semvec/pypi-packages huggingface dataset:
stemfard 0.0.2
A Python library for mathematical and Statistical Computing in Science, Technology, Engineering and Mathematics (STEM).
License: MIT
Requires Python: >=3.9
Keywords: linear algebra

## stemfard

A Python library for mathematical and Statistical Computing in Science, Technology, Engineering and Mathematics (STEM).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install the stemfard library.

```bash
pip install stemfard
```

## Usage

The library is imported into a Python session by running the following import statement.

```python
>>> import stemfard as stm
```

## Support

For any support on any of the functions in this library, send us an email at: ```stemfard@stemfard.org```. We are willing to offer the necessary support where we possibly can.

## Roadmap

Future releases aim to make ```stemfard``` a first choice library for students, trainers and professionals in Science, Technology, Engineering and Mathematics (STEM).

## Contributing

To make stemfard a successful library while keeping the code easy, we welcome any valuable contributions towards the development and improvement of this library. 

For major changes to the library, please open an issue with us first to discuss what you would like to change and we will be more than willing to make the changes.

## Authors and acknowledgement

We are grateful to the incredible support from our developers at ```STEM Research```.

## License

[MIT](https://choosealicense.com/licenses/mit/)
tabulaml 0.1.2
TabuLaML - Fast, explainable, no-code AutoML for tabular data
License: MIT
Requires Python: >=3.9
Keywords: machine-learning, automl, tabular, scikit-learn, xgboost, lightgbm, cli, no-code

# TabuLaML

**Fast, explainable, no-code AutoML for tabular data.**

TabuLaML transforms raw CSV data into trained, deployable machine learning models with zero configuration. Just point it at your data and go.

## Installation

```bash
pip install tabulaml
```

With XGBoost and LightGBM support:
```bash
pip install tabulaml[all]
```

## Quick Start

```bash
tabulaml train data.csv --target target_column
```

Or use in Python:
```python
from automl import AutoMLPipeline

pipeline = AutoMLPipeline()
results = pipeline.run("data.csv", target_column="target")
```

## What It Does

- **Automatic Data Profiling** - Detects column types, missing values, cardinality, and class imbalance
- **Task Inference** - Automatically determines if your problem is binary classification, multiclass classification, or regression
- **Smart Preprocessing** - Handles missing values, scales numerical features, encodes categorical variables
- **Class Balancing** - Addresses imbalanced datasets using SMOTE, ADASYN, and other resampling techniques
- **Multi-Model Training** - Trains and compares Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and LightGBM
- **Cross-Validation** - Selects the best model using rigorous cross-validation scoring
- **Export Ready** - Outputs trained models (.pkl), detailed reports (HTML/JSON), confusion matrices, and feature importance charts

## Output

After training, you'll find in the `outputs/` folder:

```
outputs/
  models/      - Trained model (.pkl)
  reports/     - HTML and JSON reports
  artifacts/   - Confusion matrix, feature importance charts
```

## Use Cases

| Context | What It Optimizes |
|---------|-------------------|
| Fraud Detection | Minimize missed fraud (high recall) |
| Churn Prediction | Catch at-risk customers before they leave |
| Medical Diagnosis | Reduce false negatives for critical conditions |
| Spam Detection | Minimize false positives to protect legitimate emails |
| Loan Approval | Balance risk assessment with approval rates |
| Marketing Campaigns | Target likely responders efficiently |
| Quality Control | Catch defective products before shipping |

## Key Features

- **Zero Configuration** - Point it at a CSV and go
- **Progress Bar** - Clean, user-friendly progress display
- **Explainable Results** - Understand why the model makes predictions
- **Reproducible** - Set random seeds for consistent results
- **Production Ready** - Export models ready for deployment

## Requirements

- Python 3.9+
- Works on Windows, macOS, and Linux

## License

MIT License - Copyright (c) 2025 Sekpey Herbert