semvec/pypi-packages huggingface dataset:
polytrend 1.0.9
PolyTrend is a regression tool that fits polynomial curves to noisy data.
Requires Python: >=3.11.8

# PolyTrend

PolyTrend is a Python package aimed at facilitating polynomial trend fitting, visualization, and extrapolation. It offers a comprehensive set of functionalities to analyze and interpret data using polynomial regression techniques. Below, we provide an overview of the package along with additional formatting and explanations relevant for PyPI.

## Introduction

PolyTrend is designed to approximate and plot a polynomial function onto given data, thereby aiding in the analysis of trends and patterns within datasets. Its development contributes to various fields including interpolation, polynomial regression, and approximation theory.

## Key Functionalities

PolyTrend offers the following key functionalities:

- **polyplot()**: This method plots the polynomial fit based on specified degrees of the polynomial and the provided data.
- **polyfind()**: This method calculates the best-fit polynomial function by evaluating different polynomial degrees and selecting the one with the lowest Bayesian Information Criterion (BIC) score.
- **polygraph()**: This method visualizes the polynomial function, the known data points, and any extrapolated data points if provided.

## Usage

Users can utilize PolyTrend to perform the following tasks:

1. **Data Analysis**: Analyze trends and patterns within datasets using polynomial regression techniques.
2. **Visualization**: Visualize polynomial fits alongside original data points to gain insights into the relationship between variables.
3. **Extrapolation**: Extrapolate future values based on the fitted polynomial function, enabling predictive modeling tasks.

## Dependencies

PolyTrend relies on the following libraries for its computations and visualizations:

- NumPy
- pandas
- Matplotlib
- scikit-learn

## Additional Resources

For further details on polynomial regression, refer to [this wiki](https://en.wikipedia.org/wiki/Polynomial_regression).

## Installation

PolyTrend can be installed via pip:

```bash
pip install polytrend
```

## Example Usage

```python
import random
import polytrend as pt

# sample input
degrees = [1, 2, 3]
data = [
    (float(x), float(0.5 * x**2 - 2 * x + 1 + random.uniform(-1000, 1000)))
    for x in range(0, 100)
]

# Fit polynomial and visualize
pt.polyplot(degrees, data, extrapolate_data=[112, 140])
```

## Feedback and Contributions

Feedback and contributions to PolyTrend are welcomed and encouraged. Please feel free to submit any issues or pull requests via the [GitHub repository](https://github.com/asiimwemmanuel/polytrend).

## License

PolyTrend is licensed under the GNU GPL License. See the [LICENSE](./LICENSE) file for details.