semvec/pypi-packages huggingface dataset:
ccxtools 0.0.140
ccxtools
Requires Python: >=3.6

# ccxtools

CryptoCurrency eXchange TOOLS

암호화폐 자동매매 관련 라이브러리인 CCXT를 기반으로 만들어진 암호화폐 자동매매용 Python 3 라이브러리입니다.

## Install

[ccxtools in PyPI](https://pypi.org/project/ccxtools/)

```
pip install ccxtools
```

## Features

ccxtools는 ccxt에서 함수로 구현되지 않은 API와 저 개인적으로 매매 프로그램을 개발할 때 필요한 함수들을 구현한 라이브러리이기 때문에 암호화폐 매매에 필요한 모든 API를 담고있지는 않습니다. 기본적인 API는 [ccxt](https://github.com/ccxt/ccxt)를 참조하시기 바랍니다.
gsearchpy 0.1.6
A simple Python package for performing Google searches
Requires Python: >=3.6

## 📦 gsearchpy 
**gsearchpy** is a lightweight Python package that allows you to perform Google Search queries programmatically and retrieve raw search result pages with ease.

It is built for developers, researchers, and automation enthusiasts who need a flexible interface to Google Search.

---

## Table of Contents

- [Features](#-features)
- [Installation](#-installation)
- [Example Usage](#-example-usage)


## 🚀 Features

- 🔍 Perform Google searches using customizable parameters  
- 📄 Retrieve raw HTML content and filtered data of Google search results  
- 🔁 Built-in pagination support  
---


## 📦 Installation

```
pip install gsearchpy
```

### Using Github
```
pip install git+https://@github.com/itsguptaaman/gsearchpy.git
```

### After Installation run this command to setup the drivers and dependency
```
gsearchpy
```


## 📦 Example Usage

### For raw data response 
```python
from gsearchpy import GoogleScraper

scraper = GoogleScraper()
html = scraper.google_search(query)
```

### For clean data
```python
from gsearchpy import GoogleScraper

query = "best VSCode extensions for productivity"
scraper = GoogleScraper()
html = scraper.google_search(query)
print(scraper.google_search_clean_data(html))
```

### To get google maps data or any other data use tbm paramter
```python
from gsearchpy import GoogleScraper

google = GoogleScraper()
query = "coffee shop in dubai"
html = google.google_search(query, tbm="lcl")
print(google.local_search_clean_data(html))
```

### Also you can use v2 except for google search you can use this for lcl, images, news, and etc.
```python
from gsearchpy import GoogleScraper

google = GoogleScraper()
query = "coffee shop in dubai"
html = google.google_search_v2("plumbers", page_number=2, gl="ahemdabad")
print(google.local_search_clean_data(html))
```
MLTuneX 0.1.7
A package for machine learning tuning and optimization.
Requires Python: >=3.8

# 🤖 MLTuneX - AutoML Framework for Model Training and Hyperparameter Tuning

**MLTuneX** is a powerful and extensible AutoML library designed to make machine learning model training and hyperparameter tuning easy, customizable, and scalable.

🚀 With support for preprocessed data (currently), the library can:
- Train multiple models
- Evaluate their performance
- Tune top models using **Optuna** and **OpenAI GPT-based guidance**
- Save the best-performing model

⚙️ Currently supports:
- **Model Library**: `scikit-learn`
- **Tuning Framework**: `Optuna`

🧪 Upcoming support:
- Grid Search
- Random Search
- Ray Tune
- OpenAI-based advanced tuning agents

---

🤖 **Supported LLMs for Tuning**

MLTuneX uses large language models to guide tuning strategies. You can specify the provider and model using the `model_provider_model_name` argument:

✅ **OpenAI:**
- `OpenAI:gpt-4o`

✅ **Groq:**
- `Groq:deepseek-r1-distill-llama-70b`
- `Groq:qwen/qwen3-32b`

ℹ️ Additional model support will be added in future updates. Contributions are welcome!

---

> ⚠️ **NOTE:** As of now, only preprocessed data is supported. You must provide a dataset that is already cleaned and encoded. Automated raw data handling is planned in upcoming versions.

---

## 📦 Installation

Install the package directly using pip:

```bash
pip install --no-cache-dir MLTuneX
```

```bash
export OPENAI_API_KEY="your-openai-api-key-here"
export GROQ_API_KEY="your-groq-api-key"
```

```python
from mltunex.main import MLTuneX

mltunex = MLTuneX(
    data="/path/to/your/preprocessed_data.csv",  # Must be a cleaned CSV or pandas DataFrame
    target_column="your_target_column",          # Specify the target column
    task_type="regression",                      # Choose between "regression" or "classification"
    model_provider_model_name = "OpenAI:gpt-4o"
)

mltunex.run(
    result_csv_path="/path/to/save/csv",         # Directory to store evaluation results
    model_dir_path="/path/to/save/models",       # Directory to save the best model
    tune_models="yes"                            # "yes" to enable hyperparameter tuning
)
```