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https://github.com/tonystark-edith/dynamic-learning-technique


https://github.com/tonystark-edith/dynamic-learning-technique

dlt machine-learning

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# Dynamic Learning Technique

Dynamic learning technique allows the user to train a model in batch wise manner

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Exchange Rate Api

```bash
pip install Dynamic-Learning-Technique
```

## Usage

The DLT takes 2 argument with 6 optional arguments

```python
# Initialize an object to DLT
from DLT import *
from sklearn.tree import DecisionTreeRegressor
import asyncio

async def main():
obj = DLT(['X dataset'], ['Y dataset'], DecisionTreeRegressor())
await obj.start()

if __name__ == "__main__":
asyncio.run(main())
```

## Features

- ## **Algorithms Supported**
New supported algorithms has been included
- RandomForestClassifier
- DecisionTreeClassifier
- SVC
- RandomForestRegressor
- DecisionTreeRegressor
- LinearRegression
- LogisticRegression
- SVR
- Ridge
- Lasso

- ## **Exception**
New exceptions has been included
- NoArgumentException
- InvalidMachineLearningModel
- InvalidDatasetProvided
- BatchCountGreaterThanBatchSize

- ## **Parallel Processing**
- The splitting process has been made an asynchronous process in order to increase the speed of the splitting
process

Test cases has been included

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

Please make sure to update tests as appropriate.

## License

[MIT](https://github.com/TONYSTARK-EDITH/Dynamic-Learning-Technique/blob/9cf56eb5b1421e70d895bcf52e4f3c4964987df2/LICENSE)