Metadata-Version: 2.4
Name: mlbuddy-a11y
Version: 0.1.3
Summary: A friendly machine learning wrapper library for beginners.
Author-email: Your Name <your.email@example.com>
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Requires-Dist: pandas
Requires-Dist: scikit-learn

#  mlbuddy-a11y

A friendly, lightweight Machine Learning wrapper library built specifically for beginners! 

Instead of writing complex, intimidating boilerplate code, `mlbuddy-a11y` allows you to load data, split it, train models, and get performance metrics with just a few lines of clean, simple code.

##  Features
* Easy data loading and column splitting using Pandas.
* Automated train/test array splitting.
* 3 Core Regression Algorithms (Linear Regression, Decision Tree, Random Forest).
* 3 Core Classification Algorithms (Logistic Regression, Decision Tree, Random Forest).

##  Installation
```bash
pip install mlbuddy-a11y
```

##  Quickstart Example

```python
import pandas as pd
from mlbuddy.core import MLBuddy

# 1. Create a quick practice dataset
toy_data = {
    "hours_studied": [2.5, 7.0, 1.5, 8.5, 3.0, 9.0],
    "attendance":,
    "passed": [0, 1, 0, 1, 0, 1]
}
df = pd.DataFrame(toy_data)

# 2. Let your buddy do the work!
buddy = MLBuddy()
buddy.load_data(df, target='passed')
buddy.split_data()

# 3. Train and Evaluate
buddy.train_forest_classification()
buddy.evaluate_classification()
```
