequence, covering data preparation, model architecture, training, and prediction.

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# Predicting Sequence Number using Recurrent Neural Network (RNN)

A simple RNN model that predicts the next number in a sequence. This project demonstrates data preparation, model architecture, training, and inference using Python, TensorFlow/Keras.

## Project Structure

- `notebooks/` – Jupyter Notebooks for data analysis, model training and evaluation
- `requirements.txt` – Python libraries needed to run the project
- `LICENSE` – MIT Licence for this project
- `README.md` – this file with overview and instructions

---

## Features

- Generates sequential numeric data for training and validation
- Builds a Recurrent Neural Network using TensorFlow/Keras
- Trains the model to predict the next number in a sequence
- Supports sequence inference post-training

## Tools & Libraries

- **Python 3.x**
- **TensorFlow / Keras** – for building and training the RNN
- **NumPy**, **pandas** (optional) – for data manipulation

## How to Use

1. **Clone the repository**
```bash
git clone https://github.com/nurulashraf/rnn-sequence-number-prediction.git
cd rnn-sequence-number-prediction
```

2. **Install dependencies**

```bash
pip install -r requirements.txt
```

3. **Open the notebook**

```bash
jupyter notebook rnn-sequence-number-prediction.ipynb
```

---

## License

This project is licensed under the MIT License — see [LICENSE](LICENSE) for details.