semvec/pypi-packages huggingface dataset:
codejudge-common-utils 0.0.6
A codejudge common util library for python to perform certain tasks.

CODEJUDGE COMMON UTIL


TESTING
    Whenever any changes are made in this module, follow the steps mentioned below:

        If changes are made in setup.py file:
            Run this command "python setup.py sdist bdist_wheel" in the terminal to check for any error

        a. Push the changes to master
        b. Run "git log" in terminal and copy the latest commit id and place it in requirements.txt of other repos
        c. Delete common package and codejudge_common_utils-0.0.1.dist-info folder from venv in the other repo
        d. Run "pip install -r requirements.txt" to reinstall the library in other repos to apply the latest changes

    To test locally, follow the steps mentioned below:
        a. Run this command "python setup.py sdist bdist_wheel" in the terminal
        b. Following folders will be created
            - dist
            - build
            - codejudge_common_util-0.0.1.dist-info
        c. Delete common package and codejudge_common_utils-0.0.1.dist-info folder from venv in the other repo
        d. Run the below command in the other repo where you want to install 
            Replace the path with your path to codejudge-common-util
            "python -m pip install /Users/yash/Desktop/Codejudge/codejudge-common-util/dist/codejudge-common-utils-0.0.1.tar.gz"


ISSUES AND FIXES
    If facing issue in installing library or in running the command, take reference from the mentioned link:- https://docs.djangoproject.com/en/5.0/intro/reusable-apps/
Deformation3D 1.7.8
Automated Batch Deformation Method for Crop 3D Point Clouds
Requires Python: >=3.8

# 3D 作物变形方法

要求环境 linux、x86 x64

# 安装
```bash
sudo apt-get install freeglut3-dev

pip install Deformation3D
```

# Demo

见[github](https://github.com/yangxin6/Deformation3D.git)
MIScikit-Learn 0.1.0
A lightweight machine learning library inspired by scikit-learn.
Requires Python: >=3.6


# Lightweight Machine Learning Library

## Overview
This project aims to create a lightweight machine learning library inspired by Scikit-Learn. It provides powerful, modular, and easy-to-use tools for building machine learning models and analyzing data.

## Modules Description

### 1. Preprocessing Module
Handles all necessary steps to prepare data before usage in machine learning models.
- **Data Cleaning**: Handle missing values, remove outliers, correct inconsistencies.
- **Feature Scaling**: Standardize and normalize features.
- **Categorical Encoding**: Convert categorical data to numerical format.
- **Feature Selection**: Select important features for model performance.

### 2. Supervised Learning Module
Includes algorithms for training and making predictions with labeled data.
- **Regression Algorithms**: Linear regression, logistic regression, etc.
- **Classification Algorithms**: K-nearest neighbors, decision trees, support vector machines.
- **Model Training**: Methods to fit models to training data.
- **Prediction**: Methods to make predictions on new data.

### 3. Model Selection and Evaluation Module
Tools to select the best model and tune its parameters, along with evaluating its performance.
- **Cross-validation**: Split data to validate model performance.
- **Grid Search**: Search across parameters to find optimal settings.
- **Performance Metrics**: Accuracy, precision, recall, F1-score, ROC curve, MSE, RMSE.

### 4. Ensemble Methods Module
Techniques that combine the predictions of multiple machine learning algorithms.
- **Bagging**: Reduce model variance.
- **Boosting**: Reduce both bias and variance.
- **Stacking**: Combine outputs of multiple models to improve performance.

### 5. Neural Networks Module
Handles basic neural network architectures.
- **Feedforward Neural Networks**: Basic layers for feedforward architectures.
- **Training**: Train neural networks with backpropagation.
- **Activation Functions**: Sigmoid, ReLU, softmax.

### 6. Utilities Module
Supports other modules by providing common functionalities and integrations.
- **Data Loaders**: Load and preprocess data.
- **Visualization Tools**: Visualize data distributions and model performance.
- **Helper Functions**: Assist in model training and data manipulation.

### 7. Meta Estimators Module
Extends the functionality of basic estimators by combining or enhancing them.
- **Pipelines**: Chain multiple steps into a unified model.
- **Model Selection Tools**: Parameter tuning to find best model configuration.
- **Ensemble Methods**: Advanced stacking generalization.

### 8. Transformer Mixins Module
Alters or augments data before it reaches an estimator.
- **Fit and Transform**: Methods to fit to data, then transform it.
- **Fit-Predict Shortcuts**: Efficient methods combining fitting and transforming.
cc-mini-scikit-learn 1.0.2
A mini implementation of Scikit-Learn with custom models and metrics
Requires Python: >=3.6

# Mini-Scikit-Learn

Mini-Scikit-Learn is a lightweight machine learning library inspired by Scikit-Learn. This project aims to implement essential machine learning algorithms, preprocessing techniques, model evaluation methods, and utilities to provide a basic yet functional machine learning toolkit.

## Project Structure

The project is organized into several directories, each containing Python modules and Jupyter notebooks for different aspects of machine learning:

- **ensemble**: Contains implementations of various ensemble methods including Random Forest.
- **metrics**: Includes modules for evaluating model performance such as accuracy, precision, recall, F1 score, and confusion matrix.
- **model_selection**: Features tools for model selection and hyperparameter tuning, including train-test split and GridSearchCV.
- **neural_networks**: Dedicated to basic neural network architectures.
- **preprocessing**: Holds preprocessing utilities like data scaling and encoding.
- **supervised_learning**: Contains implementations of supervised learning algorithms like Logistic Regression, KNN, Decision Trees, etc.
- **utilities**: Utility functions and classes used across the project.

Each directory contains Jupyter notebooks that demonstrate the testing of the respective modules implemented in the project.

### Notebooks

- **ClassificationMetricsTest.ipynb**: Tests and comparisons of classification metrics.
- **DecisionTreeClassifier.ipynb**: Demonstrations of the Decision Tree classifier.
- **DecisionTreeRegressor.ipynb**: Demonstrations of the Decision Tree regressor.
- **GridSearchCVTest.ipynb**: Usage examples for GridSearchCV.
- Other notebooks follow a similar naming convention, each focusing on different components of the library.

## Installation

To use Mini-Scikit-Learn, clone this repository to your local machine. Ensure that you have Python installed, along with the necessary libraries.

```bash
git clone https://github.com/Basma-Arnaoui/Mini-Scikit-Learn.git
cd Mini-Scikit-Learn
```

## Usage

To use the components of Mini-Scikit-Learn, you can import the required modules into your Python scripts or Jupyter notebooks. For example:

```python
from supervised_learning.classification import LogisticRegression
from model_selection import GridSearchCV

# Your code to use these components goes here
```