<div align="center">
<picture>
<source media="(prefers-color-scheme: dark)" srcset="https://github.com/hamdivazim/pypredictor/raw/main/logo_dark.png">
<img src="https://github.com/hamdivazim/pypredictor/raw/main/logo.png" alt="pypredictor logo">
</picture>
</div>
# pypredictor
<p>
<img src="https://img.shields.io/badge/Python-3.8 | 3.9 | 3.10 | 3.11 -blue.svg" alt="py versions">
<img src="https://img.shields.io/badge/PyPi package-0.1.0-green.svg" alt="pypi version">
<img src="https://img.shields.io/badge/License-Apache License 2.0-green.svg" alt="license">
<img src="https://img.shields.io/badge/Libraries-tensorflow | numpy | seaborn | pandas-green.svg" alt="libs">
</p>
The Python library that makes AI predictions simple.
### View the [GitHub Page](https://github.com/hamdivazim/pypredictor)
## What can it do?
pypredictor uses an RNN (Recurrent Neural Network) to predict the next n numbers in a sequence. As an example, using this code:
```python
pred = NumPredictor(500)
print(pred.predict([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 5, 3))
```
pypredictor generated the following output, which is quite accurate:
```
[10.969043, 11.950292, 12.920968, 13.859894, 14.789316]
```
pypredictor also has the ability to generate a pandas DataFrame and a seaborn line graph, from an initial sequence/DataFrame which you provide.
###
Plus, there are examples in the `examples/` directory, so you can take a look for yourself :)
## How to install
Install via pip:
```
$ pip install pypredictor
```
To get examples, clone this repository and enter `pypredictor/examples`:
```
$ git clone https://github.com/hamdivazim/pypredictor.git
$ cd pypredictor/examples
```
anaconda support hopefully coming soon!
## License
pypredictor is licensed by Hamd Waseem (codingboy_CW) under [the Apache License 2.0](https://github.com/hamdivazim/pypredictor/blob/main/LICENSE).
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