semvec/pypi-packages huggingface dataset:
predictive-modeling-auto 0.2.1
A package for automating predictive modeling tasks using Python
Requires Python: >=3.6

# Predictive Modeling Auto

Welcome to the Predictive Modeling Auto repository. This project aims to automate predictive modeling tasks using Python and its robust data science libraries.

## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)

## Introduction
Predictive Modeling Auto is a Python project designed to streamline the process of building and evaluating predictive models. It leverages popular libraries such as Pandas, NumPy, Scikit-learn, and PyCaret to automate various steps in the modeling pipeline.

## Features
- **Data Preprocessing:** Automated handling of missing values, feature scaling, and encoding.
- **Model Training:** Automated model selection, training, and hyperparameter tuning.
- **Model Evaluation:** Comprehensive evaluation metrics and visualizations.
- **User-Friendly Interface:** Simple and intuitive user interface for ease of use.

## Installation
To clone and run this project, you'll need Python installed on your system. You can clone the repository and install the dependencies using the following commands:

```bash
# Clone the repository
git clone https://github.com/ashishs1407/predictive_modeling_auto.git

# Navigate to the project directory
cd predictive_modeling_auto

# Install the required packages
pip install -r requirements.txt
```
## Usage
Here is a simple example of how to use this project:
```Python
from predictive_modeling_auto import ModelPipeline

# Initialize the pipeline
pipeline = ModelPipeline()

# Load your dataset
data = pipeline.load_data('path/to/your/dataset.csv')

# Preprocess the data
data = pipeline.preprocess_data(data)

# Train the model
model = pipeline.train_model(data)

# Evaluate the model
pipeline.evaluate_model(model, data)
For more detailed usage instructions, refer to the documentation in the docs directory.
```


## Contributing
Contributions are welcome! Please read the CONTRIBUTING.md file for guidelines on how to contribute to this project.

## License
This project is licensed under the MIT License. See the LICENSE file for more details.

## Contact
If you have any questions or suggestions, feel free to reach out:

- **Author:** Ashish Shimpi
- **Email:** a.shimpi93@gmail.com
- **GitHub:** ashishs1407
licere 20250102
Generate licenses from templates.
Requires Python: >=3.9

# Licere

Generate License files using variables and templates.

- Because copying and pasting is hard.
  - ... Even huge companies fail to enter the details right (e.g. Year/Copyright Owner).

- Because some people need licenses. 
  - ... The entire attorney industrial complex.

- Because licensing should be customizable.
  - ... There is no one size fits all.

# Features

- Jinja Templating
- Template Packages
- Custom Licenses
- Render multiple output types (Text/Markdown/HTML)

# Quick Start

1. Install:

  ```shell
  pip install licere
  ```

2. Generate a license:

  ```
  # generate a polyform strict license
  licere generate --license polyform-strict
  
  # generate an apache license with a proper copyright
  licere generate --license apache copyright_year=2024 copyright_owner="Example Company"
  
  # load data from a file
  licere generate --license apache --data=data.toml
  
  # where data.toml is:
  # copyright_year = "2024" 
  # copyright_owner = "Example Company"
  ```

# Included Licenses

- Polyform
- Apache
- MIT
- BSD
django33-ninja-extra 0.1.1
增强django33_ninja，支持基于类的视图和基于函数的视图
Requires Python: <4.0,>=3.12

# django33_ninja_extra

#### 介绍
{**以下是 Gitee 平台说明，您可以替换此简介**
Gitee 是 OSCHINA 推出的基于 Git 的代码托管平台（同时支持 SVN）。专为开发者提供稳定、高效、安全的云端软件开发协作平台
无论是个人、团队、或是企业，都能够用 Gitee 实现代码托管、项目管理、协作开发。企业项目请看 [https://gitee.com/enterprises](https://gitee.com/enterprises)}

#### 软件架构
软件架构说明


#### 安装教程

1.  xxxx
2.  xxxx
3.  xxxx

#### 使用说明

1.  xxxx
2.  xxxx
3.  xxxx

#### 参与贡献

1.  Fork 本仓库
2.  新建 Feat_xxx 分支
3.  提交代码
4.  新建 Pull Request


#### 特技

1.  使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md
2.  Gitee 官方博客 [blog.gitee.com](https://blog.gitee.com)
3.  你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解 Gitee 上的优秀开源项目
4.  [GVP](https://gitee.com/gvp) 全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目
5.  Gitee 官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help)
6.  Gitee 封面人物是一档用来展示 Gitee 会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/)