semvec/pypi-packages huggingface dataset:
kellyml 0.1.5
Lightweight ML helpers for preprocessing and evaluation
Requires Python: <3.13,>=3.10

# MLToolkit

A lightweight ML helper package for preprocessing, splitting, evaluation, and simple model wrappers.  

---

## Features
- Preprocessing: scaling, encoding, outlier handling, SMOTE
- Dataset splitting: load and save dataset, train/validation/test with random, time-based, target and feature split
- Evaluation: classification, regression, clustering, anomaly
- Models: simple wrappers for scikit-learn (KNN, regression, clustering)

---

## Installation
#1. Clone the repository
git clone https://github.com/kellykoty/kellyml.git
cd kellyml

#2. Install dependencies and activate the virtual environment
poetry install
poetry shell
protoform 0.0.2
shaping data
Requires Python: <3.13,>=3.10

# protoform

[![Release](https://img.shields.io/github/v/release/dataxight/protoform)](https://img.shields.io/github/v/release/dataxight/protoform)
[![Build status](https://img.shields.io/github/actions/workflow/status/dataxight/protoform/main.yml?branch=main)](https://github.com/dataxight/protoform/actions/workflows/main.yml?query=branch%3Amain)
[![codecov](https://codecov.io/gh/dataxight/protoform/branch/main/graph/badge.svg)](https://codecov.io/gh/dataxight/protoform)
[![Commit activity](https://img.shields.io/github/commit-activity/m/dataxight/protoform)](https://img.shields.io/github/commit-activity/m/dataxight/protoform)
[![License](https://img.shields.io/github/license/dataxight/protoform)](https://img.shields.io/github/license/dataxight/protoform)

shaping data

- **Github repository**: <https://github.com/dataxight/protoform/>
- **Documentation** <https://dataxight.github.io/protoform/>

## Getting started with your project

## Concept of protoform

protoform is a data operation management system implemented in python using a graph and network (python-igraph) to handle the manipulation of data structure. protoform leverages tasks and performed operations to the data graph by these steps.
- Define the source of data (for instance from sql database, csv file).
- Define the tasks.
- Convert the source of data and tasks to a graph (data graph and task graph) 
- Execute protoform.
protoform will digest the tasks and independently operate in parallel (unless you define dependencies) then apply those tasks to the raw data, and export new data.

## Working modules

- `cli` : captures the arguments of task and data paths and executes operations regarding the task plans.
- `io` : converts tasks to task graphs, reads input data (csv, tsv, parquet, and sql), converts the data to data graph, exports the objects to graph, csv file, or pushes to sql database.
- `operator` : contains a list of operations what to do to the data.
- `data` : implements data operation classes.
- `graph` : implements graph classs.

## Installation

- **Installation guide** <https://github.com/dataxight/protoform/wiki/Installation>

## Execution
- You need to prepare two files:
> 1. task manifest file (such as task.json). Follow an instruction how to but a task graph here: <https://github.com/dataxight/protoform/wiki/Execution-Graph-Specification>
> 2. data manifest file (such as data.json). Follow an instruction how to but a data graph here: <https://github.com/dataxight/protoform/wiki/Data-Graph-Specification>
- Run the first command

```bash
protoform --task-plan task.json --data-graph data.json
```

---

Repository initiated with [fpgmaas/cookiecutter-poetry](https://github.com/fpgmaas/cookiecutter-poetry).
pymultifit 1.0.9
A library to fit data with multiple fitters.
Requires Python: >=3.9
Keywords: fitting, multi-modal, gaussian, lognormal, skew, curve fitting, scipy

# `pyMultiFit`

- [`pyMultiFit`](#pymultifit)
    - [What is `pymultifit`](#what-is-pymultifit)
    - [How to install](#how-to-install)
    - [Documentation](#documentation)

A python multi-fit library for fitting the data with multiple `X` fitters.

![GitHub Release](https://img.shields.io/github/v/release/syedalimohsinbukhari/pymultifit?color=magenta)
![PyPI - Python Version](https://img.shields.io/pypi/pyversions/pymultifit?)
![GitHub-licence](https://img.shields.io/github/license/syedalimohsinbukhari/pymultifit?color=darkblue)
![GiHub-CodeCoverage](https://img.shields.io/codecov/c/github/syedalimohsinbukhari/pymultifit?color=cyan)
[![Project Status](https://www.repostatus.org/badges/latest/active.svg)](https://www.repostatus.org/#active)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.17273507.svg)](https://doi.org/10.5281/zenodo.17273507)
[![pyOpenSci Peer-Reviewed](https://pyopensci.org/badges/peer-reviewed.svg)](https://github.com/pyOpenSci/software-submission/issues/233)
[![status](https://joss.theoj.org/papers/739ef75550f7c096016acf808e796277/status.svg)](https://joss.theoj.org/papers/739ef75550f7c096016acf808e796277)
![GitHub top language](https://img.shields.io/github/languages/top/syedalimohsinbukhari/pymultifit?color=lightgreen)
![GitHub contributors](https://img.shields.io/github/contributors/syedalimohsinbukhari/pymultifit?color=gold)
![Github Issues](https://img.shields.io/github/issues/syedalimohsinbukhari/pymultifit?color=orange)
![GitHub OPEN PRs](https://img.shields.io/github/issues-pr/syedalimohsinbukhari/pymultifit?color=darkred)
![GitHub CLOSED PRs](https://img.shields.io/github/issues-pr-closed/syedalimohsinbukhari/pymultifit?color=darkgreen)

## What is `pymultifit`

`pymultifit` is a library made specifically to tackle one problem, **fit the data with multiple fitters**.

Fitter implementations include,

- `Gaussian` fitter,
- `SkewedNormal` fitter,
- `LogNormal` fitter,
- `Exponential` fitter,
- `Laplace` fitter, and more.

Additionally, it provides capabilities to generated n-modal data as well through its `generators` module.
Along with this, the user can also generate probability distribution data using `distributions` module.

## How to install

Using pip: `pip install pymultifit`

## Documentation

The documentation can be found on [readthedocs](https://pymultifit.readthedocs.io/latest/)
tracebackSilencer 0.1.0
Silence the anyoing tracebacks
Requires Python: >=3.0

# tracebackSilencer
## A python package for silencing the anyoing tracebacks
### **Here is a coding example:**

```py

from Packages import tracebackSilencer

ts = tracebackSilencer.Silencer()
ts.activate(__file__)
sfdihjur
print("Hello, World")
```
**The Output Will Be:**
```
/home/th3ou1d3x/Hehe/.venv/bin/python /home/th3ou1d3x/Hehe/Haha.py 
Using Traceback Silencer
By: Anthony R. + Avash K.
Hello, World

Process finished with exit code 1
```
*You Can Also Optionaly Use ts.debug(True) before activation in that code that was given to get debug prints and a debug report*
#### This is very useful but because it uses its own code parser at the current time functions , variables and classes may have some trouble
populationsim 0.10.0
Population Synthesis (for ActivitySim)
Requires Python: <3.13,>=3.9

PopulationSim
=============

[![Build Status](https://travis-ci.org/activitysim/populationsim.svg?branch=master)](https://travis-ci.org/ActivitySim/populationsim) [![Coverage Status](https://coveralls.io/repos/ActivitySim/populationsim/badge.png?branch=master)](https://coveralls.io/r/ActivitySim/populationsim?branch=master)<a href="https://medium.com/zephyrfoundation/populationsim-the-synthetic-commons-670e17383048"><img src="https://github.com/ZephyrTransport/zephyr-website/blob/gh-pages/img/badging/project_pages/populationsim/PopulationSim.png" width="72.6" height="19.8"></a>


PopulationSim is an open platform for population synthesis.  It emerged
from Oregon DOT's desire to build a shared, open, platform that could be
easily adapted for statewide, regional, and urban transportation planning
needs.  PopulationSim is implemented in the
[ActivitySim](https://github.com/activitysim/activitysim) framework.

## Command-Line Interface

PopulationSim can be run directly from the command line:

```bash
populationsim -c /path/to/configs -d /path/to/data -o /path/to/output
```

See the [examples directory](examples/) for more information on using the command-line interface.

## Documentation

https://activitysim.github.io/populationsim/