https://github.com/sshbuilder/randomnumberprediction
Random Number Predictor is a Python project leveraging online learning techniques to continuously enhance a linear regression model. It predicts the next number in a random sequence, adapting and accumulating knowledge over multiple runs. Explore and experiment with this dynamic machine learning showcase. Contributions are welcome!
https://github.com/sshbuilder/randomnumberprediction
Last synced: 3 months ago
JSON representation
Random Number Predictor is a Python project leveraging online learning techniques to continuously enhance a linear regression model. It predicts the next number in a random sequence, adapting and accumulating knowledge over multiple runs. Explore and experiment with this dynamic machine learning showcase. Contributions are welcome!
- Host: GitHub
- URL: https://github.com/sshbuilder/randomnumberprediction
- Owner: sshBuilder
- License: mit
- Created: 2023-12-27T16:32:02.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2023-12-28T05:39:16.000Z (over 1 year ago)
- Last Synced: 2025-02-26T19:48:56.082Z (3 months ago)
- Language: Python
- Size: 6.84 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Random Number Prediction Model
## Overview
The Random Number Predictor is a Python project that utilizes machine learning to predict the next number in a sequence generated by a random process. This project is designed to showcase the implementation of a simple online learning approach, where the model is continuously updated with new datasets, retaining and improving its knowledge over time.
## Features
- **Linear Regression Model:** The project employs a basic linear regression model for simplicity. This model is updated with both historical and new datasets to make predictions about the next number in the sequence.
- **Online Learning:** The script supports online learning, allowing the model to accumulate knowledge from past datasets and incorporate new information each time the code is run.
- **Random Number Generation:** Actual and predicted next numbers are displayed, providing insights into the model's performance.
## How to Use
1. **Clone the Repository:**
```
git clone https://github.com/yourusername/random-number-predictor.git
cd random-number-predictor
```
2. **Run the Script:**
```
python randomPredictor.py
```
3. **Explore and Experiment:**
- Observe actual and predicted next numbers before and after each update.
- Experiment with different datasets and explore the model's adaptability over time.
## Contributions
Contributions are welcome! Whether you want to improve the existing model, explore different machine learning algorithms, or add new features, feel free to fork the repository and submit a pull request.
## License
This project is licensed under the [MIT License](LICENSE.md).
## Acknowledgments
This project was inspired by the curiosity to explore the dynamics of machine learning in predicting seemingly random sequences. Special thanks to the open-source community for providing valuable resources and tools.
Happy predicting!