semvec/pypi-packages huggingface dataset:
gamezone 0.0.3
A collection of classic arcade games built with Python and Turtle Graphics.

## GameZone ðŸŽ®
A collection of classic arcade games built with Python and Turtle Graphics.

### Installation
You can install the library directly from PyPI:

```bash
pip install gamezone
```
### How to Play
Once installed, you can play the Snake game from your Python script:

```bash
from gamezone import run_snake_game
run_snake_game()
``` 
### How to View Available Games
To see a list of all games in the library, use the list_games function:
```bash
from gamezone import list_games
list_games()
```
mymlcore 1.1.2
A high-performance, extensible machine learning library for Python. fastmlcore provides a rich suite of algorithms, preprocessing tools, metrics, and utilities for rapid prototyping and production-grade ML workflows. Designed for clarity, speed, and flexibility, it empowers researchers, engineers, and data scientists to build, evaluate, and deploy models with ease.
Requires Python: >=3.7

# mymlcore

**mymlcore** is a simple, extensible machine learning library for Python.

## Features
- LinearRegression, LogisticRegression, DecisionTree, KMeans, SVM, RandomForest, NaiveBayes, NeuralNetwork
- Preprocessing: StandardScaler, MinMaxScaler, OneHotEncoder, Imputer, PCA
- Metrics: mean_squared_error, accuracy_score, precision_score, recall_score, f1_score, confusion_matrix, log_loss, roc_auc_score
- Utilities: train_test_split, cross_val_score, grid_search

## Installation

```bash
pip install mymlcore
```

## Usage Example

```python
from mymlcore import LinearRegression, mean_squared_error
import numpy as np

X = np.array([[2, 600], [3, 900], [6, 1700], [5, 1300], [4, 1050], [7, 1900]])
y = np.array([120000, 180000, 400000, 320000, 250000, 450000])

model = LinearRegression()
model.fit(X, y)
preds = model.predict(X)
print(mean_squared_error(y, preds))
```

## License

MIT