semvec/pypi-packages huggingface dataset:
kelp-o-matic 0.15.0.dev7
Habitat segmentation with AI
Requires Python: <3.14,>=3.10

<div align="center" style="overflow: hidden; display: flex; justify-content:center; gap:10px;">
<a href="https://github.com/HakaiInstitute/kelp-o-matic/actions/workflows/test.yml">
    <img alt="🧪 Test" src="https://github.com/HakaiInstitute/kelp-o-matic/actions/workflows/test.yml/badge.svg" height="20px" />
</a>

<a href="https://github.com/HakaiInstitute/kelp-o-matic/blob/main/LICENSE">
    <img alt="License" src="https://anaconda.org/conda-forge/kelp-o-matic/badges/license.svg" height="20px" />
</a>

<a href="https://anaconda.org/conda-forge/kelp-o-matic">
    <img alt="Version" src="https://anaconda.org/conda-forge/kelp-o-matic/badges/version.svg" height="20px" />
</a>

<a href="https://pypi.org/project/kelp-o-matic">
    <img alt="PyPI - Version" src="https://img.shields.io/pypi/v/kelp-o-matic" height="20px">
</a>

<a href="https://zenodo.org/badge/latestdoi/462897183">
    <img alt="DOI" src="https://zenodo.org/badge/462897183.svg" height="20px" />
</a>
</div>

<p align="center">
    <img src="./docs/images/kelp_o_matic_smaller.gif" alt="Kelp-O-Matic" />
</p>

<p align="center">
    <i>Kelp Segmentation Tools for Aerial Imagery</i>
</p>

<h1 align="center">
    <a href="https://kelp-o-matic.readthedocs.io">&#9758; Read the Docs &#9756;</a>
</h1>
harlequin-postgres 1.3.0
A Harlequin adapter for Postgres.
Requires Python: >=3.10

# harlequin-postgres

This project provides the Harlequin adapter for Postgres. For more information, see [harlequin.sh](https://harlequin.sh/docs/postgres/index).


## Installation

You must install the `harlequin-postgres` package into the same environment as `harlequin`. The best and easiest way to do this is to use `uv` to install Harlequin with the `postgres` extra:

```bash
uv tool install 'harlequin[postgres]'
```

## Using Harlequin with Postgres

To connect to a Postgres database, run Harlequin with the `-a postgres` option and pass a [Posgres DSN](https://www.postgresql.org/docs/current/libpq-connect.html#LIBPQ-CONNSTRING) as an argument:

```bash
harlequin -a postgres "postgres://my-user:my-pass@localhost:5432/my-database"
```

## Connection Options

You can also pass all or parts of the connection string as separate options. The following is equivalent to the above DSN:

```bash
harlequin -a postgres -h localhost -p 5432 -U my-user --password my-pass -d my-database
```

The supported connection options are:

```
host
port
dbname
user
password
passfile
require_auth
channel_binding
connect_timeout
sslmode
sslcert
sslkey
```

For descriptions of each option, run:

```
harlequin --help
```

## Environment Variables

Harlequin's Postgres driver will load connection information from the standard `PG*` environment variables. Any options supplied at the command-line will override environment variables.


## Manual Transactions

To use Manual transaction mode, click on the label in the Run Query Bar to toggle the transaction mode from Auto to Manual.

## Further Documentation

For more information, see the [Harlequin Docs](https://harlequin.sh/docs/postgres/index).
medilinda-ml 0.1.9
Add your description here
Requires Python: >=3.11

# Medilinda-ML 💊

[![PyPI version](https://badge.fury.io/py/medilinda-ml.svg)](https://badge.fury.io/py/medilinda-ml)
[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
[![Python Version](https://img.shields.io/pypi/pyversions/medilinda-ml)](https://pypi.org/project/medilinda-ml/)

A complete machine learning pipeline to predict the causality of Adverse Drug Reactions (ADRs) from patient and medication data. This package provides tools for data preprocessing, feature engineering, model training, and evaluation, with seamless MLflow integration for experiment tracking.

## Overview

The goal of Medilinda-ML is to provide a reproducible and easy-to-use system for assessing the likelihood that a suspected drug is the cause of an adverse reaction. The pipeline is built with `scikit-learn` and handles common challenges in clinical data, such as missing values and class imbalance (using SMOTE).

## Features

-   **End-to-End Pipeline**: From raw data to a trained model.
-   **Feature Engineering**: Automatically calculates features like patient BMI, drug administration duration, and more.
-   **Class Imbalance Handling**: Uses SMOTE to create a balanced dataset for training.
-   **Hyperparameter Tuning**: Leverages `RandomizedSearchCV` to find the best model configuration.
-   **Experiment Tracking**: Integrated with MLflow to log parameters, metrics, and models.

## Installation

Install Medilinda-ML directly from PyPI:

```bash
pip install medilinda-ml
```

## License

This project is licensed under the MIT License. See the `LICENSE` file for more details.