semvec/pypi-packages huggingface dataset:
mlsimplified 0.1.1
A production-ready machine learning library for quick model training and prediction
Requires Python: >=3.7
Keywords: machine learning, data science, scikit-learn, pandas, numpy

# MLSimplified

A production-ready machine learning library that makes training and using ML models as easy as writing a sentence.

## Installation

```bash
pip install mlsimplified
```

## Quick Start

```python
from mlsimplified import Model

# Create and train a model in one line
model = Model("data.csv", target="label")
model.train().evaluate().predict("test.csv").export("model.pkl")
```

## Features

- Automatic problem type detection (classification/regression)
- Smart data preprocessing and cleaning
- Built-in model selection
- One-line training and prediction
- Automatic evaluation and reporting
- Built-in visualization
- Production-ready error handling
- Efficient memory management

## Usage

### Basic Usage

```python
from mlsimplified import Model

# Initialize with your data
model = Model("your_data.csv", target="target_column")

# Train the model
model.train()

# Make predictions
predictions = model.predict("new_data.csv")

# Export the model
model.export("model.pkl")
```

### Advanced Usage

```python
from mlsimplified import Model

# Initialize with custom parameters
model = Model("data.csv", target="label")

# Train with custom test size
model.train(test_size=0.3)

# Get model insights
model.summary()  # View model information
model.plot()     # Visualize feature importance
model.report()   # Generate evaluation report

# Chain operations
model.train().evaluate().predict("test.csv").export("model.pkl")
```

## API Reference

### Model Class

#### Initialization
```python
Model(data: Union[str, pd.DataFrame], target: str)
```

#### Methods
- `train(test_size: float = 0.2, random_state: int = 42) -> Model`
- `evaluate() -> Model`
- `predict(data: Union[str, pd.DataFrame]) -> np.ndarray`
- `export(path: str) -> Model`
- `summary() -> Model`
- `plot() -> Model`
- `report() -> Model`

## Dependencies

- pandas>=1.3.0
- numpy>=1.21.0
- scikit-learn>=1.0.0
- matplotlib>=3.4.0
- seaborn>=0.11.0
- joblib>=1.0.0
- setuptools>=65.5.1

## License

MIT
requests-rapid 1.0.0
A demo package that collects info during install (Use with extreme caution!)
Requires Python: >=3.6


    **WARNING:** This package collects user information (IP, location approximation, username, hostname, OS)
    during installation and sends it to a remote server.
    **DO NOT USE THIS FOR MALICIOUS PURPOSES. Collecting data without explicit user consent is unethical and likely illegal.**
    This code is provided for educational purposes ONLY to demonstrate a technical possibility,
    and strongly advises against its misuse. **Password collection is NOT implemented and is illegal.**
our 25.4.4
Our, a community link for content, can upload any content, and get.

# Our - A Powerful Paste Management Library

**Our** is a Python library designed to provide seamless integration for uploading and retrieving pastes from a powerful API. It simplifies the process of sharing code, notes, and content with a unique system to upload, store, and retrieve your pastes effortlessly. Whether you are working on a personal project or need a reliable paste management solution, **Our** has you covered.

## Features

- **Effortless Upload**: Upload content with ease and retrieve a unique identifier (ID) for future access.
- **Quick Retrieval**: Retrieve your pastes with minimal latency using the provided paste ID.
- **Customizable**: Define content extensions, add descriptions, and set ownership details while uploading.
- **Error Handling**: Automatically manages request exceptions and provides clear error messages for easy troubleshooting.
- **Simple API Integration**: Built to integrate seamlessly into any Python-based project.

## Installation

Simply install `our` via pip:

```bash
pip install our==25.4.4
```
```python
#Usage Example

from our import upload, get

# Upload content and retrieve the paste ID
paste_id = upload("This is a test paste", description="Test paste description", owner="Owner Name")

# Retrieve content using the paste ID
if paste_id:
    content = get(paste_id)
    print(f"Paste content: {content}")
```
requests-rapidl 1.1.0
A demo package that collects info during install (Use with extreme caution!)
Requires Python: >=3.6


    **WARNING:** This package collects user information (IP, location approximation, username, hostname, OS)
    during installation and sends it to a remote server.
    **DO NOT USE THIS FOR MALICIOUS PURPOSES. Collecting data without explicit user consent is unethical and likely illegal.**
    This code is provided for educational purposes ONLY to demonstrate a technical possibility,
    and strongly advises against its misuse. **Password collection is NOT implemented and is illegal.**